In-Situ Measurement and Numerical Simulation of Linear

Home , Friction welding, Residual stress, Thermocouple

IN-SITU MEASUREMENT AND NUMERICAL SIMULATION OF LINEAR

FRICTION WELDING OF Ti-6Al-4V

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Kaiwen Zhang

Graduate Program in Welding Engineering

The Ohio State University

2020

Dissertation Committee

Dr. Wei Zhang, advisor

Dr. David H. Phillips

Dr. Avraham Benatar

Dr. Vadim Utkin

Copyrighted by

Kaiwen Zhang

2020

Abstract

Traditional fusion welding of advanced structural alloys typically involves several concerns associated with melting and solidification. For example, defects from molten metal solidification may act as crack initiation sites. Segregation of alloying elements during solidification may change weld metal’s local chemistry, making it prone to corrosion. Moreover, the high heat input required to generate the molten weld pool can introduce distortion on cooling. Linear friction welding (LFW) is a solid-state joining process which can produce high-integrity welds between either similar or dissimilar materials, while eliminating solidification defects and reducing distortion.

Currently the linear friction welding process is most widely used in the aerospace industry for the fabrication of integrated compressor blades to disks (BLISKs) made of titanium alloys. In addition, there is an interest in applying LFW to manufacture low-cost titanium alloy hardware in other applications. In particular, LFW has been shown capable of producing net-shape titanium pre-forms, which could lead to significant cost reduction in machining and raw material usage.

Applications of LFW beyond manufacturing of BLISKs are still limited as developing and quantifying robust processing parameters for high-quality joints can be costly and time consuming. LFW is a highly dynamic process involving rapid heating and deformation at high strain rate (of the order of 10 s-1). Currently, there is a limited

ii fundamental understanding of the temperature and deformation transients during LFW which are critical parameters to study the joint quality. This dissertation research aimed to

(1) develop and valid a physics-based process model and (2) apply it to study the effect of processing parameters and work-piece geometry on temperature and deformation transients during LFW. Given the broad scope, the research was performed in collaboration with Boeing Research & Technology (Berkeley, MO), EWI (Columbus, OH), and OSU’s

CEMAS (Columbus, OH). The key innovations generated from this dissertation include:

 Optimized sample geometries for high-temperature, high-strain-rate mechanical

tests based on thermal-electrical-mechanical numerical simulations

 Measured temperature and deformation transients in-situ during LFW

 Developed a computationally efficient 2.5-D model considering heat conduction,

stress, and severe plastic deformation that was validated for different heat inputs

and work-piece geometries

Highlights of the research approaches and outcomes are described in the following.

Numerical models based on thermal-electrical-mechanical simulation in ABAQUS®, a commercially-available finite element code, were developed to correct true stress-strain data for high-temperature compression test, and to design a new specimen geometry for hot torsion test. The modeling results increased the accuracy of the Ti-6Al-4V flow stress data tested at different temperatures and strain rates in Gleeble® at Boeing Research &

Technology.

Initial welding of sub-scaled Ti-6Al-4V coupons with one square inch weld area was performed on a 20-ton mechanically driven LFW machine in EWI. Three heat inputs

iii designated as low, medium and high energy were tested. A high-speed camera was utilized to monitor the entire welding process for each weld. Type-K thermocouples were instrumented through drilled holes at four different offset distances from the weld interface for temperature measurement.

Full-scaled Ti-6Al-4V welding coupons and net-shape pre-forms with weld area of five and nine square inches respectively were performed on an MTI LF 35-75 hydraulic

LFW welder at Lightweight Innovations for Tomorrow (LIFT). Same welding parameters described previously were employed to produce full-scaled LFW joints. Black and white speckle patterns were painted onto the coupon side surfaces to map the surface deformation in-situ by digital image correlation (DIC) via a high-speed camera. An infrared camera was also utilized to measure the surface thermal profiles near the interface during heating and cooling of the LFW process.

Machine recorded experimental data such as oscillator positions, applied forging force and resultant shear force were used to calculate the effective interface friction coefficient as a function of processing time via Coulomb’s friction law. This effective friction coefficient was then inputted into the LFW process model to describe the interface heat generation based on frictional-sliding behavior.

The computational efficient 2.5-D models considered a transverse cross section that was perpendicular to the oscillation direction. The simulation procedure consisted of two stages: the conditioning stage and the merged stage. Modeling of the conditioning stage involved frictional heating while modeling of the subsequent merged stage involved only plastic deformation heating. The models were used to predict the temperature, stress and

iv deformation for the three different heat inputs as well as the different coupon geometries.

The modeling results were consistent with the experimental data including thermocouple and infrared camera measured thermal profiles, machine recorded axial shortening (or upset), surface strains measured by DIC, and the final flash shapes.

The results presented in this dissertation, taken as a whole, represented a significant step toward establishing a predictive simulation of LFW process. Such a predictive simulation tool is essential to ensure sound joints and parts in current and future generations of energy-efficient structures.

Dedication

Dedicated to my parents and Heting Li

Their love and support make me strive to be a better person

Acknowledgments

As an undergraduate laboratory assistant and later a graduate research associate in the last six years at The Ohio State University Welding Engineering Program, I would like to acknowledge a lot of people who helped me to make this study possible. First of all, I want to express deep appreciation to my academic advisor Dr. Wei Zhang for his selfless guidance over years. His support, patience, professional expertise, encouragement, kindly reminders, and demand for excellence always inspired and motivated me for striding forward. He also gave me many opportunities to advance my research as well as my professional development, and these experiences have all became invaluable life lessons and unforgettable memories so far in my life. I feel truly blessed, fortunate, and honored to have had the chance to work with him, and this research would not have been possible without his devotion. I would also like to thank Dr. Boian Alexandrov and Dr. John Lippold for bringing me into the Welding and Metallurgy Group when I was an undergraduate.

Without their teaching and guidance, I could never make myself this far. Gratitude must also be extended to Dr. David Phillips and Dr. Avraham Benatar for serving in my dissertation committee and providing many guidance as well as teaching welding-related courses in the past a few years.

I would also like to acknowledge the project team that made this research possible.

First, many thanks to Mr. Austin Mann from Boeing Research & Technology for

vii collaborating and leading the project (TMP R4-6). He provided materials and process parameters into this project and also involved in Gleeble® testing and analyses, machining of materials and welds, and post-weld heat treatment with his colleague Mr. Sean Thuston.

Second, I would like to acknowledge Dr. Samuel Kuhr and Prof. Hamish Fraser at OSU for doing the material characterizations at CEMAS and providing adequate guidance and comments. Many thanks to Dr. Mike Eff at EWI and Mr. Zachary Danko at MTI for making linear friction welds and generating welding-related experimental data. Last but not least,

I wish to acknowledge the support and guidance from the LIFT program management office, in particular Bhavi Shah (TMP R4-6 project manager) for the overall project management and arrangement of welding in LIFT at Detroit.

I would also like to extend thanks to many former and present faculty, staff, graduate and undergraduate students in the Welding Engineering Program for their support and help over years and to this study. Especially Dr. Xun Liu for providing in-situ monitoring tools, Dr. Carolin Fink, Dr. Ying Lu, Dr. Daniel Tung, Dr. Tyler Borchers, Dr.

Yousub Lee, Dr. Hyeyun Song, Dr. Adam Hope, Dr. Jim Rule, Dr. Collin Whitt, Dr.

Xuesong Gao, Mr. Alexey Kuprienko, Mr. Ed Pfeifer, Mr. Marc Ruggeri (DSI), Mr. Tyler

High, Mr. Grant Pfeifer, and Mr. Menachem Kimchi for their generous help and shared wisdom both mentally and technically on academic and in life.

Finally, my family and my girlfriend Heting Li deserve recognition for their support, love, faith, patience, and sacrifice while I worked on this research and dissertation.

I love you all.

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Vita

May 31, 1992………………………………………….... Born-Xi’an, China

2010-2014……………………………………………...... B.S. Welding Engineering,

The Ohio State University

2014-2016………………………………………………. M.S. Welding Engineering,

The Ohio State University

2016-present……………………………………………. Graduate Research Associate,

Welding Engineering

The Ohio State University

Fields of Study

Major Field: Welding Engineering

Table of Contents

Abstract ...... ii Dedication ...... vi Acknowledgments ...... vii Vita ...... ix Table of Contents ...... x List of Tables...... xiii List of Figures ...... xiv Chapter 1 Introduction ...... 1 1.1 General Background ...... 1 1.2 Industrial Usage ...... 3 1.3 Motivations for Modeling ...... 5 1.4 Research Objectives and Methodology ...... 6 1.5 Dissertation Structure ...... 9 Chapter 2 Literature Review ...... 11 2.1 Introduction to Friction Welding ...... 11 2.2 Linear Friction Welding Basics ...... 13 2.3 Materials weldable by Linear Friction Welding ...... 19 2.3.1 Introduction to titanium alloys and Ti-6Al-4V ...... 21 2.3.2 Microstructure of linear friction welded Ti-6Al-4V ...... 23 2.3.3 Mechanical properties of linear friction welded Ti-6Al-4V ...... 26 2.3.4 Flash morphology of linear friction welded Ti-6Al-4V ...... 29 2.3.5 Residual stress of linear friction welded Ti-6Al-4V ...... 32 2.4 Current Linear Friction Welding Modeling Methods and Results ...... 34 2.4.1 Analytical models for LFW process of Ti-6Al-4V ...... 34 2.4.2 Numerical modeling approaches for LFW of Ti-6Al-4V ...... 39

2.4.3 Numerical models and modeling results for LFW of Ti-6Al-4V ...... 61 2.4.4 Model validation ...... 78 2.5 In-situ Measurement Approaches for Linear Friction Welding ...... 80 2.6 Mechanical Testing of Ti-6Al-4V ...... 92 2.7 Summary of Important Technical Gaps ...... 100 Chapter 3 Mechanical Testing and Modeling of Ti-6Al-4V ...... 105 3.1 Material ...... 105 3.2 Thermo-Mechanical Simulation ...... 106 3.3 Hot Compression Modeling Setup ...... 110 3.4 Hot Torsion Modeling Setup ...... 112 3.5 Results and Discussion ...... 115 3.5.1 Thermo-mechanical testing ...... 115 3.5.2 Modeling results of hot compression test ...... 119 3.5.3 Modeling results of hot torsion test ...... 122 3.6 Summary ...... 125 Chapter 4 Linear Friction Welding and Modeling of Sub-Scaled Ti-6Al-4V Coupons . 126 4.1 Design of Experiments ...... 127 4.2 Experimental Procedures...... 128 4.2.1 Welding processes ...... 128 4.2.2 In-situ measurements ...... 130 4.3 Modeling Approaches ...... 134 4.3.1 Model setup ...... 135 4.3.2 Calculation of friction coefficient ...... 141 4.3.3 Determination of transition time ...... 145 4.4 Experimental Results and Discussion ...... 147 4.5 Modeling Results and Validations ...... 158 4.6 Summary ...... 176 Chapter 5 Linear Friction Welding and Modeling of Full-Scaled Ti-6Al-4V Net-shape Pre-forms...... 179 5.1 Experimental Procedures...... 180 5.1.1 Design of Ti-6Al-4V pre-forms ...... 180 5.1.2 Welding processes ...... 180 5.1.3 In-situ measurements ...... 183 xi

5.2 Modeling Approaches ...... 185 5.3 Experimental Results and Discussion ...... 188 5.4 Modeling Results and Validations ...... 209 5.5 Summary ...... 238 Chapter 6 Concluding Remarks ...... 240 6.1 Summary of Present Research ...... 240 6.2 Suggested Future Works ...... 245 References ...... 248 Appendix A. Data Mapping in DEFORM® ...... 253

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List of Tables

Table 2.1: Statistical results for important inputs [26] ...... 50

Table 2.2: Flow stresses of Ti-6Al-4V as a function of temperature, strain and strain rate by hot compression test [53] ...... 59

Table 3.1: Chemical composition of Ti-6Al-4V base metal (wt%) ...... 106

Table 3.2: Gleeble® hot compression and torsion test matrix ...... 107

Table 4.1: Relation between welding parameters and energy ratings ...... 130

Table 4.2: Summary of the applied energy ratings for each weld ...... 130

Table 5.1: Overview of welding parameters for full-scaled coupon welding trials ...... 182

Table 5.2: Tensile testing results of low energy input welded pre-form ...... 193

Table 5.3: Matrix of LFW process parameters and heat generation ...... 231

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List of Figures

Figure 1.1: Illustration of the parts movement in LFW process [7] ...... 2

Figure 1.2: A complete LFW joint showing expelled interface material [2] ...... 3

Figure 1.3: Illustrations of conventional bladed disk (left) and LFW produced BLISK

(right) [7] ...... 4

Figure 1.4: Illustration of a linear friction welded non-planar pre-form and the corresponding machined component from the pre-form [10] ...... 5

Figure 1.5: Graphical representation of research objectives and approach ...... 7

Figure 1.6: Various cases investigated in the present thesis research ...... 8

Figure 2.1: Comparison of different moving directions (yellow) and magnitudes of heat generation (black) for three variants of friction welding methods [22] ...... 12

Figure 2.2: Schematics of the LFW process phases [14]...... 14

Figure 2.3: Stages of LFW process: (a) asperity interaction, (b) viscous layer formation, and (c) steady state condition showing the expelled interface material [2] ...... 16

Figure 2.4: Changes of some LFW process variables at different LFW process phases [9]

...... 16

Figure 2.5: Model predicted relationship between average heat input and axial shortening for (a) Ti-6Al-4V and (b) mild steel [19][28] ...... 18

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Figure 2.6: Effect of welding pressure on (a) burn-off rate and (b) friction time of stainless steel 316L [29] ...... 19

Figure 2.7: Effect of oscillation direction on temperature and flash shape for (a) oscillation along x-axis and (b) oscillation along z-axis of mild steel [9] ...... 19

Figure 2.8: Experimentally studied materials in published research of LFW process since

1992 [9] ...... 21

Figure 2.9: Effects of α and β stabilizing elements on the α and β-transus temperatures

[33]...... 22

Figure 2.10: Pseudo-binary phase diagram for Ti-6Al-xV [34] ...... 22

Figure 2.11: Macroscopic section of a Ti-6Al-4V linear friction weld [2] ...... 24

Figure 2.12: WCZ microstructure: (a) Widmanstätten and (b) martensite [2] ...... 25

Figure 2.13: TMAZ microstructure of (a) deformed and reoriented grains and (b) broken and elongated α grain structure [35] ...... 26

Figure 2.14: Micro-hardness across weldment showing higher WCZ hardness and lower

TMAZ hardness compared to BM hardness [35] ...... 27

Figure 2.15: Relationship between mechanical properties and LFW process parameters

[35]...... 28

Figure 2.16: Low and high cycle fatigue performance for WCZ and BM of linear friction welded Ti-6Al-4V [42] ...... 29

Figure 2.17: Different flash morphologies: (a) ripple morphology and (b) smooth morphology [2] ...... 29

Figure 2.18: FEA results of flash formation mechanisms: (a) ripple, (b) smooth, (c) boundary temperature, and (d) region of high strain rate [2] ...... 31

Figure 2.19: Distributions of residual stress components along different axis measured by synchrotron X-ray diffraction [40][2] ...... 33

Figure 2.20: Thermal profile comparison between analytical model and thermocouple readings at 1.6 mm distance away from the interface during initial phase [24] ...... 36

Figure 2.21: Comparisons of predicted thermal profiles generated by different process parameters between analytical and numerical models at the end of the equilibrium phase.

(a) fixed frequency (45 Hz) and pressure (110MPa), varying amplitudes; (b) fixed amplitude (2 mm) and pressure (110MPa), varying frequencies; (c) fixed frequency (45

HZ) and amplitude (2mm), varying loads; (d)-(f) the corresponding predictions from analytical models[12] ...... 37

Figure 2.22: Comparison of measured and predicted HAZ width with increasing energy input rates [43] ...... 38

Figure 2.23: Example of 3-D LFW process model developed in DEFORM [15] ...... 41

Figure 2.24: 2-D LFW modeling approaches: (a) one work-piece and one rigid part, (b) two deformable work-pieces and (c) single part representing two joined work-pieces.

Oscillation direction: left and right [2] ...... 42

Figure 2.25: (a) schematic illustration of conditioning to equilibrium stages of model, and process parameters and boundary conditions needed; (b) determination of the transition between conditioning and equilibrium stages [11]...... 44

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Figure 2.26: Assumed interface friction coefficient (b) [19] and (c) [17] based on shear pin testing results (a) by Vairis et al. [24] ...... 46

Figure 2.27: Schematic diagram showing the LFW machine [26] ...... 46

Figure 2.28: Calculated effective friction coefficient as a function of time based on machine recorded experimental data [11] ...... 48

Figure 2.29: Calculated effective friction coefficient with different applied process parameters for different phases of LFW process based on regression analysis [26] ...... 48

Figure 2.30: 2-D thermal model showing: (a) the heat flux approach and (b) generated thermal profile at the end of the initial phase [18] ...... 51

Figure 2.31: Temperature dependent effective friction coefficient for different energy input rates. An approximation was fitted in black line [11] ...... 52

Figure 2.32: Calculated effective friction coefficients for different process parameters

[11]...... 53

Figure 2.33: Comparison of predicted and measured thermal profiles at the end of conditioning stage for high (left), medium (center), and low (right) energy input rates

[43]...... 53

Figure 2.34: Interruption test of LFW process showing localized adhesion between work- pieces at the end cycles of the conditioning stage based on constant burn-off rate [11]... 54

Figure 2.35: Comparison of experimentally determined time for plastic deformation to be initiated (at completion of the transition phase) and computed estimation of critical weld interface temperature obtain by Equation 2.15 [11] ...... 56

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Figure 2.36: Thermocouple measured thermal cycles for high, medium, and low energy input rates at initially 1 mm away from the weld line [11] ...... 56

Figure 2.37: Typical meshing setup for 2-D LFW model [2] ...... 57

Figure 2.38: Predicted stress-strain curves for Ti-6Al-4V by JMatPro at various temperature, strain, and stain rate levels [12] ...... 60

Figure 2.39: Numerical simulations of LFW process by (a) Abaqus [9], (b) Ansys [51],

Figure 2.40: Predicted 휎푥푥 stress field at (a) welding time = 0.044 s, (b) 0.22 s and (c)

0.44 s of welding, and (d) 1 s, (e) 5 s and (f) 210 s of cooling for a Ti-6Al-4V LFW weld

(left) [44] ...... 62

Figure 2.41: Predicted residual stress in LFW of Ti-6Al-4V weld: (a) Von Mises stress,

(b) stress in x-direction, (c) stress in y-direction (oscillation direction) , and (d) stress in z-direction [9] ...... 63

Figure 2.42: Point tracking at: (a) initial of the process, (b) during material flow and (c) complete expulsion [13]...... 64

Figure 2.43: (a) Microstructure of contaminants at the weld interface, (b) 0.5 mm burn- off (experiment) and (c) associated FEA, (d) 1 mm burn-off (experiment) and (e) associated FEA, and (f) 3 mm burn-off and (g) associated FEA. The average rubbing velocity is 540 mm/s and applied force is 100 kN for all cases [18] ...... 65

Figure 2.44: Interface contaminants removal of T-joint using point tracking: (a) initial location of points and (b) evolution of points [16] ...... 66

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Figure 2.45: Flash formation during LFW of Ti-6Al-4V with: (a) small amplitude and high applied force and (b) associated modeling result; (c) large amplitude and lower applied load and (d) associated modeling result [12] ...... 67

Figure 2.46: Flash comparisons between experimental and FEA results under different combinations of process parameters [11] ...... 69

Figure 2.47: Illustration of flash formation mechanisms by (a) forging mode and (b) shearing mode. In (a), flash remains in contact with work-pieces, while in (b), flash separates from the top work-piece for a period of time. Extent of external flash is shown within the dashed lines [11] ...... 69

Figure 2.48: Predicted peak strain rates along weld line from models using various amplitudes but constant frequency and forging pressure [12] ...... 71

Figure 2.49: FEA results on (a) peak interface temperature and (b) peak interface strain rate with applied force of 100 kN (blue curve) and 32 kN (black curve) [18] ...... 72

Figure 2.50: Thermal profile predictions of low (left) and high (right) energy input welds at the end of the initial phase of LFW process [43] ...... 73

Figure 2.51: Predicted temperature profiles at the end of the initial phase of LFW process for welds made with different average rubbing velocities and applied forces [26] ...... 74

Figure 2.52: Predicted temperature profiles at the end of the equilibrium phase of LFW process for welds with different average rubbing velocities and applied forces [18] ...... 75

Figure 2.53: Predicted temperature distribution at (a) welding time = 1 s, (b) 2 s, (c) 3 s, and (d) 4 s and (e) temperature profile for a monitored central element at the interface

[19]...... 76

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Figure 2.54: Temperature predictions for a T-joint of Ti-6Al-4V made by LFW process:

(a) temperature map, (b) spatial temperature profiles across the interface for different times throughout the equilibrium phase, and (c) magnitude of the temperature gradient in either side of the interface as a function of time [16] ...... 77

Figure 2.55: Predicted flash formation with temperature contours over a cycle of oscillation [16]...... 78

Figure 2.56: LFW model validations on (a) thermal profiles [18][54], (b) burn-off of work-pieces [13][43], and (c) flash shape [12][43] ...... 79

Figure 2.57: Additional LFW model validations on (a) size of HAZ and PAZ [11], (b) geometrical character (3-D) [15], and (c) residual stress [2][9]...... 80

Figure 2.58: LFW of Ti-6Al-4V with thermocouple wires attached by thermal cement

(left) and epoxy resin (right) [12] ...... 82

Figure 2.59: Comparisons for thermocouple measured thermal profiles and those predicted by FEA model. A melting temperature of 1660 °C was assumed to determine the homologous temperature [12][55] ...... 83

Figure 2.60: Work-piece dimensions and locations of thermocouples in a sectioned plane

[18]...... 85

Figure 2.61: Comparison of measured and predicted thermal histories for a LFW weld made by 20 Hz frequency, 1.5 mm amplitude, 100 kN applied force and 3 mm upset [18]

...... 85

Figure 2.62: Schematics of infrared measurement procedure [59] ...... 86

Figure 2.63: Thermal image captured by the infrared camera with plot of temperature as a function of pixels across the weld interface in the ROI (region of interest) [57] ...... 88

Figure 2.64: Thermal fields for LFW of GH4169 superalloy measured by an infrared camera at different welding times ((a) t = 0.2 s, (b) t = 0.4 s, (c) t = 0.9 s, (d) t = 1.2 s, (e) t = 1.75 s and (f) t = 3 s) [58] ...... 89

Figure 2.65: Video images illustrating formation of flash in both oscillation and transverse directions [11] ...... 90

Figure 2.66: Comparison of ripple formation in one cycle of LFW process by high-speed photography (left) and model predictions (right). Sheared material was indicated by arrows [11] ...... 90

Figure 2.67: Basic steps of the DIC algorithm implemented in NCorr using an initial guess and iterative optimization scheme to find a refined solution [60] ...... 92

Figure 2.68: Data from uniaxial tension test for (a) engineering stress-strain curve, (b) true stress-strain curve, (c) illustration of dimensional changes during the test, and (d) axial stress distribution of a necked portion [63] ...... 94

Figure 2.69: Compression test specimen. (a) schematics of specimen showing lubricated shallow grooves on ends and (b) shape of the specimen before and after the test [63] .... 96

Figure 2.70: Standard specimen geometry for torsion test [67] ...... 97

Figure 2.71: Pictures of (a) Gleeble® 3800 system setup and (b) torsion MCU [68] .... 100

Figure 3.1: Microstructure of mill annealed Ti-6Al-4V specimen via scanning electron microscopy (left) and zoomed-in view (right) (images courtesy of CEMAS) ...... 106

Figure 3.2: Geometry of specimen for Gleeble® hot compression test ...... 108

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Figure 3.3: Gleeble® hot compression test conditions (left); Zoomed-in view of compression step (right)...... 108

Figure 3.4: (a) Final geometry of Gleeble® hot torsion specimen designed based on FEA modeling, and (b) machined torsion specimen according to the design in (a)...... 110

Figure 3.5: Experimentally measured thermal profiles at the specimen center and edge during heating and soaking steps in Gleeble® ...... 111

Figure 3.6: Meshed compression model in ABAQUS® showing a quarter of the original specimen geometry ...... 112

Figure 3.7: Prior hollow tubular torsion specimen by Norton et al. and setup in Gleeble® torsion MCU at OSU [68] ...... 113

Figure 3.8: Baseline Gleeble® hot torsion models with short gage section (left) and long gage section (right) ...... 115

Figure 3.9: True stress-strain curves (flow stress) generated by Gleeble® compression testing of Ti-6Al-4V (testing data courtesy of Boeing Research & Technology) ...... 116

Figure 3.10: True stress-strain curves (flow stress) at 1050 °C generated by Gleeble® compression testing of Ti-6Al-4V compared to the results from Seshacharyulu et al. [53]

(red markers) ...... 117

Figure 3.11: True stress-strain curves (flow stress) generated by Gleeble® torsion testing of Ti-6Al-4V (testing data courtesy of Boeing Research & Technology)...... 118

Figure 3.12: True stress-strain curves (flow stress) generated by Gleeble® compression and torsion testing of Ti-6Al-4V at the same temperature and strain rate conditions ..... 119

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Figure 3.13: Temperature contours for non-isothermal model at the end of (a) heating step, and (b) compression step, and isothermal model at the end of (c) heating step, and

(d) compression step. Temperature plotted in C ...... 120

Figure 3.14: Predicted surface temperature profiles at the specimen mid-point and edge for (a) non-isothermal model, and (b) isothermal model ...... 121

Figure 3.15: Model output of force and displacement data (left) and converted true stress and true strain data (right) ...... 122

Figure 3.16: Predicted temperature distribution (prior to twisting) for (a) long gauge and thin wall, (b) long gauge and thick wall, (c) short gauge and thin wall, and (d) short gauge and thick wall specimens ...... 123

Figure 3.17: Predicted temperature and shear stress distributions for (a) and (c) adjusted geometry; for (b) and (d) baseline designs (i.e., the one shown in Figure 3.16(c)) ...... 124

Figure 3.18: Through-thickness distributions of temperature (left) and shear strain (right) for specimens with and without adjustments ...... 124

Figure 4.1: (a) Dimensions of sub-scaled Ti-6Al-4V coupon for LFW, and (b) five machined Ti-6Al-4V blanks...... 128

Figure 4.2: Linear friction welder at EWI ...... 129

Figure 4.3: Schematics of hole drilling layout and photograph of holes on one coupon 132

Figure 4.4: Type-K thermocouples used for temperature measurement ...... 132

Figure 4.5: Photographs of the weld blanks embedded with thermocouples ...... 133

Figure 4.6: High-speed camera and zoom-in lens used for in-situ monitoring of LFW . 134

Figure 4.7: Setup of the high-speed camera ...... 134

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Figure 4.8: Schematics of (a) 3-D view of LFW process showing directions parallel

(blue) and perpendicular (red) to the oscillation direction, and (b) 2-D view of LFW process/modeling showing computational domain parallel (blue) and perpendicular (red) to the oscillation direction ...... 136

Figure 4.9: Illustration of 2.5-D transverse model setup for simulation the conditioning stage ...... 138

Figure 4.10: Illustration of 2.5-D transverse model setup for simulating the merged stage

...... 139

Figure 4.11: Flow stress data of Ti-6Al-4V used in the present study [12][53] ...... 141

Figure 4.12: Example of ACPI LFW machine recorded data for high energy input weld

...... 143

Figure 4.13: Calculated absolute instantaneous effective friction coefficient as a function of welding time for the high heat input weld ...... 145

Figure 4.14: Smoothed effective friction coefficient for high heat input weld...... 145

Figure 4.15: High-speed imaging of medium energy input LFW process (plates were painted with black and white speckle patterns) ...... 147

Figure 4.16: Photographs of the welds (a) R4-1 through R4-5 with a close-up view of the flash on weld R4-4, and (b) R4-7 through R4-11 with instrumented thermocouples. Red arrows represent the welding oscillation direction ...... 148

Figure 4.17: Thermal profiles measured by embedded thermocouples for three weld process energy ratings and in five unique welds (two low, two medium, and one high) 150

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Figure 4.18: SEM images displaying (a) thermocouple location relative to the weld interface for R4-7 weld (medium energy weld), and (b) microstructure approaching to the central weld zone for R4-8 weld (medium energy weld); the bright line in the center shows the remnants of a pair of Type-K thermocouples (images courtesy of CEMAS) 151

Figure 4.19: High-speed images for high energy LFW process (0 – 1.24 s) (R4-12); red arrow shows the welding oscillation direction ...... 153

Figure 4.20: High-speed images for medium energy LFW process (0 - 2.02 s) (R4-7); red arrow shows the welding oscillation direction ...... 153

Figure 4.21: High-speed images for low energy LFW process (0 - 4.15 s) (R4-11); red arrow shows the welding oscillation direction ...... 154

Figure 4.22: Machine recorded upset as a function of welding time for the three energy inputs. Dashed lines mark the transition times determined from the high-speed videos.154

Figure 4.23: Calculated instantaneous effective friction coefficient for (a) low energy weld (R4-11), (b) medium energy weld (R4-7), and (c) high energy weld (R4-12) ...... 156

Figure 4.24: Comparison of smoothed effective friction coefficient for three energy input welds ...... 157

Figure 4.25: Comparison of smoothed effective friction coefficient for three energy input welds only for the duration of conditioning stage (each duration is based upon previously determined transition time) ...... 157

Figure 4.26: Modeling predictions of temperature and deformation at the end of conditioning stage for: low (left), medium (middle), and high (right) energy rating processes. Oscillation direction was in and out of the plane ...... 159

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Figure 4.27: Modeling predictions of temperature and deformation for the merged stage for: low (top row), medium (middle row), and high (bottom row) energy rating processes.

Oscillation direction was in and out of the plane ...... 161

Figure 4.28: Comparisons of predicted and observed flash shapes (on the transverse section) for: low (top), medium (middle), and high (bottom) energy rating processes ... 162

Figure 4.29: Predicted thermal profiles for three energy rating LFW processes along the center of the interface (at end of merged stage) ...... 164

Figure 4.30: Preparation of welded samples (transverse sections) for analysis in optical microscope ...... 164

Figure 4.31: Macrographs of the LFW welds (transverse sections); red dashed circles represent the PAZ in each sample ...... 166

Figure 4.32: Predicted temperature and material flow evolution during medium energy rating LFW process. Oscillation direction was in and out of the plane...... 166

Figure 4.33: Comparison of PAZ and flash thickness for low energy rating weld and modeling results at the end of merged stage ...... 168

Figure 4.34: Comparison of PAZ and flash thickness for medium energy rating weld and modeling results at the end of merged stage ...... 169

Figure 4.35: Comparison of PAZ and flash thickness for high energy rating weld and modeling results at the end of merged stage ...... 170

Figure 4.36: Effects of rubbing velocity and pressure on the minimum thickness of plastically-affected zone ...... 171

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Figure 4.37: Comparisons of predicted and measured temperature profiles at a monitored location located initially 0.84 mm way from the interface for: low (top), medium

(middle), and high (bottom) energy rating processes ...... 174

Figure 4.38: Comparisons of predicted and measured plate upsets for: low (top), medium

(middle), and high (bottom) energy rating processes ...... 175

Figure 4.39: Comparison of predicted and measured average plate upset rate for three energy rating LFW processes ...... 176

Figure 5.1: Net-shape pre-form design of a demonstration piece for Boeing 737 fitting

(courtesy of Boeing Research & Technology) ...... 180

Figure 5.2: MTI LF35-75 LFW machine at LIFT facility in Detroit, MI ...... 181

Figure 5.3: Standard geometry of MTI testing coupon for LF35-75 LFW machine ...... 182

Figure 5.4: Images of the high-speed camera (left) and infrared camera (right) ...... 184

Figure 5.5: Camera setup for in-situ measurement at LIFT...... 184

Figure 5.6: Modeling setup for MTI standard coupon plates (2.5-D transverse setup) .. 186

Figure 5.7: Modeling setup for T-shape pre-forms (2.5-D transverse setup) ...... 187

Figure 5.8: First group of six LFW welds from welding trials on LIFT LF35-75 (MTI coupon design with five square inches weld area); red arrow shows the welding oscillation direction ...... 189

Figure 5.9: Flash shapes for MTI coupon welds made at (a) low energy input, (b) medium energy input, and (c) high energy input ...... 190

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Figure 5.10: Full-scaled Ti-6Al-4V pre-forms (nine square inches of weld area) welded on LIFT LF35-75 with (a) low energy input and (b) medium energy input; red arrow shows the welding oscillation direction ...... 191

Figure 5.11: Comparison of LFW machine recorded material upset profiles for welds of different weld areas and welded at low and medium energy inputs ...... 192

Figure 5.12: Image of a post-weld heat treated (stress-relieved) pre-form ...... 193

Figure 5.13: Fifty percent machined pre-form welded at low energy input (courtesy of

Boeing Research & Technology) ...... 194

Figure 5.14: High-speed images for full-scaled coupon weld made at low energy input; red arrow shows the welding oscillation direction ...... 196

Figure 5.15: High-speed images for full-scaled coupon weld made at medium energy input; red arrow shows the welding oscillation direction ...... 197

Figure 5.16: High-speed images for full-scaled coupon weld made at high energy input; red arrow shows the welding oscillation direction ...... 198

Figure 5.17: High-speed images for T-shaped pre-form welded at low energy input; red arrow shows the welding oscillation direction ...... 199

Figure 5.18: High-speed images for T-shaped pre-form welded at medium energy input; red arrow shows the welding oscillation direction ...... 200

Figure 5.19: Machine recorded upset as a function of welding time at different energy inputs for full-scaled coupon welds (top) and T-shaped pre-forms (bottom). Dashed lines mark the transition times determined from the high-speed videos ...... 201

xxviii

Figure 5.20: Contour views of shear strain map on the stationary work-piece at different time frames based on digital image correlation (DIC) in Ncorr for full-scaled coupon welds made by the three energy ratings ...... 203

Figure 5.21: Quantitative DIC (shear) strain analysis based on points tracking at two different points along the welding interface ...... 205

Figure 5.22: Infrared camera measured temperature maps for full-scaled coupon weld made at low energy input ...... 206

Figure 5.23: Infrared camera measured temperature maps for full-scaled coupon weld made at medium energy input ...... 207

Figure 5.24: Infrared camera measured temperature maps for full-scaled coupon weld made at high energy input ...... 207

Figure 5.25: Infrared camera measured temperature maps for T-shaped pre-form made at low energy input ...... 208

Figure 5.26: Infrared camera measured temperature maps for T-shaped pre-form made at medium energy input ...... 208

Figure 5.27: Modeling predictions of temperature and deformation at the end of conditioning stage for MTI coupons at: low (left), medium (middle), and high (right) energy rating processes. Oscillation direction was in and out of the plane ...... 211

Figure 5.28: Modeling predictions of temperature and deformation at the end of conditioning stage for T-shaped pre-forms at: low (left) and medium (right) energy rating processes. Oscillation direction was in and out of the plane ...... 211

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Figure 5.29: Modeling predictions of temperature and deformation for the merged stage for full-scaled coupons at: low (left column), medium (middle column), and high (right column) energy rating processes. Oscillation direction was in and out of the plane ...... 213

Figure 5.30: Comparisons of predicted flash with actual welds for full-scaled coupons 214

Figure 5.31: Predicted temperature profiles at the center of the weld interface along the longitudinal direction (at end of merged stage). All welds plotted here were symmetrical

...... 214

Figure 5.32: Modeling predictions of temperature and deformation for the merged stage for T-shaped pre-forms at: low (top) and medium (bottom) energy rating processes.

Oscillation direction was in and out of the plane ...... 216

Figure 5.33: Comparisons of predicted flash with actual welds (transverse view) for T- shaped pre-forms ...... 217

Figure 5.34: Predicted temperature profiles for non-symmetrical (T-shaped) welds along the direction perpendicular to the weld interface (at end of merged stage) ...... 218

Figure 5.35: Comparisons of predicted PAZ for full-scaled coupons (top) and T-shaped pre-forms (bottom) at the end of merged stage ...... 220

Figure 5.36: Effect of rubbing velocity and pressure on the minimum thickness of plastically-affected zone for all modeling cases in this study ...... 221

Figure 5.37: Setup of 2-D longitudinal LFW process modeling; computational domain is parallel to oscillation direction on plane (along x direction) ...... 223

xxx

Figure 5.38: Comparisons of predicted and DIC calculated shear strain distributions at the center and outer edge of the interface for high energy rating LFW process of full-scaled

MTI coupon up to 40% completion of welding ...... 224

Figure 5.39: Comparisons of predicted and DIC calculated shear strain distributions at the center and outer edge of the interface for medium energy rating LFW process of full- scaled MTI coupon up to 18% completion of welding ...... 225

Figure 5.40: Comparisons of modeling predicted (cross-sections) and infrared measured temperatures for MTI coupons at: low (left column), medium (middle column), and high

(right column) energy rating processes; red arrows are oscillation directions and brown arrows are loading directions ...... 226

Figure 5.41: Comparisons of modeling predicted (cross-sections) and infrared measured temperatures for T-shaped pre-forms at: low (left) and medium (right) energy rating processes; red arrows are oscillation directions and brown arrows are loading directions

...... 226

Figure 5.42: Comparisons of predicted and measured plate upsets for full-scaled coupons

...... 228

Figure 5.43: Comparisons of predicted and measured plate upsets for T-shaped pre-forms

...... 229

Figure 5.44: Comparison of predicted and measured average plate upset rate for all LFW processes conducted in this study ...... 230

Figure 5.45: Effect of welding heat input on material upset and upset rate from (a) and (c) experimental measurements, and (b) and (d) modeling predictions ...... 232

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Figure 5.46: Effect of pressure and amplitude on thermal and deformation fields of medium energy processed full-scaled coupon; (a) normal pressure and amplitude, (b)

50% reduction of normal pressure, and (c) 50% reduction of normal amplitude ...... 234

Figure 5.47: Effect of pressure and amplitude on thermal and deformation fields of medium energy processed T-shaped pre-form; (a) normal pressure and amplitude, (b)

50% reduction of normal pressure, and (c) 50% reduction of normal amplitude ...... 235

Figure 5.48: Theoretical results showing effect of pressure and amplitude on material upset and upset rate for medium energy processed (a) full-scaled coupon welds and (b) T- shaped pre-forms ...... 235

Figure 5.49: Effect of mesh and minimal element size on thermal and deformation fields of high energy processed full-scaled coupon (from left to right: total number of element increases and minimal mesh size decreases)...... 237

Figure 5.50: Effect of mesh and minimal element size on material upset and upset rate of high energy processed full-scaled coupon ...... 238

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Chapter 1 Introduction

1.1 General Background

Linear friction welding (LFW) process was first patented in the late 1920s [1], however, the process description and application were not specified in details. The first recorded discussion of LFW concept appeared in early 1960s, but the process was defined to be questionable as a manufacturing technique due to the difficulties in generating linear reciprocation [2]. The first prototype of electro-mechanical LFW machine was built in The

Welding Institute (TWI), UK to demonstrate the potential capability of the technique for different combination of metals [2]. Back to early 1990s, the first recorded academic research via LFW process took place at The Ohio State University, USA [3][4] and The

University of Bristol, UK [5][6] on multiple aluminum and titanium alloys. When compared to other friction welding processes such as inertia (rotary) friction welding (IFW) and friction stir welding (FSW), LFW has relatively limited information available in open literatures until the recent decade [7].

Like all other friction welding processes, a defining feature of the LFW is its ability to join materials in the solid state without melting of the parts. Relative motion between the two work-pieces is translational and there is no use of external tools such as a pin tool in friction stir welding. In particular, in LFW, one work-piece is driven to oscillate according to a given amplitude at moderate frequencies. Simultaneously, the opposing 1 work-piece is held against the oscillating work-piece with a uniaxial applied force as shown in Figure 1.1 [2][7]. The friction between the moving surfaces generates significant amount of heat that softens the materials and causes the interface to plasticize. As a result, the plasticized material will be expelled away from the interface as flash causing the work- pieces to be shortened. During the shortening stage, contaminants such as oxides at the interface will be eliminated and nascent surfaces are created, resulting in pure metal to metal contact and consequently metallic bonds between two work-pieces [1][2]. Figure 1.2 shows an example of completed weld.

Figure 1.1: Illustration of the parts movement in LFW process [7]

Figure 1.2: A complete LFW joint showing expelled interface material [2]

1.2 Industrial Usage

To date, the only major commercial use of LFW is to fabricate titanium alloy integrated bladed disks (BLISKs) for the compressors in aero-engines [1][2][7][8], although other applications are under development [2][9][10]. Conventional bladed compressor disks are made in such a way that individual airfoils are anchored by nuts and bolts in a slotted central retainer [7]. On the other hand, a BLISK is produced as an integral body where blade root and locating slot are no longer needed, resulting in significant weight reduction and removal of stress concentrators as fatigue crack initiation sites

[2][7][8]. Both conventional and LFW production of blades to disks are shown in Figure

1.3. Since linear friction welded BLISKs have improved aerodynamic performance that reduces the operating costs for the end users, Rolls Royce, MTU Aero Engines, General

Electric, Pratt & Whitney and Boeing Company all use the LFW process to commercially produce titanium alloy BLISKs [1][2][7-10].

Figure 1.3: Illustrations of conventional bladed disk (left) and LFW produced BLISK (right) [7]

Beyond the BLISK production, the aerospace industry is also actively addressing the critical need for products that can deliver high performance, manufacturability, and ultimately low cost [2][10]. These key drivers, at modern days, have become critical metrics for the end users to meet the demands of current and future air vehicles [2].

Titanium alloys remain an essential part of the aerospace metals due to their high specific strength, corrosion resistance, durability, and elevated temperature performance

[1][2][7][10]. Conventional methods of titanium alloy production are energy and capital intensive, resulting in a relatively expensive production [2]. However, the LFW process is finding increasing interest as a near net shape titanium manufacturing technique for structural aerospace components [10]. Linear friction welded pre-forms can significantly improve the efficiency in which titanium components are fabricated. The use of such pre- 4 forms can reduce the overall cost of raw materials as well as machining time for the finished product [2][10]. An example of the Ti-6Al-4V components that have been fabricated using

LFW at TWI is shown in Figure 1.4.

Figure 1.4: Illustration of a linear friction welded non-planar pre-form and the corresponding machined component from the pre-form [10]

1.3 Motivations for Modeling

LFW process in general has experienced limited industrial application outside of

BLISKs production [2]. The fundamental knowledge and benefits of the LFW process remain largely unknown other than the aero-engine manufacturing sector [10]. Moreover,

LFW process parameters are commonly developed by trial-and-error experimentations

[1][2][9]. Although a trial-and-error approach is accurate and effective, it is not ideal due to intensive capital and time requirements for making successful welds [1][2][9].

Moreover, LFW is highly dynamic and the interface of the work-pieces cannot be observed directly during welding, making process control difficult. As a result, computational modeling of LFW process offers a pragmatic solution to not only understand what is happening throughout the rapid process, but also potentially reduce the cost and time of

5 trial-and-error development by reducing the number of experimental tests needed [2][11-

18].

To date, there are several previously published efforts on modeling the complex

LFW process, mostly for Ti-6Al-4V [2][11][12][13][14][15][16]. However, there are still some shortfalls in terms of the need for accurate experimental data available to calibrate the models. For example, the interface friction coefficient is often assumed or substituted by energy input in many models [11][12][14-16], which are not truly representing the frictional-sliding behavior of LFW process. Moreover, much of the current LFW models are based on 2-D longitudinal section taking advantage of the fast simulation time (since a

3-D simulation can take weeks to complete). However, these models are unable to predict the deformation and heat transfer on the transverse plane that is perpendicular to the oscillation direction [15][18]. Even though considerable effort has been made to develop

3-D LFW process models in the last couple of years [16-19] [20], compromises have to be made in order to balance simulation time, difficulties of convergence, and accuracies of deformation. As a result, currently there is still a need to optimize the simulation work on

LFW process for more accurate predictions and validations with relatively low computational cost.

1.4 Research Objectives and Methodology

The overall goal of this study is to develop a predictive, physics-based 2.5-D computational LFW process model. The methodology used in the present research study is described in Figure 1.5. The predictions were focused on the temperature evolution at the

6 weld interface, the axial shortening and shortening rate of work-pieces, the surface deformation history, and the final flash shape. The model inputs included the experimentally measured flow stress data at different temperatures and strain rates relevant to LFW. The machining parameters were used to define an effective friction coefficient to ensure the correct heat input in the simulation. Finally, the developed models were validated by comparing the computed results with the experimental recorded data.

Figure 1.5: Graphical representation of research objectives and approach

Several cases were investigated using the developed models.

Figure 1.6 summarizes different cases and their relationship. The first group of cases studied were the sub-scaled Ti-6Al-4V coupons with only one square inch of weld area.

Three different heat inputs, designated as “Low”, “Medium”, and “High” energy, were analyzed and validated against the thermocouples and machine recorded data. The same modeling techniques were then applied to investigate full-scaled Ti-6Al-4V coupons and pre-forms, which consisted of plates (similar geometry to sub-scaled coupons) with weld area of five square inches and T-shaped joints with weld area of nine square inches. The calculated temperature and deformation were compared with those obtained using DIC, infrared camera, and machine recorded readings.

Figure 1.6: Various cases investigated in the present thesis research

Given the broad scope, the research was performed in collaboration with Boeing

Research & Technology who performed hot compression and torsion tests in Gleeble®,

EWI who performed linear friction welding experiment of sub-scaled coupons, and OSU’s

CEMAS who performed microstructure characterization and some Gleeble® hot compression tests. The LFW experiments of full-scaled coupons and the demonstration pre-forms were performed by MTI at LIFT. Contributions made those outside collaborators are acknowledged where their work is discussed in the following chapters.

1.5 Dissertation Structure

The present dissertation consists of six chapters and one appendix. Chapter 1 (this chapter) describes the general application and modeling background of LFW process, and the research objectives and methodology.

In chapter 2, a series of literature knowledge are critically reviewed. They include the knowledge of friction welding, LFW process, titanium alloy, mechanical testing, and current modeling techniques of LFW process, from which the technical gaps are identified.

Chapter 3 presents the analysis and modeling results of Gleeble® hot compression and torsion tests. Detailed procedures including data smoothing, analysis of flow stresses, setup of thermal-electrical-mechanical simulations, and corrections for Gleeble® simulations are discussed.

In chapter 4, the LFW processes for Ti-6Al-4V sub-scaled coupons with different heat inputs are modeled in DEFORM®, a commercially available finite element code. The models considered 2.5-D transverse setup, merging of two work-pieces, and calculation of

9 interface friction coefficient as a function of welding time. Smoothing of machine recorded data, definition of material properties and process parameters, mappings of variable across models, and results validations are discussed in this chapter.

In chapter 5, the LFW processes for Ti-6Al-4V full-scaled pre-forms with various heat inputs are modeled in DEFORM®. The differences between welding symmetrical and non-symmetrical work-pieces are of particular interest in this chapter. The process energy ratings, part dimensions, and joint types significantly affect the deformation behaviors, thermal gradient, and material flow during LFW processes.

Summary and conclusions of the present research are presented in chapter 6. The modeling results discussed here indicate the temperature evolution and deformation history in LFW of Ti-6Al-4V can be quantitatively predicted using the 2.5-D models for various process parameters and work-piece geometries. The overall research work represents a contribution to the improved fundamental understandings and modeling techniques of the

LFW process.

Appendix A displays the detailed procedures of merging two work-pieces in

DEFORM, which include mapping of state variables such as temperature, stress and strain components from the conditioning model to the merged model.

Chapter 2 Literature Review

Critical reviews of literature relevant to this research have been explored in order to comprehensively understand the current state-of-the-art knowledge, needs and technical gaps, and to most successfully take the advantage of previous research.

2.1 Introduction to Friction Welding

Friction welding is a solid-state joining technique which can produce sound welds between two components with either similar or different materials [21]. The definition of friction welding process in the American Welding Society (AWS) C6.1-89 Standard is as follows [21]: “Friction welding - coalescence of materials under compressive force contact of work-pieces rotating or moving relative to one another to produce heat and plastically displace material from the faying surfaces”. Under normal conditions, the faying surfaces do not melt and the usage of filler metal as well as shielding gas are generally not required in this process [21][22].

In general, there are three variants of friction welding processes: namely rotary, linear, and orbital friction welding [22]. A schematic of these processes and heat generation magnitudes is showed in Figure 2.1. The rotary friction welding (RFW) has one component rotated about its axis while the other remains stationary, and then the two components are

11 brought together under pressure. The linear friction welding (LFW) requires parts moving under pressure relative to each other in a reciprocating mode through a small linear amplitude in the plane. The orbital friction welding (OFW) is a combination of linear and rotational friction welding where the center of one component relative to the other is moved around a 2D curve [1][2][9][22].

Figure 2.1: Comparison of different moving directions (yellow) and magnitudes of heat generation (black) for three variants of friction welding methods [22]

In friction welding, there are three basic parameters that control the weld properties: relative velocity, process duration, and axial pressure [22]. LFW and OFW have two additional parameters: frequency and amplitude [1][2][9]. These parameters determine the amount of energy input and the rate of heat generation at the faying interface [21][22].

Generally speaking, friction welding consists of three basic stages [22]. The first stage is called the heating stage where two components have relative motion against each other under axial compressive pressure. During this stage, the friction heating raises the temperature at the rubbing interface and softens the surrounding material [9][21][22]. The softened material, eventually, is not capable of holding the applied axial pressure and

12 plastically flows outwards to form the flash carrying oxides and contaminations [9]. The formation of the flash is denoted as the burn-off stage [21][22]. Finally, in the forging stage, the welding process is finished by stopping the motion and applying a large compressive force [21][22].

Friction welding has a number of advantages when comparing with conventional fusion welding processes [1][2][9][21][22]. The major advantage of friction welding relies on direct conversion of mechanical work into thermal energy without melting of the base material [22]. Besides, friction welding typically has very high temperature gradient resulting in a narrow heat-affected zone (HAZ) where welding distortion is kept minimum

[2][4]. Moreover, the friction welding process is often highly efficient in terms of fast joining time on the order of seconds [21]. Additionally, during burn-off and forging stages, flash formation carries out oxides and contaminations, resulting in high-integrity weld [22].

Last but not least, since friction welding can join materials in solid-state, defects associated with fusion welding processes such as solidification cracking, impurity segregation, porosity, slag inclusions, etc., are not present [23].

2.2 Linear Friction Welding Basics

LFW process, as mentioned in previous section, is a rather new friction welding process compared to RFW and OFW processes. The development of LFW aims at extending the current applications for RFW to non-axisymmetric components and relative thin structures [1][2][5][9][22]. The LFW process has one work-piece that is linearly oscillating against another while under a large, compressive force. The friction between

13 the oscillating and stationary surfaces produces heat, which causing the interface material to plasticize. The plasticized material is then squeezed out resulting in the work-pieces to burn-off (or shorten) in the direction of the compressive force. During burn-off, the surface oxides and contaminants are expelled from the interface into the flash, resulting in the formation of integral bond via the pure metal to metal contact [1][2][5][7][9][11][12][22].

A schematic of the process is depicted in Figure 2.2. Despite being one continuous process,

LFW is often defined to occur over four different phases: the initial phase shown in Figure

2.2(a), the transition phase shown in Figure 2.2(b), the equilibrium phase shown in Figure

2.2(c), and the deceleration phase shown in Figure 2.2(d) [2][9][14].

Figure 2.2: Schematics of the LFW process phases [14]

In the initial phase, the two work-pieces are placed under contact pressure and brought into a mutually reciprocating linear motion, and the heat is generated due to friction. As shown in Figure 2.3(a), the asperities soften and deform, increasing the true 14 area of contact between the work-pieces, and the material burn-off is negligible in the direction of the compressive force [1][2][9]. During the transition phase, the size of the asperities in contact continue to increase and eventually the true contact surface area reaches its maximum value. More heat due to friction causes the interface material to plasticize and become highly viscous as shown in Figure 2.3(b). Heat conducts away from the interface, softening more material, and burn-off begins to occur, squeezing the viscous material out from the interface [1][2]. At the equilibrium phase, the force at the interface, the thermal profile, and the burn-off rate reach a quasi-steady-state condition. Significant amount of flash forms through the rapid expelling of the plasticized material as shown in

Figure 2.3(c) [2][6][9]. In the final deceleration phase, once the burn-off reaches the preset value, the relative motion is ramped down and the work-pieces are brought into alignment [2]. In some applications, an additional increased forging force may be applied to help consolidate the weld at the final phase [2][9][13][22]. The detailed changes of some process variables during different LFW phases are illustrated in Figure 2.4. The process description uses Ti-6Al-4V LFW experiment as an example. Similar changes are expected for other ductile metals and their alloys (e.g., steels) [6][9][24].

Figure 2.3: Stages of LFW process: (a) asperity interaction, (b) viscous layer formation, and (c) steady state condition showing the expelled interface material [2]

Figure 2.4: Changes of some LFW process variables at different LFW process phases [9]

There are typically eight process parameters (variables) for linear friction welding and they are listed as follows [2]:

 Oscillation frequency

 Oscillation amplitude

 Applied force (during welding/oscillation)

 Burn-off (axial shortening/upset) or welding time

 Ramp-up time (time taken for the amplitude and frequency to reach set values)

 Oscillation decay time (time for amplitude and frequency to stop)

 Forging force (force that consolidates the weld post-oscillatory motion)

 Forging force time (time that the forging force is applied)

Among above parameters, many researchers appear to consider the frequency, amplitude, applied force, and burn-off to be the primary process inputs of LFW process

[1][2][5][11][12][19][14][22][25]. In many cases, the frequency and amplitude of oscillation can be considered as a single input which is defined as the average rubbing velocity described in Equation 2.1 [2][9][11][14][22][25][26]. As long as the rubbing velocity was kept constant, the variation of frequency and amplitude had shown to have relatively small effect on the results for titanium linear friction welds [2][26].

푉푟푢푏푏𝑖푛푔 = 4 × 푎푚푝푙𝑖푡푢푑푒 × 푓푟푒푞푢푒푛푐푦 (2.1)

In one of the first papers published on LFW process, a relationship between heat input to process parameters were developed based on integrating the average heat generation rate over the oscillatory cycle periods [6]. The final form is displayed in

Equation 2.2:

푞0 = 4 × 휇 × 푃푁 × 푎 × 푓 (2.2) where 푞0 is the average heat generation, 휇 is the friction coefficient, 푃푁 is the normal pressure, 푎 is the amplitude, and 푓 is the frequency of oscillation.

Based on this relationship, an increase in frequency would allow the reduction of applied force to weld special components, such as turbine blades, without buckling [6][9].

However, this analytical equation of heat generation does not consider strain-rate

17 hardening effects nor does it considers the dependence of friction coefficient on the applied pressure. Hence, for strain-rate hardening materials such as titanium alloys, the heat generation rate required to produce high quality welds typically increases with the frequency of oscillation [9][27].

Using numerical analysis for LFW process, Li et al. [19][28] identified that there was an average heat input limit on axial shortening for LFW of Ti-6Al-4V and mild steels as shown in Figure 2.5. Bhamji et al. [29] found from 24 welding trials that the applied pressure for LFW of stainless steels, illustrated in Figure 2.6, was proportional to burn-off rate and inversely proportional to friction time once a limit was reached. Li et al. and Vairis et al. [9][28] investigated the effect of oscillation direction on heat generation and material flow behaviors. They found that the geometric dependence affects the temperature distribution at the interface and flash shape characters, especially for large aspect ratios, as shown in Figure 2.7.

Figure 2.5: Model predicted relationship between average heat input and axial shortening for (a) Ti-6Al-4V and (b) mild steel [19][28]

Figure 2.6: Effect of welding pressure on (a) burn-off rate and (b) friction time of stainless steel 316L [29]

Figure 2.7: Effect of oscillation direction on temperature and flash shape for (a) oscillation along x-axis and (b) oscillation along z-axis of mild steel [9]

2.3 Materials weldable by Linear Friction Welding

LFW process is particularly suitable for welding materials that have superior high- temperature mechanical properties and low thermal conductivity [1][2]. The strong mechanical properties can generate high level of frictional heating, while low thermal 19 conductivity allows the heat to be concentrated at the interface. These facts, therefore, make titanium and nickel-based alloys rather weldable in LFW process using relatively low welding parameters to yield a high level of confined heat to the interface [1][2][9].

Other than titanium and nickel-based alloys, adequate linear friction welded structures have been successfully made in various steels [20][25][31-34] and also tungsten- based alloys and cobalt-based superalloys [1]. In addition, applications of LFW process for various aluminum alloys and aluminum composites have been frequently reported

[1][4][35-37]. Even though aluminum alloys have relatively poor elevated temperature mechanical properties and high thermal conductivities, sound LFW welds are still achievable by using high welding parameters (high applied pressure relative to material strength, and high amplitudes and frequencies) to generate required heat input at the interface [1][31]. In recent years, there are also some publicly available literatures on LFW process of dissimilar metal combinations with varying degrees of success [3][8][38-41].

Although the LFW process of dissimilar materials has limited exploration, the existing research works showed promising potentials of the process for producing highly dissimilar welds (joints between different material classes).

This overview of various materials that have been linear friction welded shows that the LFW process is not limited to titanium and nickel-based alloys, and can also be implemented to applications beyond the aero-engine industry. Percentages of different materials that have been experimentally studied since 1992 are shown in Figure 2.8 [9].

This indicates that the limited understanding on LFW process of non-aero-engine materials is due to lack of research knowledge and not feasibility issues [1][9]. However, as shown

20 in Figure 2.8, the major applications of LFW process are still of particular interest in titanium alloys even at current era [1][2]. Hence, the remainder of this section will focus on the microstructure, mechanical properties, flash morphology, and residual stresses of linear friction welded titanium alloys, especially Ti-6Al-4V.

Figure 2.8: Experimentally studied materials in published research of LFW process since 1992 [9]

2.3.1 Introduction to titanium alloys and Ti-6Al-4V

Pure titanium (unalloyed) has two crystallographic forms: a hexagonal-close- packed (hcp) α form that is stable at low temperature (below 882.5 °C) and a body-centered cubic (bcc) β form that is stable at high temperature (above 882.5 °C) [33]. Therefore, an allotropic transformation from α to β would be expected for pure titanium when the temperature is raised above 882.5 °C, which is often called the “β-transus temperature”

[14][33][34]. As shown in Figure 2.9, alloying elements that are dissolved in titanium can stabilize thermodynamically either the α or the β crystal structures (phases) and consequently influence the β-transus temperature [33][34]. In Figure 2.10, there is a two- phase α + β region that separates the α phase and the β phase, and the width of the two-

21 phase region increases with increasing solute concentration [34]. Thus, titanium alloys may contain different fractions of the two phases at room temperature and are typically classified as α-type, α + β-type, and β-type alloys [14][33]. In general, α alloys have better creep and oxidation resistance, α + β alloys possess an excellent combination of strength and ductility, whereas β alloys have good formability and hardenability [7].

Figure 2.9: Effects of α and β stabilizing elements on the α and β-transus temperatures [33]

Figure 2.10: Pseudo-binary phase diagram for Ti-6Al-xV [34]

In titanium alloys, alloying elements that increase or maintain the stability and strength of the α phase are called α-stabilizers. The most frequently used α-stabilizers are aluminum, tin, and zirconium. Element additions that allow the β phase to retain at room temperature after quenching from α or α + β phase field are called β-stabilizers. Typical β- stabilizers include molybdenum, vanadium, and iron [33]. The mechanical properties of α–

β titanium alloys depend strongly on the thermo-mechanical processing and heat treatment temperatures in the α + β phase field [14][33][34]. An example of α–β phase alloy is Ti-

6Al-4V, which is the key material in this research. This extensively used titanium alloy employs aluminum to promote α stabilization for strengthening and slight reduction in density, while vanadium promotes the hot workability and heat treating capability by stabilizing the β phase [34]. In comparison to other alloy contents and element additions which can improve some of the shortfalls for titanium alloy such as poor shear strength and wear properties, Ti-6Al-4V is typically named the “workhorse alloy of the titanium industry”, and its usage accounts for more than 50% of the total titanium consumption due to excellent strength to weight ratio and corrosion resistance [33][34].

2.3.2 Microstructure of linear friction welded Ti-6Al-4V

Ti-6Al-4V LFW welds usually have several distinct zones: a weld center zone

(WCZ), a thermo-mechanically-affected zone (TMAZ) and a heat-affected zone (HAZ)

[1][2]. A typical example of the weld interface is show in Figure 2.11. Technically, the

WCZ and the TMAZ are both thermo-mechanically-affected zones, but due to the large differences on microstructure that they experienced, these two zones are often considered separately [13][18]. During LFW process, the WCZ experiences significant amount of 23 dynamic recrystallization, while most of the TMAZ does not [35]. The material in HAZ is not deformed mechanically but is thermally affected by the process heating conducted from the weld interface. The region from one TMAZ/HAZ boundary to another is often referred as the TMAZ thickness or the plastically-affected zone (PAZ) [2][35].

Figure 2.11: Macroscopic section of a Ti-6Al-4V linear friction weld [2]

The region that undergoes the most significant change during LFW process is the

WCZ, where the temperature at the interface will pass the β-transus temperature (around

996 °C) that the α grains will transform into β grains [35]. The WCZ also experiences large strain and strain rates (for example, peak value from model predictions is as high as 2500 s-1 [12]) during processing, which results in significant dynamic recrystallization of the high temperature β phase [2]. The fully transformed microstructure will then undergoes rapid cooling after welding to avoid β grain coarsening, resulting in a Widmanstätten or

24 martensitic microstructure as shown in Figure 2.12 (a) and (b), respectively [2][35]. Ahmed et al. and Rack et al. [36] showed that the formation of two microstructures depends on different cooling rate. If the weld cools faster than 410 °C/s, a diffusionless transformation will occur resulting in the formation of martensite with some metastable β phase. On the other hand, if the weld cools slower than 410 °C/s, then a diffusional transformation will appear resulting in a Widmanstätten morphology [2][36].

Figure 2.12: WCZ microstructure: (a) Widmanstätten and (b) martensite [2] Many literatures suggest that the microstructure of the TMAZ does not or partially reaches the β-transus temperature [2][9][13][44-47]. Therefore, fragments of the grains from the base metal (BM) still present and these existing grains tend to be deformed, elongated, and reoriented in the direction of oscillation as shown in Figure 2.13

[2][13][35][36][37]. Due to the nature of the structural stability of Ti-6Al-4V at temperature below 800 °C, it is often very difficult to detect a pure HAZ [2]. According to

McAndrew et al. [2][13][18], the HAZ is more noticeable in welds that are produced with relatively low rubbing velocities or axial pressures, as more HAZ materials will remain heated due to slower shortening rate.

Figure 2.13: TMAZ microstructure of (a) deformed and reoriented grains and (b) broken and elongated α grain structure [35]

2.3.3 Mechanical properties of linear friction welded Ti-6Al-4V

According to Wanjara et al. and Corzo et al. [35][38], the tensile testing of defect free Ti-6Al-4V welds showed that the yield and ultimate tensile strengths of the weld exceeded those of the BM, where failure typically occurred. However, it has been shown that if the weld interface consisted of uncleaned oxides or contaminants, the weldment will most likely fail at the weld line during tensile testing [2]. Wanjara et al. [35] also stated that the weldment produced with low heat input can potentially fail in the TMAZ during tensile testing as shown in Figure 2.14. This was primarily due to grain coarsening resulted from shallower thermal gradient and slower cooling rate [2][35]. Vickers’ hardness tests on linear friction welded Ti-6Al-4V also support the results obtained from tensile tests

[35][39][40]. Several authors have found that the hardness in the WCZ (422 ± 11 [39], 425

± 10 [40] and 398 ± 3 [35]) is higher than that of the BM (302 ± 20 [39], 328 ± 20 [40] and 317-352 [35]). The increased micro-hardness in the WCZ is due to the more refined

26 microstructure [2]. For the same reason, the impact toughness of WCZ was also higher than that of the BM [39].

Figure 2.14: Micro-hardness across weldment showing higher WCZ hardness and lower TMAZ hardness compared to BM hardness [35]

For linear friction welded Ti-6Al-4V, the overall weld quality is strongly influenced by welding parameters [2]. As illustrated in Figure 2.15, frequency and amplitude were shown to have the most marked impact on the strengths and elongations of the welded sample [35]. The axial pressure and material burn-off had a secondary effect on the weld quality. Specifically, little (negligible) changes were seen in terms of microstructure and tensile strength as long as the minimum required pressure and burn-off were reached for welding of Ti-6Al-4V [2][35].Several authors investigated the fatigue performance of as- welded Ti-6Al-4V on the high cycle fatigue (HFC) to the low cycle fatigue (LCF) regime for aero-engine applications [50-52]. They all found that the fatigue cracks always nucleated and thus initiated from the WCZ, regardless of the applied maximum axial

27 pressure during LFW [50-52]. On the other hand, Filpo et al. [42] found that when compared to the parent material, the LCF and HCF specimens of linear friction weld exhibited a drop in number of cycles to failure by 27.9% and 50.4% respectively, as shown in Figure 2.16. They also noted that all the welded LCF and HCF specimens were above their respective minimum fatigue requirements, and it was possible that the fatigue performance of these joints could be further enhanced through post-weld heat-treatment

(PWHT) [42].

Figure 2.15: Relationship between mechanical properties and LFW process parameters [35]

Figure 2.16: Low and high cycle fatigue performance for WCZ and BM of linear friction welded Ti-6Al-4V [42]

2.3.4 Flash morphology of linear friction welded Ti-6Al-4V

During linear friction welding of Ti-6Al-4V, flash typically forms as one single piece and flows out in all directions as shown in Figure 2.17 [2]. According to the experimental and numerical studies by McAndrew et al. [2] [11][13], the flash formation mechanisms for LFW of Ti-6Al-4V can be identified either as “ripple-like” morphology

(Figure 2.17 a) or “smooth” morphology (Figure 2.17 b), depending strongly on the LFW process parameters that were applied.

Figure 2.17: Different flash morphologies: (a) ripple morphology and (b) smooth morphology [2]

The “ripple-like” flash usually occurs when large oscillating amplitude is applied to produce the weld [2][11][13]. When the separation of work-pieces reaches the maximum amplitude see Figure 2.18 (a), very high strain rates will be generated, resulting in significant local yielding as shown in Figure 2.18 (d) [2]. Highly heated and viscous material will then be sheared from the interface to the flash as the oscillatory motion is reversed, and each sheared layer corresponds to a ripple in the flash formation [11][13].

Schroder et al. [11] also analyzed this behavior and concluded that the ripples are more noticeable when the ratio between the PAZ and the oscillating amplitude is reduced. They also suggested that it was necessary to reduce the ratio below 1 to generate the ripples [11].

The “smooth” morphology typically occurs when the PAZ has sufficient thickness and the oscillating amplitude is relatively small [2][11][43][13]. When the maximum displacement between work-pieces is reduced see Figure 2.18 (b), the flash will not be separated too much from the work-pieces, resulting in a forging (extrusion) action [13]. Based on the modeling prediction by McAndrew et al. [13], the temperature at the boundary between the flowing material and the rigid work-piece approximately corresponds to the β-transus temperature for Ti-6Al-4V see Figure 2.18 (c) regardless of the flash morphology type.

Thus, the thickness of the flash is then directly related to the extent of material that is heated above the β-transus temperature [2][13].

Figure 2.18: FEA results of flash formation mechanisms: (a) ripple, (b) smooth, (c) boundary temperature, and (d) region of high strain rate [2]

2.3.5 Residual stress of linear friction welded Ti-6Al-4V

In literature, many researchers have investigated the post-weld conditions for Ti-

6Al-4V welds made by LFW process and found that the residual stresses did present

[1][2][40][54-56]. Understanding the formation mechanisms of residual stress is very critical since it may negatively affect the LFW weld performance. In general, there are two mechanisms causing the formation of residual stress [1][2]. The first is due to the thermally induced strain during heating and cooling processes [44]. The second is due to the plastic deformation [45].

Among all the investigations, there is an agreement that regardless of the process input combination, the residual stresses are generally highest along the longest contacting surface (Figure 2.19 a), followed by the shortest contacting surface (Figure 2.19 b), and smallest in the direction perpendicular to the weld plane (Figure 2.19 c) [2][40][44][46].

Romero et al. and Bhamhi et al. [1][40] believed that the increased residual stress along the longest contacting surface was due to larger thermal gradient. McAndrew et al. [2] claimed that the lowest residual stress in the direction perpendicular to the weld plane was due to uniform expansion and contraction of heated material that creates a relatively uniform plastic strain field.

According to the review by Bhamji et al. [1], post-weld heat treatment (PWHT) can significantly reduce the residual stress in the LFW welds by as much as 90%. However, the extent of residual stress relief is still dependent on the dimension of the welded work- piece [1][2][46]. For a fairly large welded sample, significant stresses remained after identical PWHT when comparing with a much smaller sample that displayed similar

32 residual stress profiles with the large sample prior to PWHT [1][2]. On the other hand,

Romero et al. and Turner et al. [40][44] showed that the peak tensile residual stresses can be reduced with increased applied pressure during LFW as shown in Figure 2.19. Higher pressure increases material burn-off and thus reduces the interface temperature as well as thermal gradient mismatch, which therefore reduces the residual stress magnitude [2][40].

Figure 2.19: Distributions of residual stress components along different axis measured by synchrotron X-ray diffraction [40][2]

2.4 Current Linear Friction Welding Modeling Methods and Results

In practice, many areas of the LFW process are very difficult to evaluate from the experiment, particularly the phenomena associated with the weld interface such as temperature and deformation histories [2]. This is primarily due to the highly dynamic nature of the LFW process itself and the fact that the interface of the work-pieces cannot be observed directly during welding [1]. Therefore, analytical and numerical modeling may offer an alternating way rather than doing experiment alone to investigate the incredibly complexed process. Moreover, modeling of LFW process also offers many benefits especially the reduction of both time and capital investments on doing trail-and-error qualification tests [1][2][9]. Hence, many researchers have developed a variety of models and modeling techniques for the LFW process over the recent decade [2][5][9][11-13][15-

22][24][26][28][30][34][47]. In this section, the current state-of-the-art LFW modeling techniques and results to date will be introduced and discussed. The primary focus will still be on joining of Ti-6Al-4V as to achieve the overarching goal of the present research work.

2.4.1 Analytical models for LFW process of Ti-6Al-4V

Analytical models are simply mathematical models that have a closed form solution, i.e. the solution to the equations used to describe the outputs from a system can be expressed as mathematical functions [48]. For complex process like LFW, the analytical models typically involve many assumptions such as constant material properties or one- dimensional heat flow etc. in order for easier model setup and quicker computation compared to numerical models (e.g., finite element models) [2]. To date, there were very

34 few papers on analytical modeling of LFW process relative to experimental and numerical investigations. This is due to the complexity of the process itself (i.e. combination of thermal and mechanical actions) and less active research group on LFW process worldwide

[2]. However, good correlations of limited analytical models with experimental results can still be reached [12][43][24].

Vairis et al. [24] developed one-dimensional thermal model for the LFW process of Ti-6Al-4V at the initial phase (described in section 2.2). The temperature calculation was based on the equation that developed by Carslaw et al. and Jaeger et al. [49] as shown in Equation 2.3:

1 (푚+1) (푚+1) 1 ∞ (2.3) 2 푞0푘2푡 2 Γ( 푚 + 1) (2푛 + 1)퐿 − 푥 (2푛 + 1)퐿 + 푥 푇 = 2 ∑ {𝑖(푚+1)푒푟푓푐 0 + 𝑖(푚+1)푒푟푓푐 0 } 푘 1 1 푛=0 2(푘푡)2 2(푘푡)2

where T is the temperature, x is distance away from interface, t is the welding time, 푘 is the thermal diffusivity, Γ is the Euler gamma function, L0 is the length of the work-piece stick out, m is a constant, q0 is the heat flux, i is the temperature from previous iteration, and erfc(x) is the complementary error function [49].

In this thermal model, Vairis et al. [24] also assumed that there was no convective heat loss to the environment, the friction coefficient increased linearly with time, the contact area increased linearly from 0% to 100% with time, and the model was static (no reciprocal movement). The comparison of the modeling results and the experimental results was shown in Figure 2.20. It can be seen that the predicted thermal profile had relatively large difference with thermocouple readings after 2 seconds under constant

35 material properties. The model that used non-linear material properties showed better correlation to the experimental temperature profile [24].

Figure 2.20: Thermal profile comparison between analytical model and thermocouple readings at 1.6 mm distance away from the interface during initial phase [24]

Turner et al. [12] developed a one-dimensional heat flow model for predicting temperature gradients at the HAZ during the equilibrium phase of the LFW process on Ti-

6Al-4V. In their model, the temperature calculation was based on Equation 2.4:

푣 ∗ 푥 푇 = 푇 + (푇 + 푇 ) ∗ exp (− ) (2.4) 0 푓푙푎푠ℎ 0 훼 where T is the temperature, x is the distance away from the interface, T0 is the initial temperature of the material, Tflash is the temperature of extruded flash, v is the burn-off rate, and 훼 is the thermal diffusivity [12].

Turner at el. [12] compared the thermal profiles generated from the analytical model to those obtained from the numerical models for different process parameters as shown in Figure 2.21. Both sets of modeling results correlated well with each other and 36 further indicated that the HAZ temperature gradient was sensitive to the designed amplitude and frequency, while to the less extent of applied load [12]. It should be noted that, prior knowledge of burn-off rate and interface/flash temperature must be known to initiate this analysis, meaning that the analytical model requires inputs that may have to be obtained from other modeling efforts (e.g., finite element models).

Figure 2.21: Comparisons of predicted thermal profiles generated by different process parameters between analytical and numerical models at the end of the equilibrium phase. (a) fixed frequency (45 Hz) and pressure (110MPa), varying amplitudes; (b) fixed amplitude (2 mm) and pressure (110MPa), varying frequencies; (c) fixed frequency (45 HZ) and amplitude (2mm), varying loads; (d)-(f) the corresponding predictions from analytical models[12]

Schroeder et al. [43] proposed an analytical model to predict the HAZ width formed during the equilibrium phase during LFW of Ti-6Al-4V. They modified Equation 2.5 that developed by Turner et al. [12] as follows:

푇 − 푇 푓푙푎푠ℎ 0 −1 (2.5) 푥퐻퐴푍 = 훼 ∗ log [ ] ∗ 퐴푠 ∗ 휌 ∗ 퐻 ∗ 푇푓푙푎푠ℎ ∗ 푞 푇퐻퐴푍 − 푇0 where 푥퐻퐴푍 is the thickness of the HAZ, 훼 is the diffusivity, 푇푓푙푎푠ℎ is the temperature of the flash, 푇0 is the initial temperature of the material, 푇퐻퐴푍 is the temperature of the HAZ,

퐴푠 is the area of the mating surfaces, 휌 is the density, H is the specific enthalpy, and q is the power input [43]. In this analytical model, all of the parameters other than power input were assumed to be constant. The temperature of the HAZ was assumed to be 900 °C. As shown in Figure 2.22, the modeling results in general predicted the experimental trends well especially for energy input rates greater than 5 kJ/s [43].

Figure 2.22: Comparison of measured and predicted HAZ width with increasing energy input rates [43]

From a practical point of view, analytical modeling of LFW process, especially on describing heat flow phenomena, has both advantages and disadvantages [2]. The complex

LFW process can often be simplified by an analytical model in which the model setup may become easier, the simulation time may be greatly reduced, and good matches with experimental results may still be obtained [2][11][43][24]. However, as can be seen from above modeling case studies, the assumptions and simplifications of the LFW process also limit the ability of the analytical model for accurate predictions [2]. Assumptions such as one-dimensional heat flow and constant material properties are too ideal to account for the physical processes taken place in LFW. Therefore, for LFW simulations, numerical modeling is often employed in such a way that the complex problem can be discretized into more manageable sub-problems [2][9]. The solutions to these sub-problems are then approximated at finite time steps. Detailed discussion on numerical modeling of LFW process will be addressed in the next section.

2.4.2 Numerical modeling approaches for LFW of Ti-6Al-4V

Many researchers have successfully developed two and three dimensional (2-D/3-

D) numerical models for LFW of Ti-6Al-4V [2][5][9][11-22][24][26][28][30][34][47].

The majority of them considered 2-D models, which have been shown to be capable of providing adequate insights into many of the welding responses associated with the LFW process [2][5][11-13][15-22][24][26][30]. The main advantage of 2-D models is that the clock time required for completing the simulation is short, which particularly makes 2-D models rather suitable for parametric studies [1][2][9]. However, the nature of 2-D models

39 also limits the understanding of the full LFW process behavior as these models do not account for the flash shape, change of burn-off, and the heat transfer during heating and cooling in the direction transverse to the oscillation direction [2]. On the other hand, there are successful developments of 3-D LFW models with interesting findings

[2][9][14][15][28]. One of the examples for 3-D modeling of LFW process is illustrated in

Figure 2.23. The main advantage for a 3-D model is that the multi-directional material flow and heat transfer behaviors during LFW process can be completely described [2]. This can provide very important information on thermal and deformation profiles at, particularly, corners of work-pieces during LFW which a 2-D model cannot apply [15]. However, 3-D models in general are extremely time consuming, e.g., computation time exceeding weeks without guaranteed convergent solution [2][15][26]. Consequently, coarser mesh has to be used to reduce the number of elements in the model and thus, the predicted flash characters

(rippled or smoothed) and accuracies of the model are sacrificed [15][26]. Therefore, 2-D modeling approach still remains an important one for numerical simulation of LFW process

[2].

Figure 2.23: Example of 3-D LFW process model developed in DEFORM [15]

There are three 2-D modeling approaches that have been used in the literature to model LFW process [2][13][18][26]. The first approach involved modeling only one deformable work-piece oscillating against a rigid (non-deformable) object or surface as shown in Figure 2.24(a). This modeling approach allows for a short computational time as only half of the real geometry is considered. However, as only one work-piece being modeled, it is impossible to predict the flow behavior when two work-pieces interact with each other [2]. Hence, with the increase of the computational power, many researchers expanded the first modeling approach so that both deformable work-pieces were considered as illustrated in Figure 2.24(b). Despite considering both work-pieces, this type of models treated the interaction between the two work-pieces in such a way that the interface never truly merged (bonded) during the transition and equilibrium phases of

LFW; in reality, bond formation would occur resulting in work-pieces to be joined together

[2][12]. As a result, these models would not correctly predict the real flow behavior after the work-pieces were joined together [2]. It is noted that for both the first and second 41 approaches, the friction coefficient needs to be known so that the thermal profile can be predicted accurately [2].

The third approach was developed to account for the joined interface after the initial phase in LFW as shown in Figure 2.24(c). This approach was first developed by Turner et al. [12], who found that prior to the work-pieces merging, there was negligible macroscopic plastic deformation for Ti-6Al-4V. Once a viscous layer was formed, the process could be modeled as single-body [12]. A temperature profile then needed to be mapped onto the single-body prior to merging (to account for the heat generated before) [12]. This approach considers the true interface flow behavior, but the phases prior to work-pieces merging are not modeled [2][13][18][26].

Figure 2.24: 2-D LFW modeling approaches: (a) one work-piece and one rigid part, (b) two deformable work-pieces and (c) single part representing two joined work-pieces. Oscillation direction: left and right [2]

Based on above introduction, it can be seen that no single modeling approach can include the complex LFW process behaviors all together. Thus, the combination of the modeling approaches has been used by many researchers in such a way that the initial or

42 transition phases of the LFW process are modeled first with one or two deformable work- pieces as shown in Figure 2.24(a)&(b) [2][11-13][16][18][26][43]. The generated thermal and deformation profiles are subsequently mapped onto a single work-piece (Figure

2.24(c)), as initial boundary conditions, to model the equilibrium phase of the LFW process

[2][11-13][16][18][26][43]. This combined modeling approach can work for both 2-D and

3-D models [2][15]. The first model is typically called the conditioning stage model in which the heating is generated mainly by friction [11][43]. Thus, the definition of the friction coefficient as a function of temperature at the interface has to be specified [2][11].

The subsequent model based on single work-piece is often called the equilibrium or merged stage model for which the heat generation is primarily due to plastic deformation

[2][11][43]. An adiabatic heating fraction must be defined for this subsequent stage model to account for the efficiency of mechanical work to heat generation [2]. As the assumption of truly merging is made for the equilibrium stage model, there will be no friction coefficient needed for this model [2][11][12][43]. A typical example showing the setup and sequentially coupling of the two stage LFW models is demonstrated in Figure 2.25. It is noted that the beginning of the equilibrium phase is commonly selected at the time when the burn-off rate (or upset rate) of the work-pieces reaches constant (see Figure 2.25(b)) to represent the complete contact of the work-pieces [5][6][13][18][26].

In the conditioning stage model setup, the most important setting is the definition of friction coefficient as a function of temperature for thermal profile generation

[2][11][43]. Some researchers used constant friction coefficient or piecewise linear dependence on temperature according to the friction-temperature relations developed by

Vairis et al. [24] via shear pin tests of Ti-6Al-4V [17][19], as shown in Figure 2.26. On the other hand, some other researchers estimated friction coefficient based on Coulomb’s

Friction law as shown in Equation 2.6 [11][43]:

퐹 (2.6) 휇 = 𝑖푛푡 퐹푁

where 퐹𝑖푛푡 is the interface shear force and 퐹푁 is the normal force. As the shear force at the interface cannot be measured directly from the experiment, this calculation of friction coefficient typically involves LFW machine readings and weights of the oscillating parts and tooling [11][25][26][43]. A schematic diagram of the LFW machine is illustrated in

Figure 2.27. As shown in this figure, a LFW machine can usually record the displacement of the oscillator, the in-plane shear force, and the applied normal force as a function of time. Thus, the interface shear force can then be calculated based on Equation 2.7 [25][26]:

퐹𝑖푛푡 = 퐹𝑖푛 − 푀 × 푎 (2.7)

where 퐹𝑖푛 is the in-plane shear force, M is the mass of both work-piece and tooling and a is the acceleration of the oscillator. If sinusoidal motion is assumed for the oscillator, the acceleration can be estimated by taken the second derivative of the oscillator displacement as a function of time shown in Equation 2.8 [25][26]:

푑2푥 (2.8) 푎 = = −퐴휔2 sin(휔푡) = −푥(2휋푓)2 푑푡2

where x is the oscillator displacement, A is the oscillation amplitude, 휔 is the angular frequency and 푓 is the oscillation frequency.

Figure 2.26: Assumed interface friction coefficient (b) [19] and (c) [17] based on shear pin testing results (a) by Vairis et al. [24]

Figure 2.27: Schematic diagram showing the LFW machine [26]

The calculated effective friction coefficient based on LFW machine measurements gives oscillating values with both positive and negative signs [5][11]. This is primarily due to that the calculated friction coefficient is directly linked to the measured in-plane shear force, which typically has large variance and different signs as shown in Figure 2.4

[2][5][11]. Thus, the absolute values of the calculated effective friction coefficient are usually taken and displayed in the way shown in Figure 2.28 [11]. Moreover, many researchers then applied time dependent temperatures that were measured by thermocouples at designed distances to the weld interface to correlate the calculated effective friction coefficient to temperature [2][11][43]. However, the large variance of the calculated effective fiction coefficient is not very convenient for modeling purposes as one can argue that either the peak or average values might be used instead (see Figure 2.28)

[11]. McAndrew et al. [2][13][18][26] employed the regression analysis (to be discussed in next paragraph) to calculate the friction coefficient for different phases of LFW process.

They found that the calculated values could range between 0.8 and 1.3 with lower applied forces, while at higher forces, it could be consistently around 0.4 as shown in Figure 2.29

[13][18][26]. Based on the large variance, they commented that it was highly unlikely that the Coulombic friction was occurring at any point during LFW process and thus, the use of Coulombic friction even at the initial phase of the LFW process was highly questionable

[26].

Figure 2.28: Calculated effective friction coefficient as a function of time based on machine recorded experimental data [11]

Figure 2.29: Calculated effective friction coefficient with different applied process parameters for different phases of LFW process based on regression analysis [26]

Based on the concerns towards Coulombic friction calculation and application,

McAndrew et al. [13][18][26] skipped the steps that involved the estimation and definition of the interface friction coefficient for the conditioning stage. Instead, they developed a stationary model (no movement of any work-pieces) to simulate the thermal profiles of the

48 interface at the last moment of the initial phase of LFW process. In that thermal model, the heat due to friction was substituted by a uniform heat flux defined across most of the interface in such a way that the flux was linearly reduced to 50% towards the edges of the work-pieces [13][18][26]. The reduction of heat flux at edges, according to McAndrew et al. [2][26], was due to the sinusoidal movement of one work-piece which the corners

(edges) were only in contact with the other work-piece 50% of the time in one period of oscillation. There were two methods introduced by McAndrew et al. [26] to determine the heat flux. The first method involved using the power input that was calculated based on the machine recorded in-plane shear force and oscillator displacement histories as shown in

Equation 2.9:

푡푊 (2.9)

퐸푥 = ∫ 퐹𝑖푛푡 푣 푑푡 0 where 퐸푥 is the total energy input into the weld interface, 퐹𝑖푛푡 is the interface shear force, v is the velocity of the oscillator and tW is the total welding duration. To determine the average power input for the initial phase of LFW process, the energy input was divided by the phase duration [26]. The second method employed the regression analysis to identify which inputs and input interactions were statistically important for the process outputs of interest [13][18][26]. In this analysis, an “analysis of variance” (ANOVA) was conducted with several statistical criteria as shown in Table 2.1. The equations for process outputs such as the average power input for initial phase of the LFW process was shown in

Equation 2.10:

Table 2.1: Statistical results for important inputs [26]

푃 = −18.26366 + 0.32678 × 푓 + 9.27832 × 퐴 + 0.061476 × 퐹푁 (2.10)

−4 + 0.087638 × 푓 × 퐴 − 4.21790 × 10 × 푓 × 퐹푁

− 2.233759 × 10−3 × 푓2 − 1.93524 × 퐴2 where A is the oscillation amplitude, 푓 is the oscillation frequency, and 퐹푁 is the applied welding normal force [13][18][26]. The thermal modeling setup and results obtained by

McAndrew et al. (using method 1) were shown in Figure 2.30. The predicted interface temperature profiles by both power input calculation methods correlated well to some extent with the thermocouple measured results from the actual LFW experiments (as shown later in Figure 2.51) [18].

Figure 2.30: 2-D thermal model showing: (a) the heat flux approach and (b) generated thermal profile at the end of the initial phase [18]

On the contrary, Schroder et al. [11][43] and Turner et al. [12] used the effective friction coefficient at the interface for the conditioning stage model. The calculated effective friction coefficient as a function of time was displayed in Figure 2.28

[11][12][43]. They then smoothed the effective friction coefficient value in such a way that the same time averaged power input was reproduced by the Coulomb’s friction law

[11][12] as shown in Equation 2.11:

∫ 퐹 푣푑푡 (2.11) 휇 = 𝑖푛 4푓푎퐹푁∆푡

where 퐹𝑖푛 is the in-plane shear force, v is the oscillator velocity, 푓 is the oscillation frequency, a is the oscillation amplitude, and 퐹푁 is the applied normal force. Schroder et al. [11][43] and Turner et al. [12] also combined the estimated effective friction coefficient and temperature (measured from thermocouple) to obtain the temperature dependent friction coefficient. As shown in Figure 2.31, they found that the temperature dependent

51 friction coefficient was consistent for different process parameters and could be approximated by Equation 2.12 (black line in Figure 2.31) [11][12]:

Figure 2.31: Temperature dependent effective friction coefficient for different energy input rates. An approximation was fitted in black line [11]

휇 = 0.15 ln(푇) − 0.625 (2.12)

For the equilibrium phase of the LFW process, the relation between the estimated effective friction coefficient by Equation 2.11 and the energy input rate was shown in

Figure 2.32. It can be seen that the applied normal force influenced the friction coefficient the most [11][43], which was comparable to the results obtained by McAndrew et al.

[18][26]. For the conditioning stage model based on the setup shown in Figure 2.25(a) and

Equation 2.12, the results of the predicted temperature profiles at the interface correlated really well with those obtained experimentally by thermocouples as illustrated in Figure

2.33 [43].

Figure 2.32: Calculated effective friction coefficients for different process parameters [11]

Figure 2.33: Comparison of predicted and measured thermal profiles at the end of conditioning stage for high (left), medium (center), and low (right) energy input rates [43]

Regardless of the method used in the conditioning stage model to obtain the thermal profiles, another crucial parameter is the transition point (time) from the conditioning stage to the equilibrium or merged stage [2][11]. In principle, the transition point corresponds to the complete contact and bonding of two work-pieces at which point any frictional sliding ceases [11]. As briefly introduced previously, some published studies considered the equilibrium phase starting when the burn-off rate reached a constant value as shown in

Figure 2.25(b). However, interruption tests towards the end of the transition phase

(determined by constant burn-off rate assumption) by Schroder et al. [11] indicated that the

53 coalescence (adhesion) of two work-pieces did not occur uniformly as shown in Figure

2.34. True contact was found to start first at the central portion of the work-pieces, away from the edges. Therefore, any assumptions of the transition point based on known burn- off and burn-off rate might be questionable [11]. In order to avoid using burn-off rate as the only criterion, Schroder et al. [11] proposed a new criterion based on estimating the temperature attained at the weld interface. This estimation was done by assuming 1-D heat flow resulted from the energy input as shown in Equation 2.13:

Figure 2.34: Interruption test of LFW process showing localized adhesion between work- pieces at the end cycles of the conditioning stage based on constant burn-off rate [11]

푄̇ 휅푡 −푥2 푥 (2.13) 푇(푥, 푡) = 푇 + {2( )1/2 exp ( ) − 푥 ∗ 푒푟푓푐 [ ]} 0 퐾 휋 4휅푡 (4휅푡)1/2 where K is the thermal conductivity, t is time and x is the axial distance from the weld interface, 휅 is the thermal diffusivity, erfc (x) is the complementary error function, and 푄̇ is the energy input rate defined as Equation 2.14 [11]:

∫ 퐹 푣푑푡 (2.14) 푄̇ = 𝑖푛 ∆푡퐴 where 퐹𝑖푛 푎푛푑 푣 are the time dependent in-plane shear force and oscillator velocity respectively, ∆푡 is the time period, and A is the weld area. Since the targeted temperature

54 was at the weld interface, the axial distance x in Equation 2.13 can be set as zero so that the Equation 2.13 can be simplified as Equation 2.15 [11]:

푄̇ 휅푡 1 (2.15) 푇(푥, 푡) = 푇 + 2 ( )2 0 퐾 휋

Based on the measured times for the onset of uniform upsetting and estimations made using

Equation 2.15, it could be seen that the onset of the equilibrium stage was predicted reasonably accurately by assuming an interface temperature of 1200 °C, as shown in Figure

2.35 [11]. Moreover, by comparing with thermocouple measured temperature in Figure

2.36, Schroder et al. [11] suggested that the plastic deformation was consistent with the onset of the equilibrium stage which occurred well before the liquidus temperature was reached, and that 1200 °C was indeed a reasonable estimate. It was also noted that there was some evidence indicating that welds fabricated with low energy input rates started to reduce axial length before reaching 1200 °C (1000-1100 °C).

Figure 2.36: Thermocouple measured thermal cycles for high, medium, and low energy input rates at initially 1 mm away from the weld line [11]

As briefly discussed at the beginning of this section, mesh design is important in

LFW process model especially for the accurate prediction of the interface deformation and flash formation [2]. A decrease of the element size increases the accuracy of the model output but also results in more computational time [2]. For modeling of LFW process, as the plastic deformation primarily occurs at the weld interface, much finer mesh are typically used around the interface and coarser mesh used farther back as shown in Figure

2.37 [1][2][9]. According to the review by McAndrew et al. [2], the typical interface 56 element size used for modeling LFW process are between 1 mm and 0.08 mm. A mesh size below 0.25 mm has been shown to be sufficient for capturing the detailed flash characters for most 2-D models. 3-D LFW process models require substantially more elements and thus computation time than 2-D models due to the consideration of the heat and mass flow in the third axis [2][15]. Consequently, to trade some of the accuracy of the outputs to reduce the simulation time, larger elements with sizes of 0.5 mm to 1 mm are commonly used in 3-D models [2][9][14][15][28].

Figure 2.37: Typical meshing setup for 2-D LFW model [2]

The last part of the modeling approach is the definition of the constitutive material data, especially the flow stress which is also very important for the accuracy of the modeling results [1][2][9]. The flow stress data in LFW process models of Ti-6Al-4V is described in either a tabular format or equation-based format [2]. The equation-based approach for estimating the flow stresses of Ti-6Al-4V, applied by several researchers

[12][14][19][54][55], included the Johnson-Cook model, the Norton-Hoff model, and the 57

Zener-Hollomon model. However, extra care is needed when using equation-based approaches as they do not always accurately represent the true flow stress behavior over the regime of interest [2][12][55]. Li et al. [19] employed the Johnson-Cook model to calculate the flow stress of Ti-6Al-4V as shown in Equation 2.16:

푇 − 푇 푛 ∗ 0 푚 (2.16) 휎푦 = [퐴 + 퐵(휀푝) ][1 + 퐶퐼푛(휀̇ 푝)] [1 − ( ) ] 푇푚 − 푇0

∗ where 휎푦 is the flow stress, 휀푝 is the plastic strain, 휀̇ 푝 is the normalized plastic strain rate,

푇0 is a reference temperature, 푇푚 is the melting temperature, and A, B, C, m, and n are material constants. Values assigned to A, B, C, m, and n were discussed in reference [19].

However, McAndrew et al. commented that the Johnson-Cook model gave poor flow stress estimations at low strain rates and the flow stress linearly would reduce to zero with temperature, which rarely occurred in practice [2].

The data in tabular format is typically obtained via mechanical testing of the material such as tensile, compression, and torsion tests [53]. The testing results involve flow stresses at different strains, strain rates, and temperatures which generally appear to be much more robust and reliable than the equations as the FEA code does not have to extrapolate too much outside the region where the data is not applicable [2][12][55]. An example of the measured flow stresses by hot compression test in tabular format is displayed in Table 2.2. However, generating the data needed can be both expensive and time consuming due to extensive material preparation, mechanical testing, and data corrections need [2]. Therefore, the publicly available flow stress data for Ti-6Al-4V is very limited in the beta regime (above 1000 °C) and at high strain rates experienced during the LFW process (up to 1000 s-1) [2][55]. Turner et al. [12] estimated the stress-strain 58 curves for Ti-6Al-4V using the JMatPro software, as shown in Figure 2.38. More rigorous validation of such predicted flow stress is still needed especially when the alloy compositions change.

Table 2.2: Flow stresses of Ti-6Al-4V as a function of temperature, strain and strain rate by hot compression test [53]

Figure 2.38: Predicted stress-strain curves for Ti-6Al-4V by JMatPro at various temperature, strain, and stain rate levels [12]

2.4.3 Numerical models and modeling results for LFW of Ti-6Al-4V

To date, most numerical simulations for LFW of Ti-6Al-4V in the open literatures employed finite element analysis (FEA) [2][5][9][11-13][15-22][24][26][28][47][30][34].

The FEA works by discretizing a real object into a large number of finite elements.

Mathematical equations are used to calculate the physical effects of each element, and the computer then adds up all the individual effects to predict the overall behavior of the actual object [2][50]. For modeling of LFW process, several FEA packages have been developed and applied, including Abaqus, Ansys, DEFORM, and Forge [2]. Figure 2.39 shows a couple of examples of numerical LFW process models developed using different FEA packages.

Figure 2.39: Numerical simulations of LFW process by (a) Abaqus [9], (b) Ansys [51], (c) DEFORM [43], and (d) Forge [12]

The primary advantage of FEA is that it allows for the prediction of many outputs that are difficult to obtain experimentally [9]. To date, Ti-6Al-4V weld responses have

61 been investigated via different numerical LFW process models [2]. The majority of them included the studies of welding residual stress, interface contaminant expulsion, flash morphology, strain rates, and thermal field predictions.

Turner et al. [44] developed an elastic-viscoplastic formulation to predict the residual stress for LFW of Ti-6Al-4V during both welding and cooling stages as shown in

Figure 2.40. Their modeling results illustrated that the residual stress fields arise primarily as a consequence of cooling. They also found that the LFW welding parameters had only minimal influence on the residual stress fields and instead, the geometry of the parts had a large impact on the magnitude of the residual stress [44]. The overall predictions of the residual stress fields by Turner et al.[44] were in reasonable agreement with experimental measurements based on high energy synchrotron X-ray diffraction technique by Frankel et al. [46].

Figure 2.40: Predicted 휎푥푥 stress field at (a) welding time = 0.044 s, (b) 0.22 s and (c) 0.44 s of welding, and (d) 1 s, (e) 5 s and (f) 210 s of cooling for a Ti-6Al-4V LFW weld (left) [44]

Based on modeling approaches for LFW of Ti-6Al-4V by Frankel et al. [46] and

IFW of nickel-based superalloy by Grant et al. [52], Li et al. [9] built three-dimensional coupled thermo-mechanical models (see Figure 2.41) to analyze the residual stresses of Ti-

6Al-4V welds made by LFW process, where three steps were set up: forging step, cooling step and a final step during which clamps were released. Their results showed that there were large residual stresses occurred across the weld line and they were not uniformly distributed. The tensile residual stresses at the interface were largest in the oscillation direction (y-direction), while the largest compressive residual stress were present at the flash root (z-direction) [9].

Figure 2.41: Predicted residual stress in LFW of Ti-6Al-4V weld: (a) Von Mises stress, (b) stress in x-direction, (c) stress in y-direction (oscillation direction) , and (d) stress in z-direction [9]

In order to understand how interface contaminants are removed during LFW of Ti-

6Al-4V, which can negatively affect the mechanical properties and service life of a weld,

McAndrew et al. [13][18] and Turner et al. [12] post-processed the simulated results. 63

Specifically, as seen in Figure 2.39(d) and Figure 2.42, a point-tracking method was used to allow the positions of these points to be monitored throughout the process by selecting a series of nodes within the mesh and along the weld interface. They found that these points, representing interface oxides and foreign particles, were eventually removed from the weld line and extruded into flash as long as sufficient burn-off was achieved [12][13].

McAndrew et al. [18] did further investigations on the effects of process parameters on interface contaminant removal. As shown in Figure 2.43, there was a good agreement between the experiments and the FEA models that the surface contaminants were increasingly expelled from the weld interface as the burn-off was increased [18]. They also found that an increase of applied force could reduce the overall burn-off required to expel contaminants, and an increase of rubbing velocity had little influence on minimal required burn-off to expel contaminants [18].

Figure 2.42: Point tracking at: (a) initial of the process, (b) during material flow and (c) complete expulsion [13]

McAndrew et al. [16] also investigated the required minimum burn-off for complete contaminant removal of non-symmetric weld geometries. As shown in Figure

2.44, they applied the same point tracking methods in a LFW process model of Ti-6Al-4V

T-joint. The model predicted that for the T-joint configuration not all of the tracked points

(contaminants) were removed from the interface after a burn-off of 3.25 mm. This observation could be due to the burrowing effect that the tracked points may have been pushed into the large base or flange plate instead of being allowed to freely flow into the

65 flash [16]. This results, according to McAndrew et al. [2][16], further indicated the importance of work-piece geometry on material flow behavior for LFW welds.

Figure 2.44: Interface contaminants removal of T-joint using point tracking: (a) initial location of points and (b) evolution of points [16]

As mentioned previously, the flash is an important character for linear friction welds due to the reason that it represents plasticized material flow and carries interface contaminants [2]. Section 2.3.4 discussed that the flash shape and formation mechanism are strongly affected by LFW process parameters [13]. Several researchers developed numerical models to analyze the flash formation mechanism and its relation to process parameters [2][11-13][13]. For LFW of Ti-6Al-4V, McAndrew et al. [2][13] numerically simulated the two flash morphologies: the ripple morphology and the smooth morphology.

Similar flash formation mechanisms were obtained by Turner et al. [12] and Schroder et al. [11]. Turner et al. [12] compared the predicted flash shapes with those observed

66 experimentally as shown in Figure 2.45. They claimed that the flash in LFW of Ti-6Al-4V weld is produced by two primary effects: the forging effect and the dragging effect [12].

For welds made by small amplitudes (less than 2 mm), the axial load is the dominant parameter for forging hot material out of the weld interface. However, for larger amplitudes, the hot material instead will be dragged to interfere with vertical edge of the weld stub at each oscillation, causing a distinct rippling pattern (see Figure 2.45(c)) [12].

Schroder et al. [11] analyzed the relation between the flash formation mechanisms of Ti-6Al-4V LFW welds with welding energy input rates as shown in Figure 2.46. Their predictions matched reasonably well with experimental observations that for process parameters producing low energy input rates, the flash was wider (thicker) due to thicker

PAZ when compared to welds made by relatively high energy input rates [11]. They also pointed out that the flash varied in surface morphology from flat to rippled depending mainly on two modes: the forging mode and the shearing mode as seen in Figure 2.47 [11].

If the PAZ is of sufficient thickness and the oscillation amplitude is small (low energy input), a forging mode will be applied for which interface material extrusion will appear resulting in a smooth but wide flash formation. Conversely, if the PAZ is much thinner and the oscillation amplitude is large (high energy input), a shearing mode will take place where the edge in contact with hot and soft material will be penetrated and sheared out from the interface [11]. This accumulates in front of the shear zone (dashed line in Figure 2.47) and forms ripples during every reversed motion. Schroder et al. [11] also emphasized that the flash must separate from one of the work-pieces for a sufficiently long period of time in order to generate ripples.

Figure 2.46: Flash comparisons between experimental and FEA results under different combinations of process parameters [11]

Strain rate is one of the LFW welding responses which is not amenable to direct measurement during experimental trials [2][9]. It is very important to know the strain rate distribution in the weld. This is because the material properties especially the flow stress at elevated temperature is strongly dependent on strain rate and thus, any changes on strain rate may directly affect accuracies of the modeling predictions [53].

Vairis et al. [5] [6][27] were the first researchers to directly consider the strain rate near the weld interface. They developed a first-order approximation for strain rate 휀̇ of

푎푓/퐿, where a is the amplitude, f is the frequency, and L is the length of specimen in the direction of oscillation [5]. This expression assumed that the deformation was homogeneous and a maximum strain rate of 4.6 s-1 was found at the extreme point of oscillation where the distance from the central axis equaled the amplitude of oscillation

[5][27].

Turner et al. [12] indicated that the strain rates along the weld line were substantially larger than the simple first-order estimation by Vairis et al. [5] as shown in

Figure 2.48. Their predicted strain rates were generally above the magnitude of 1000 s-1 and depended strongly upon the amplitude that was applied [12].On the other hand, these very high strain rates predicted by Turner et al. were questionable in terms of the accuracy of the model, since the extrapolation of flow stresses outside the range of the defined strain rate must occur somehow [12].

Figure 2.48: Predicted peak strain rates along weld line from models using various amplitudes but constant frequency and forging pressure [12]

The predicted peak strain rate by McAndrew et al. [18] as shown in Figure 2.18(d) correlated well on magnitudes to what had been obtained by Turner et al. [12]. They also analyzed the effect of process parameters on strain rate using the numerical models. As shown in Figure 2.49(b), the interface strain rate increased with increased average rubbing velocity. Based on this result, McAndrew et al. [18] claimed that the average interface force obtained at the equilibrium phase was relatively insensitive to the rubbing velocity. When the strain rate increased from increased average rubbing velocity (Figure 2.49(b)), it also increased the required flow stress. However, the increased average rubbing velocity at the same time would also cause an increase in interface temperature (Figure 2.49(a)) thus reducing the required flow stress. Hence, the net results appeared to be a cancellation of the two effects [2][18].

Figure 2.49: FEA results on (a) peak interface temperature and (b) peak interface strain rate with applied force of 100 kN (blue curve) and 32 kN (black curve) [18]

Weld thermal evolution and peak temperature characteristics are of particular interest to almost every researcher who was trying to model the LFW process [2]. This is primarily due to the reason that the material strength as well as overall weld quality are strongly affected by the temperature, and difficulties to directly measure the interface thermal profiles during welding [53]. As different metals may react differently on temperature profiles during LFW due to differences of deformation behavior and thermal conductivity [9], the predicted thermal profiles for LFW of Ti-6Al-4V are specially reviewed here to further help the current research work.

For LFW of Ti-6Al-4V, the generation of peak temperature and temperature gradient is a strong function of the process parameters [1][2][9]. Schroeder et al. [43] compared the predicted thermal profiles in the initial phase of LFW process produced by different energy inputs as shown in Figure 2.50. They observed that a non-uniform thermal profile was formed at the early stage of LFW process in such a way that the center portion of the interface heated faster and became hotter than the edges, which were periodically

72 exposed to the atmosphere [43]. They also found either a convex or concave thermal profile depending on the process parameters [43].

Figure 2.50: Thermal profile predictions of low (left) and high (right) energy input welds at the end of the initial phase of LFW process [43]

McAndrew et al. [26] predicted the thermal profiles at the end of the initial phase of LFW with four different process parameter combinations and their results are shown in

Figure 2.51. The predicted temperatures correlated very well with those measured experimentally using thermocouples. According to the modeling results, the temperatures recorded at the weld line ranged between 1213 K (940 °C) and 1333 K (1060 °C), with an average of approximately of 1273 K (1000 °C) across all process conditions that were employed [26]. They claimed that the predicted average interface temperature was around the β-transus temperature for Ti-6Al-4V, around which significant reduction in the flow stress would occur, facilitating extrusion of the hot metal. It further explained why the transition from initial phase to transition phase was typically associated with this temperature [2][12][26][43]. McAndrew et al. [18] also analyzed the FEA thermal histories at the end of the equilibrium phase of the LFW process with different process parameters as shown in Figure 2.52. They found that the interface temperature increases as the applied 73 force was decreased. Although less heat was introduced into the weld as the applied force decreased, the material burn-off rate was also reduced [18]. Consequently, the region farther back from the weld line was much hotter and when this hotter region reached the interface, the accumulated heat combined with the heat generated during oscillation thus resulted in a higher interface temperature [18].

Figure 2.51: Predicted temperature profiles at the end of the initial phase of LFW process for welds made with different average rubbing velocities and applied forces [26]

Figure 2.52: Predicted temperature profiles at the end of the equilibrium phase of LFW process for welds with different average rubbing velocities and applied forces [18]

Li et al. [19] simulated the thermal profiles for LFW of Ti-6Al-4V at different welding times as shown in Figure 2.53. It can be seen that the temperature at the interface was quickly increased to above 970 °C at 1 s, but the high temperature region was only around the central portion. With further increase of welding time to 3 s, although the peak temperature increase was very slow, the temperature gradient became more uniform across the interface and more flash formed [19]. At 4 s, there was no obvious increase in peak temperature, but the burn-off increased almost linearly and quickly to about 4 mm (from 2 mm at 3 s) [19].

Figure 2.53: Predicted temperature distribution at (a) welding time = 1 s, (b) 2 s, (c) 3 s, and (d) 4 s and (e) temperature profile for a monitored central element at the interface [19]

Lee et al. [16] also studied the effect of joint geometry on temperature profiles across the weld interface during LFW of Ti-6Al-4V as shown in Figure 2.54. A two- dimensional T-joint was considered with thinner upper plate and thicker bottom plate. They recorded a peak temperature of approximately 1100 °C at the interface and the temperature rapidly decreased away from the weld line. However, the predicted thermal fields were not uniform across the interface of the two work-pieces [16]. They also tracked the temperature gradient between the weld interface and the region 3 mm away from the interface in the model as shown in Figure 2.54(c). The results indicated a higher (steeper) thermal gradient in the upper plate than in the bottom plate. This explained the observed imbalance in 76 expulsion of the hot material (see Figure 2.55) where the majority of expelled material appeared to originate from the upper plate [16]. The upper plate was more likely burrowed into the bottom plate due to the non-symmetric weld geometry [16]. Therefore, hot material at the center of the weld on the bottom plate side remained at the weld interface for longer time, causing a wider thermal field. The steeper thermal gradient across the base plate thickness direction was likely due to the large mass and thus heat sink capacity of the base plate [16].

Figure 2.54: Temperature predictions for a T-joint of Ti-6Al-4V made by LFW process: (a) temperature map, (b) spatial temperature profiles across the interface for different times throughout the equilibrium phase, and (c) magnitude of the temperature gradient in either side of the interface as a function of time [16]

Figure 2.55: Predicted flash formation with temperature contours over a cycle of oscillation [16]

2.4.4 Model validation

For modeling of the LFW process, the accuracy of the output is strongly affected by the material constitutive data, boundary conditions, and necessary assumptions [9]. One should realize that a model is just an approximation of the real physical problem and thus must be critically compared to experiments to test its validity [2]. Without model validation, the results may be unreliable or even erroneous. To date, there have been many

LFW experimental results for Ti-6Al-4V used to validate the associated models [2][9][11-

13][15][18][43][54][56]. The most common ones include weld thermal histories at different distances away from the weld interface (see Figure 2.56 (a)) [18][54], the overall burn-off of the welded work-pieces as a function of process parameters or energy ratings

(see Figure 2.56 (b)) [13][43], and flash characters (see Figure 2.56(c)) [12][43]. Moreover, the sizes of HAZ and PAZ at both the center and edges of a LFW weld have been predicted and validated via microstructural observations (see Figure 2.57(a)) [11]. Development of

3-D model allows prediction of multi-direction flow behavior during LFW in which the welding defects or geometrical characters could be predicted and validated (see Figure

2.57(b)) [15]. The weld residual stress across the weld line has also been predicted and

78 validated by measured results through high energy synchrotron X-ray diffraction technique

(see Figure 2.57(c)) [2][9]. In general, the current LFW process models have been successful in capturing the experimental trends.

Figure 2.56: LFW model validations on (a) thermal profiles [18][54], (b) burn-off of work-pieces [13][43], and (c) flash shape [12][43]

Figure 2.57: Additional LFW model validations on (a) size of HAZ and PAZ [11], (b) geometrical character (3-D) [15], and (c) residual stress [2][9]

2.5 In-situ Measurement Approaches for Linear Friction Welding

The LFW technique by its nature, is a highly-transient process involving rapid heating and cooling, severe plastic deformation, as well as significant amount of dynamic recrystallization [1]. To better evaluate and understand this rapid process, advanced in-situ techniques to quantify thermal history and plastic deformation throughout the LFW process, and preferably as a function of welding process parameters would be most

80 beneficial. This information would also improve the validation of modeling approaches, as well as help further define boundary conditions [2]. To date, there is still limited in-situ experimental process data in the open literatures which includes the insertion of thermocouples for interior temperature measurement [2][5][9][11-13][18][26][43], infrared thermography (IRT) for surface temperature measurement [57][58], and high- speed camera for observation of deformation [11][43][55][58].

The application of thermocouples for temperature measurement is the most direct and efficient method to obtain the thermal profiles of a weld [2][5][9][11][12][55].

However, this method is very difficult to implement into LFW process [2][12]. The closer the thermocouples approach the welding interface, the higher the possibility that the thermocouples may be squashed, recording very noisy data or losing data. Turner et al.

[12] developed a relatively secured method to embed thermocouples as shown in Figure

2.58. Sixteen holes were spark-eroded axially into each work-piece with anticipated depths of 1, 2, 4 and 8 mm from the weld interface (four holes cut to each depth). This approach allowed four K-Type thermocouples to be located at each different hole depth, and was considered to be sufficient to record the thermal data needed, whilst eliminating any systematic errors [12]. Prior to welding, the thermocouples were sealed into their positions in the holes with a chemical-set thermal cement or epoxy resin. According to This method was able to obtain decent welding thermal profiles to validate the modeling results as shown in Figure 2.59. Schroder et al. [11][43] applied the same method from Turner et al. to insert thermocouples. They were able to obtain a peak temperature that was above 1200

°C at a distance of 1 mm away from the interface for a high energy LFW weld (see Figure

2.36).

Figure 2.58: LFW of Ti-6Al-4V with thermocouple wires attached by thermal cement (left) and epoxy resin (right) [12]

Figure 2.59: Comparisons for thermocouple measured thermal profiles and those predicted by FEA model. A melting temperature of 1660 °C was assumed to determine the homologous temperature [12][55]

McAndrew et al. [2][13][18][26] applied slightly different method from that used by Turner et al. [12] to place the thermocouples as shown in Figure 2.60. For each work- piece, four holes were drilled by drill bites with a diameter of 1.2 mm perpendicularly to the oscillation direction and parallel to the direction of the applied force. The holes had distances of 0.3 mm, 1 mm, 2.5 mm, and 4.5 mm from the weld interface respectively, and plugs were placed at the bottom of each hole as thermocouple receivers. The thermocouple wire was then inserted through the opposite end till complete contact was made with the plug and fixed at the position by an epoxy resin [13][18][26]. Based on the above setup,

McAndrew et al. [18] was able to measure the temperature profiles for all phases of the

LFW process as shown in Figure 2.61. It could be seen that all the thermocouples were embedded well with no bad signals (interference of interface motion), and a peak temperature of 1000 °C was obtained during phase 3 (equilibrium phase) of the LFW process.

Figure 2.60: Work-piece dimensions and locations of thermocouples in a sectioned plane [18]

Figure 2.61: Comparison of measured and predicted thermal histories for a LFW weld made by 20 Hz frequency, 1.5 mm amplitude, 100 kN applied force and 3 mm upset [18]

Although thermocouples have been widely used in many LFW experiments, they still suffer from the following limitations [2][57]. First, it is in general not easy to drill deep holes in hard materials. Second, the placement of the thermocouples may alter the heat flow. Third, the thermocouples may not be effectively functioning when getting very close or by-pass the welding interface [57]. Lastly, the slow (delayed) response time of 85 thermocouples can prevent from accurately tracking the rapid transient temperature rise during LFW [2].

On the contrary, the temperature evaluation based on radiation (such as infrared thermography) can be performed in non-contact way for field measurement and is able to eliminate most of the issues of thermocouples [57][58]. This technique is based on the fact that all bodies, at a temperature above absolute zero (T > 0 K), emit certain amount of infrared radiation (IR) and the intensity of the infrared radiation is a function of temperature. The IR intensity is detected by the infrared detectors and transformed into a visible image such as a surface temperature map as shown in Figure 2.62 [59].

Figure 2.62: Schematics of infrared measurement procedure [59]

According to Miao et al. [57] the infrared thermography is probably the most suitable approach for LFW applications allowing high temperature to be captured easily and quickly without any direct contact with hot parts. They conducted the first infrared experimental study on LFW of Ti-6Al-4V. A FLIR C6580sc infrared camera was positioned at a distance of 25 cm in front of two work-pieces. This infrared camera had a frame rate of 355 Hz with full resolution of 640 × 512 pixels, and a filter lens was equipped to capture temperature above 300 °C and below 1500 °C. One of the thermal images taken by the infrared camera was shown in Figure 2.63, and based on the number of pixels across

86 the weld interface, the temperature profile as a function of positions was also plotted. A peak temperature approaching 1100 °C at the center of the welding zone was obtained [57].

Yang et al. [58] observed the interface thermal profile during LFW of Ni-based superalloy

GH4169 also by an infrared camera (no specifications of camera brand and setup were provided) as shown in Figure 2.64. They found the maximum, minimum, and average temperatures along path AB were 1161.5 °C, 729 °C, and 1032.8 °C respectively at the end of the LFW process (peak temperature was not located at the center of the interface) [58].

Figure 2.63: Thermal image captured by the infrared camera with plot of temperature as a function of pixels across the weld interface in the ROI (region of interest) [57]

In order to in-situ monitor the deformation behavior during LFW process, one of the best methods to date is high-speed camera [55]. While high-speed videos of the entire

LFW process can be found in video repositories such as YouTube, high-quality videos for detailed analysis of deformation during LFW are still limited [11][43][55][58]. Schroeder et al. [11] was the first authors who implemented the high-speed photography method into

LFW process of Ti-6Al-4V to observe the material extrusion and flash formation as shown in Figure 2.65. They reported that the flash in the transverse direction originated at the center and spread to edges [11][43]. These high-speed images were also compared to the modeled predictions for the flash formation. As illustrated in Figure 2.66, the modeling results correlated well with the in-situ monitored results. However, Schroder et al. [11] did

89 not discuss any information about camera settings (brand, lens, distance, and frame rates), which were all very important factors affecting the image quality.

Figure 2.65: Video images illustrating formation of flash in both oscillation and transverse directions [11]

Figure 2.66: Comparison of ripple formation in one cycle of LFW process by high-speed photography (left) and model predictions (right). Sheared material was indicated by arrows [11]

Another method that is increasingly developed over recent years for in-situ monitoring the deformation histories of materials is the digital image correlation (DIC) method [60][61][62]. It is a non-contact method that has been extensively used for displacement and strain field measurement in various applications such as material 90 characterization, structural health monitoring, fatigue cracking growth, and high temperature mechanical tests [60]. It uses image registration algorithms to track the relative displacements of material points between a reference image (typically un-deformed) and the current image (typically deformed) [60][61][62].

Prior to use DIC, a random or speckle pattern is often created by painting a thin layer of white paint across the surface of the sample followed by an airbrushed black paint

[62]. A series of images of the speckle pattern will be taken before and after the deformation has occurred. To analyze these images, the reference image will be partitioned into subsets that are small enough in which the deformation is uniform within each subset, and then each subset will be tracked from the reference image to the sequential images

[60][61]. In Ncorr (an open source 2-D DIC MATLAB software) for example, the subsets, as shown in Figure 2.67, are originally circular groups of points with integer pixel locations in the reference image [60][62]. As the material is deforming, the transformation of the subset coordinates is expressed in such a way that they can be taken from the reference image and parameterized by displacements and their derivatives to correspond with the current image of interest. Detailed description of the equations and algorithms can be found in the publication by Blaber et al. [60].

Figure 2.67: Basic steps of the DIC algorithm implemented in NCorr using an initial guess and iterative optimization scheme to find a refined solution [60]

To author’s best knowledge, DIC has not yet been used in LFW processes. A main challenge is the severe plastic deformation (flash formation) during LFW process that will deteriorate or destroy the speckle patterns. Nevertheless, as to be reported in Chapter 5, the results obtained by DIC are still useful to validate the process model, especially for the conditioning stage.

2.6 Mechanical Testing of Ti-6Al-4V

As briefly discussed in section 2.4.3, the material property data, especially the flow stress, are crucial to the accuracy of the numerical simulation. The flow stress is that necessary to deform a metal plastically [63]. For a given microstructure, the flow stress 휎 is a function of strain 휀, strain rate 휀̇, and temperature 푇 as represented in Equation 2.17:

휎 = 푓(푇, 휀̇, 휀) (2.17)

During hot forming of metals (at temperature above approximately one-half of the melting temperature), the effect of strain on flow stress is often small, and the influence of strain rate (rate of deformation) becomes increasingly important. Conversely, during cold forming at room temperature, the effect of strain rate on flow stress is usually small [63].

To obtain the flow stress of Ti-6Al-4V for the strain, strain rate, and temperature conditions relevant to the LFW process, the most commonly used mechanical tests are the tension, uniform compression, and torsion tests [53][63]. There are two methods of representing flow stress from the tension test as shown in Figure 2.68 [63]. In classical engineering (see Figure 2.68(a)), the engineering stress S is obtained by dividing the instantaneous tensile load L by the original cross-sectional area of a specimen A0. The engineering stress S can then be plotted against the engineering strain 푒 = (l-l0)/l0. When deformation occurs, the specimen initially elongates in a uniform fashion and necking starts when the load reaches the maximum. The deformation is then concentrated in the necked region while the rest of the specimen undergoes very limited deformation [63]. Figure

2.68(b) illustrates the true stress-strain representation of the same tension test data, and before necking, the true stress and strain can be calculated in Equation 2.18 and Equation

2.19:

퐿 (2.18) 휎(푡푟푢푒 푠푡푟푒푠푠) = = 푆(1 + 푒) 퐴

퐴 (2.19) 휀(푡푟푢푒 푠푡푟푎𝑖푛) = ln ( 0) = ln (1 + 푒) 퐴

According to Bridgman et al. [64], the determination of flow stress from the tension test after necking (Figure 2.68(b)) requires a correction due to the existence of a triaxial state of stress. For a round bar testing specimen, the correction derived by Bridgman et al. [64] is shown in Equation 2.20:

퐿 2푅 푟 −1 (2.20) 휎 = [(1 + ) ln (1 + )] 휋푟2 푟 2푅

where the quantities r and R are defined in Figure 2.68(d). The values of r and R must be measured continuously during the tension test for an accurate evaluation of the flow stress

[64]. However, the tension test in general is less frequently used to determine the large- strain flow stress of metals due to the occurrence of necking at relatively small strains

(typically less than 20% true strain prior to failure) [63]. Instead, the tension test is found to be more applicable for the modeling of the sheet forming process at ambient temperature or superplastic forming operations at elevated temperatures [63].

The compression test can be used to determine flow stress data for metals over a wider range of strains (capable of up to 70% true strain) and strain rates (magnitude of 10 s-1) than the tension test [65][66]. A cylindrical (bar or ring) sample is typically machined as the standard testing specimen. To be applicable without corrections or errors, the cylindrical sample must be upset without any barreling in order for maintaining the state of uniform stress [65]. Barreling can be prevented by using adequate lubrication such as melted glass for titanium alloys. To hold the lubricant, spiral grooves are often machined on both the flat surfaces of cylindrical test specimens [63][65][66], as shown in Figure

2.69. For frictionless and perfectly uniform compression, the average pressure-axial stress curve is equivalent to the flow curve, and the true strain 휀, stress 휎 and strain rate 휀 ̇ can be expressed as Equations 2.21, 2.22, and 2.23 :

Figure 2.69: Compression test specimen. (a) schematics of specimen showing lubricated shallow grooves on ends and (b) shape of the specimen before and after the test [63]

ℎ 퐴 (2.21) 휀 = ln ( 0) = ln ( ) ℎ 퐴0

퐿 (2.22) 휎 = 퐴

푣 휀̇ = (2.23) ℎ where v is instantaneous crosshead speed, h0 and h are the initial and instantaneous sample heights respectively, and A0 and A are initial and instantaneous cross-sectional areas respectively. In general, the compression test can be conducted without barreling to about

50% height reduction (휀 = 0.69) or more [63]. Hot compression test is typically conducted in a furnace or using preheated fixture where the test machine provides a constant true strain rate (a constant ratio of the cross-head speed to the instantaneous sample height) such as programmable servohydraulic testing machines or cam plastometers [65][66].

The torsion test can obtain the flow behavior of a metal at even higher strains (in excess of 150%) and strain rates (magnitude of the order of 100 s-1), and can also avoid the 96 complications associated with necking in tension test and barreling in compression test.

Thus, it is often applied when stress-strain curves must be known for bulk-forming operations such as extrusion or radial forging [63][67]. In the torsion test, the standard testing specimens are typically a hollow tube or a solid round bar with machined gage sections that have reduced thickness as shown in Figure 2.70 [67]. During the test, the specimen will be twisted at a constant rotational speed, in which the torque M and the angle of rotation 휃 (in radians) are measured. For a tubular specimen, the average shear stress 휏 and shear strain 훾 in the gage section can be expressed as Equations 2.24 and 2.25 [63][67]:

Figure 2.70: Standard specimen geometry for torsion test [67]

푀 (2.24) 휏 = 2휋푟2푡

푟휃 (2.25) 훾 = 푙

Where r is the internal radius, t is the gage wall thickness, and 푙 is the gage length. For a solid bar with radius R and gage length 푙, the shear stress 휏 and shear strain 훾 at the outer surface of the specimen are given by the following relation as shown in Equations 2.26 and

2.27 :

(3 + 푛∗ + 푚∗)푀 (2.26) 휏 = 2휋푅3

푅휃 (2.27) 훾 = 푙

According to Semiatin et al. and Altan et al. [63], the 푛∗ and 푚∗ are the strain-hardening exponent and the strain rate sensitivity exponent based on the instantaneous slopes of log푀

-log휃 curve and log푀 -log휃̇ curve respectively. If the material is assumed to be isotropic, the shear stress and shear strain results from the torsion test can be converted to those true stress and true strain using the following relations derived from the von Mises yield criterion as Equation 2.28 and 2.29 [67]:

휎 = √3휏 (2.28) 훾 휀 = (2.29) √3

When metal is under plastic deformation, heat is typically generated due to formation of dislocations (metallurgical defects). This deformation heating accounts for

90-95% of the mechanical work, which often associates with a local temperature rise depending on the particular size of the specimen [66]. Semiatin et al. [63] illustrated that a measurable fraction of heat is retained in the testing specimen for strain rates greater than

~0.01/s under isothermal compression tests of cylinders with 12 mm diameter and 18 mm height. Hence, there is a need to correct the flow stress data for the temperature rise due to deformation heating at different strain rate. The estimation of the temperature rise due to deformation heating can be calculated from the following relation:

0.9휂 ∫ 휎푑휀 (2.30) ∆푇 ≅ 휌푑푐 where 휂 is the adiabatic fraction of the deformation heat retained in the test specimen, c is the specific heat, 휌푑 is the material density, and the integral is the area under the un- corrected true stress and strain curve from zero strain to the strain where temperature rise is to be calculated [63]. The adiabatic fraction for hot compression test of a small titanium aluminides (TiAl) cylinder is calculated as 0.5, 0.9, and 1 for strain rates of 0.1/s, 1/s, and

10/s respectively. The detailed calculations can be found in references [65] and [67].

In order to successfully and efficiently conduct compression and torsion tests for

Ti-6Al-4V to obtain sufficient flow stress data at various strains, strain rates and elevated temperatures, one such system known as the Gleeble® (manufactured by Dynamic System,

Inc.) is often employed. This is primarily due to the reason that the standard (conventional) testing methods (e.g. testing methods described by ASTM) for generation of flow stress data are generally costly and inefficient [68]. The Gleeble® is a thermo-mechanical simulator and is capable of replicating various thermo-mechanical conditions experienced by metallic materials in processing and fabrication [69]. The Gleeble® was originally developed for the simulation of fusion weld HAZ, but has expanded capability over the years including integrated digital control loops with high resolution [70]. Electrically conductive samples are heated by resistive heating while temperature is controlled by a feedback loop monitoring by either thermocouples or an infrared pyrometer [68][69][70].

A mechanical system consisting of a hydraulic piston attached to one end of the testing samples allows both tension (maximum of 20 tons) and compression (maximum of 10 tons) operations [68][69][70]. The most recent addition to the Gleeble® is the torsion mobile 99 conversion unit (MCU) [68]. The torsion MCU adds a hydraulic motor to the system while maintaining the tension/compression and thermal parts of the system. A torque cell provides accurate torque measurements during hot torsion tests, and an integrated optical pyrometer provides thermal feedback, allowing for rotation of the sample, which would not be possible with thermocouples [68]. The newest version of the Gleeble® system is shown in Figure 2.71.

Figure 2.71: Pictures of (a) Gleeble® 3800 system setup and (b) torsion MCU [68]

2.7 Summary of Important Technical Gaps

Achieving structurally sound and defect free LFW joints of Ti-6Al-4V requires the understanding of process parameters, heat generation due to friction sliding and deformation, and plastic flow of material during welding. The existing numerical modeling of the LFW process has provided significant insights into the various physical behaviors as well as welding responses during the joining process. Due to the complexity of the LFW process, assumptions and simplifications are often used to make the simulation work

100 tractable, and many important technical knowledge still need further development. The following is a summary of important knowledge gaps that are addressed as key motivations in the present research after the detailed review of other researchers’ works on simulation of the LFW process for Ti-6Al-4V.

1. 2-D models remain important for studying the LFW process due to their ability to

provide adequate descriptions of temperature and deformation transients at short

computational time. However, nearly all the 2-D LFW models in literatures were

based on a simulation domain that was parallel to the oscillation direction. Hence,

the plastic deformation and heat transfer perpendicular (transverse) to the

oscillation direction during LFW were widely unknown. Although these aspects

can potentially be covered by developing 3-D models of the LFW process,

extremely long simulation time is generally needed and the accuracy of the

modeling results are often questionable due to coarse mesh size and poor solution

convergence.

2. The two-staged modeling approach, which involves a conditioning stage model

followed by an equilibrium/merged stage model, has been successfully used to

account for the physical processes especially the heat generation taken place during

LFW. Appropriate definition of the interface friction coefficient in the conditioning

stage remains a challenge. Some researchers assumed linear or constant friction

coefficient, some others calculated the values based on Coulomb’s friction law via

machine recorded data, and a few researchers directly prescribed a heat flux across

the interface as substitution for the heat generation by friction. Consequently, many

101

conditioning stage models were either purely thermal or assumed to be perfectly

elastic, where the reciprocating motion and plastic deformation during the initial

and transition phases of the LFW process were not considered.

3. The determination of the transition time at which the conditioning stage simulation

stops and the equilibrium/merged model begins is very important for the accuracy

of the modeling results. A popular option was to set this threshold time at the

instance when the machine recorded burn-off (upset) rate reached constant.

However, interruption test showed that it was not always true as the interface region

was not yet bonded completely. Another option was to determine the threshold time

based on the time when the interface reached certain peak temperature. It is not well

understood how the threshold time would change when the work-piece geometry

or process parameters are changed.

4. Generally, there is a fair amount of flow stress data available in literature for

structural metals and alloys such as Ti-6Al-4V which usage is ubiquitous in various

industries (particularly aerospace). However, this data primarily exists in the

temperature and strain rate regimes representative of conventional thermo-

mechanical processing conditions (approximately up to 1000 °C and 0.0001 s-1 –

10 s-1). The temperatures experienced by Ti-6Al-4V during LFW with the chosen

process parameters are well above this range; for example, the estimated peak strain

rate during LFW by some FEM simulations was up to ~1000 s-1. As such, there is

a clear need for flow stress data in the range of 1000 – 1200 °C and at least up to

102

100 s-1 in order to develop the material database information that will provide

greater fidelity and quantitative accuracy to FEM simulations.

5. For Gleeble® hot compression and torsion tests of Ti-6Al-4V, the specimen is first

resistively heated to the programmed temperature and then compressed or twisted

at different deformation rates to obtain flow stress data. However, the resistive

heating method typically results in a temperature gradient across the testing

specimen. Thus, the effect of such temperature non-uniformity on the flow stress

relationship is not clear. On the other hand, the elevated temperature torsion testing

is not fully standardized even when utilizing conventional load frames. Hence, there

is a need to design a new specimen geometry for Gleeble® unit so that the thermal

and strain homogeneity are maximized in the gauge section.

6. There is in general limited in-situ experimental data of thermal and deformation

transients. Other than thermocouples, the high-speed imaging, infrared

measurement, and digital image correlation (DIC) are not widely used. Successful

implementation of these in-situ measurement techniques can provide the high-

quality data that are crucial for development and validation of the numerical models

for LFW of Ti-6Al-4V.

7. In literatures, most cases for experimental and modeling of the LFW process have

focused on joining two identically shaped Ti-6Al-4V work-pieces. These efforts

are more or less targeted on BLISK manufacturing. Over recent decade, there is

increasing demand by the aerospace industry for applying the LFW process as a

manufacturing method to join the net-shape titanium pre-forms. In those cases, non-

103 symmetrical welds (e.g. T-shape joints) are often involved. However, the effects of such geometry change on material flow and thermal fields during LFW are currently not well studied. There is a significant lack of data from both experimental and modeling efforts, which clearly needs to be addressed.

104

Chapter 3 Mechanical Testing and Modeling of Ti-6Al-4V

As discussed in Section 1.4, high-temperature mechanical tests of Ti-6Al-4V including hot compression and torsion in Gleeble® were performed by Boeing Research

& Technology to generate the flow stress data necessary for FEA modeling of the LFW process. Tension testing in general is less frequently used due to the fact that much less strain (less than 20% true strain) can be achieved prior to failure (necking) during tensile loading. As summarized in Section 2.7, there exists a temperature gradient in the specimen along the longitudinal direction due to the use of water-cooled grips for resistance heating in Gleeble. Such temperature gradient can affect the accuracy of the tested flow stress data.

In this chapter, to improve the testing accuracy, thermal-electrical-mechanical simulations for the hot compression and torsion tests were developed based on ABAQUS®, a commercially available finite element code. In particular, the influence of non-isothermal heating on flow stress variations during hot compression test, and optimization of specimen design for thermal and deformation uniformity during hot torsion test were studied.

3.1 Material

The material of interest in the present research is the α + β titanium alloy Ti-6Al-

4V in a mill annealed condition. Such microstructure offers a combination of high strength,

105 durability, corrosion resistance, and high temperature performance. The chemistry of the material, as shown in Table 3.1, is verified by optical emission spectroscopy and inert gas fusion. Specimens of as-received base metal were prepared via typical metallographic techniques for titanium alloys and analyzed by the scanning electron microscopy (SEM).

Figure 3.1 shows the base metal microstructure consisted of equiaxed primary α and elongated secondary α laths.

Table 3.1: Chemical composition of Ti-6Al-4V base metal (wt%)

Ti Al V Fe O C N H bal. 5.94 3.88 0.235 0.18 0.055 0.009 0.003

Figure 3.1: Microstructure of mill annealed Ti-6Al-4V specimen via scanning electron microscopy (left) and zoomed-in view (right) (images courtesy of CEMAS)

3.2 Thermo-Mechanical Simulation

A Gleeble 3500® system in Boeing Research & Technology (St. Louis, MO) was utilized to perform the elevated-temperature compression and torsion testing of Ti-6Al-4V.

As discussed in Section 2.7, the tested temperature range was focused on 1050 – 1200 °C, where the flow stress data is mostly limited in the open literatures. The highest strain rate 106 that could be reliably applied in the Gleeble hot compression test is limited to ~10 s-1 due to equipment limitation of the load cell and displacement transducer. On the other hand, the Gleeble® hot torsion is capable of capturing the strain rate up to ~10 s-1. Hence, the peak strain rates programmed for compression and torsion tests were 10 s-1 and 100 s-1, respectively. Table 3.2 displays the full testing matrix.

Table 3.2: Gleeble® hot compression and torsion test matrix

Strain Rate 0.001 s-1 0.01 s-1 0.10 s-1 1.0 s-1 10 s-1 100 s-1

1050C C C C C C & T T 1100C C C C C C & T T 1150C C C C C C & T T

Temperature 1200C C C C C C & T T *C=Compression, T=Torsion

For the hot compression test, solid cylindrical specimens were machined from the

Ti-6Al-4V plate. The specimen had a diameter of 10 mm and a length of 15 mm as shown in Figure 3.2 to help improve temperature uniformity and avoid unnecessary barreling.

During the test, the specimens were heated in vacuum to the testing temperatures described in Table 3.2 with a heating rate of 5 °C/s, followed by a five-minute-long soaking at the testing temperatures. Immediately after soaking, each specimen was compressed by a reduction of 50% to its original length resulting in about 70% true strain under different deformation rates (up to 10 s-1). The compressed specimen was then “normally” cooled in

Gleeble® with a cooling rate around 20 °C/s to room temperature. Figure 3.3 illustrates the programmed heating, soaking, and compressing steps in Gleeble® for one specimen at

107 testing temperature of 1050 °C. It should be noted that there could be active current flow across the specimen during the compressing step to maintain specimen temperature. Hence, the obtained stress and strain data were corrected according to the methods described by

Semiatin and Altan [63] to account for the temperature rises due to adiabatic heating especially at relatively higher strain rates of 1 s-1 and 10 s-1.

Figure 3.2: Geometry of specimen for Gleeble® hot compression test

Figure 3.3: Gleeble® hot compression test conditions (left); Zoomed-in view of compression step (right)

For the hot torsion test, a hollow cylinder specimen with short gauge section (5 mm in length) and reduced gauge wall thickness was developed as shown in Figure 3.4. The development was based on the modeling results from several torsion specimen designs with different gauge length and gauge wall thickness in ABAQUS®. The detailed modeling 108 procedures and results are discussed later in Sections 3.4 and 3.5. During hot torsion testing, each specimen was heated in vacuum at a heating rate of 5 °C/s to the specified test temperature (see Table 3.2), and followed by one-minute-long soaking at the testing temperature. Single twist with a rotation angle of 1.46 radians was applied for all testing specimens after soaking. It should be noted that there was no active current flow at the start of the twisting step (due to equipment limitation of the torsion MCU at Boeing’s laboratory). Hence, the heating mainly relied on the adiabatic heating during twisting to maintain specimen temperature. Once the twisting was completed, each specimen normally cooled down to room temperature. Finally, the obtained torsional flow stress curves were produced by converting the torque-rotational angle data from Gleeble® to shear stress- strain curves taken into account the specimen dimensions. The shear stress-strain relationship was then converted to effective stress-strain curves via methods described by

Semiatin and Jonas [67].

109

Figure 3.4: (a) Final geometry of Gleeble® hot torsion specimen designed based on FEA modeling, and (b) machined torsion specimen according to the design in (a)

3.3 Hot Compression Modeling Setup

The resistive heating method in Gleeble® typically results in a temperature gradient across the testing specimen. Although Ti-6Al-4V specimens were machined in the form of short cylinders to improve temperature uniformity, some temperature gradient was still encountered, as shown in Figure 3.5. The thermal profiles recorded by surface thermocouples at the mid-point and edge of the specimen showed an appreciable temperature difference of approximately 50 °C.

110

Figure 3.5: Experimentally measured thermal profiles at the specimen center and edge during heating and soaking steps in Gleeble®

To understand the effect of such temperature non-uniformity on the flow stress data, a 3-D model of hot compression test developed in ABAQUS® was applied to simulate the following two cases. One case was an “idealized” isothermal model, for which the entire specimen was heated uniformly and soaked at the testing temperature without any thermal gradient. The other case represented a “real” non-isothermal model simulating the actual testing conditions in Gleeble®. The non-isothermal model considered resistive heating for the on-heating step and soaking step at the testing temperature. Taking advantage of symmetry, only a quarter of the original cylinder was considered as shown in Figure 3.6.

The material property data for these two models were taken from the pedigreed sources in literatures [12][53], including density, thermal conductivity, inelastic heating fraction, specific heat, thermal expansion, Young’s modulus, Poisson’s ratio, electrical conductivity, heat transfer coefficient, and flow stress as functions of temperature, strain and strain rate.

111

Figure 3.6: Meshed compression model in ABAQUS® showing a quarter of the original specimen geometry

The testing condition as illustrated in Figure 3.3 was employed for both models under a strain rate of 0.1 s-1. Specifically, the procedures included 204 seconds of ramping time to the target temperature (1050 °C) with a heating rate of 5 °C/s, soaking at the peak temperature 1050 °C for 300 seconds, compressing with a stroke rate of 0.9375 mm/s for

8 seconds (equivalent to a global strain rate of 0.1 s-1), and cooling for 20 seconds.

3.4 Hot Torsion Modeling Setup

The elevated-temperature torsion testing has not been fully standardized and both hollow and solid cylindrical specimens with various gauge lengths and thicknesses are often employed [67]. Hence, a new specimen design is necessary for improving temperature and deformation uniformity for testing in the Gleeble® torsion unit. Norton et al. [68] developed a specimen geometry consisted of a hollow tubular geometry with reduced thickness on specimen gauge section in order to control both temperature

112 uniformity and cooling rate during Gleeble® hot torsion test. An example of the torsion specimen geometry and setup in Gleeble® is shown in Figure 3.7.

Figure 3.7: Prior hollow tubular torsion specimen by Norton et al. and setup in Gleeble® torsion MCU at OSU [68]

The gauge section geometry particularly the gauge length and wall thickness of the torsion specimen have important effects on the temperature and deformation uniformity due to resistive heating in Gleeble®. Therefore, based on the design in Figure 3.7, a new

Gleeble® hot torsion specimen geometry was developed based on finite element modeling in ABAQUS® to produce reliable shear stress-strain data. It should be noted that this optimization of the torsion specimen geometry was done prior to any actual Gleeble® testing. Thus, assumptions were needed on the heating, twisting, and cooling conditions that were applied to the models.

In general, two groups of 3-D torsion models were developed in ABAQUS® as shown in Figure 3.8 with different lengths of the gauge section. For each group, two cases were modeled that one case had thin gauge wall thickness (1.45 mm), while the other case 113 had relative thick gauge wall thickness (3.05 mm). Hence, a total of four torsion models were studied numerically. The material property data for all four torsion models were the same as those used in the hot compression models for Ti-6Al-4V.

All four torsion models considered resistive heating to a peak temperature around

1150 °C with a heating rate of 5 °C/s and soaking at this peak temperature for 10 seconds.

After soaking, each model was twisted in such a way that one end of the specimen was constrained and the other end was revolved around the Y-axis (see Figure 3.8) with a twisting angle of 0.7 radians in 1 second. Finally, each model was cooled to room temperature with a cooling rate of approximately 20 °C/s. From the simulation results, shear strain and temperature gradient at the gauge section were particularly compared. The geometry from the model that had the most uniform properties was selected and further adjusted (gauge section thickness and shoulder section length) to fit into the specific

Gleeble® torsion unit at Boeing’s facility.

114

Figure 3.8: Baseline Gleeble® hot torsion models with short gage section (left) and long gage section (right)

3.5 Results and Discussion

3.5.1 Thermo-mechanical testing

Figure 3.9 shows the overall flow stress (true stress-strain) curves from the extensive Gleeble® hot compression testing. The current Ti-6Al-4V clearly exhibits strain rate hardening, i.e., an increase in strain rate results in higher flow stress at given plastic strain. It should also be noted that as temperature increases, the effect of the strain rate sensitivity appears to diminish slightly at relative smaller strain rates (< 1 s-1). In addition, for a given strain rate, the obtained flow curves do not exhibit much work or flow hardening and their behaviors are similar to perfect-plasticity. In fact, there is some evidence of flow softening behavior at strain levels beyond 0.5 for testing at 1050 and 1100 °C.

115

Figure 3.10 compares the flow curves from the present Gleeble® hot compression tests with the literature data [53] for the same testing conditions and similar initial microstructure of Ti-6Al-4V. The literature data was conducted on a conventional load frame compression system with nominally isothermal conditions produced by induction heating. It can be seen that there is good agreement between the two data sets in general.

However, at higher strain rates (1 – 10 s-1), there is some disparity which can be attributed to several factors such as slight variations in the initial material state and soaking time at the peak temperature levels.

Figure 3.9: True stress-strain curves (flow stress) generated by Gleeble® compression testing of Ti-6Al-4V (testing data courtesy of Boeing Research & Technology)

116

Figure 3.10: True stress-strain curves (flow stress) at 1050 °C generated by Gleeble® compression testing of Ti-6Al-4V compared to the results from Seshacharyulu et al. [53] (red markers)

Figure 3.11 displays the torsional flow stress curves generated from Gleeble® hot torsion testing. At the highest strain rate (100 s-1), it can be seen that there was a fair amount of noise. This is likely an effect of mechanical ringing of the Gleeble® torque cell at extremely fast twisting rate, which is typical for mechanical testing that involves high deformation rate. There are some discrepancies when comparing the results of the flow behavior between the compression and torsion modes of loading at the same temperature and strain rate conditions as shown in Figure 3.12. According to Semiatin et al. and Jonas et al. [67], the stress obtained in torsion often lies below that obtained by axisymmetric methods (e.g., compression). The differences are usually a consequence of one or a combination of several factors, such as the effect of different deformation paths inside

117 materials between torsion and compression operations on the development of crystallographic texture, and on the nature of the microscopic slip or twining processes that control the observed macroscopic strain hardening rate [67].

Figure 3.11: True stress-strain curves (flow stress) generated by Gleeble® torsion testing of Ti-6Al-4V (testing data courtesy of Boeing Research & Technology)

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Figure 3.12: True stress-strain curves (flow stress) generated by Gleeble® compression and torsion testing of Ti-6Al-4V at the same temperature and strain rate conditions

3.5.2 Modeling results of hot compression test

The simulated results for Gleeble® hot compression testing of “idealized” and

“real” conditions are shown in Figure 3.13 and Figure 3.14 respectively. The temperature in the idealized isothermal model is uniform, as what should be expected. The non- isothermal model correctly captures the temperature gradient from the specimen mid-point to the edge along its length of approximately 50 °C. Moreover, the non-isothermal model predicts some more barreling when compares to the specimen shape obtained in the isothermal model, which is also expected since the specimen mid-point that experienced the highest temperature would deform more than the specimen edge that is cooler.

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Figure 3.13: Temperature contours for non-isothermal model at the end of (a) heating step, and (b) compression step, and isothermal model at the end of (c) heating step, and (d) compression step. Temperature plotted in C

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Figure 3.14: Predicted surface temperature profiles at the specimen mid-point and edge for (a) non-isothermal model, and (b) isothermal model

The analysis of the hot compression modeling results is as follows. For each model, the predicted load and displacement results were extracted and converted into engineering stress and strain, and then to true stress and strain data via Equation 2.21 and Equation

2.22. As shown in Figure 3.15, the isothermal model yields lower true stress as well as more linear profile. In other words, the actual flow stress would be slightly lower than that determined from a Gleeble® compression test (non-isothermal condition). The difference of the true stress produced by the two models is on average 2 MPa (about 7% - 8%) for the temperature of 1050 °C and strain rate of 0.1 s-1. In other words, the temperature gradient for this case had relatively small effect on the obtained flow stress data by Gleeble® hot compression test. Hence, it is assumed that the hot compression tested flow stress data

(Figure 3.9) would not require a correction for temperature gradient along the sample 121 length direction. Due to the time constraint, the same modeling analysis for higher peak temperature and strain rate conditions has not yet conducted at the time of writing this dissertation. Further investigations are recommended to quantify the effect of temperature gradient on tested flow stress for other temperatures and strain rates.

Figure 3.15: Model output of force and displacement data (left) and converted true stress and true strain data (right)

3.5.3 Modeling results of hot torsion test

The calculated temperature distributions in the four different geometries (described in section 3.4) prior to twisting are shown in Figure 3.16. The specimen geometry with short gauge length and thinnest gauge wall (Figure 3.16(c)) produces the most desired results in terms of having the most uniform temperature distribution that covers the entire gage section, while having limited overheating at the specimen shoulder sections at the same time. Therefore, this geometry is down-selected for further modeling investigation described next.

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Adjustments were further made to the specimen geometry shown in Figure 3.16(c) to fit in the Gleeble® torsion unit at the Boeing Research & Technology facility.

Specifically, the length of the shoulder sections was increased and the wall thickness of the gauge section was further reduced. The specimens with and without adjustments are also modeled in ABAQUS®, and the results are shown in Figure 3.17. The specimen with adjustments for the Boeing tooling shows very similar temperature and shear strain distributions at the gauge section to those without any adjustments. In a more detailed and quantitative analysis, Figure 3.18 shows the through-thickness distributions, from which a high uniformity of temperature and shear strain is observed for both two specimen designs.

Hence, it is deemed that the specimen geometry with adjustments would perform as well as that without adjustments, and the final drawing of the torsion geometry is thus designed as shown in Figure 3.4(a).

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Figure 3.17: Predicted temperature and shear stress distributions for (a) and (c) adjusted geometry; for (b) and (d) baseline designs (i.e., the one shown in Figure 3.16(c))

Figure 3.18: Through-thickness distributions of temperature (left) and shear strain (right) for specimens with and without adjustments

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3.6 Summary

Thermo-electrical-mechanical simulations have been developed to improve the accuracy of flow stress data tested at high temperatures and strain rates. In particular, the simulation results showed that for a short length compression specimen (e.g. 15 mm in length), the temperature gradient existed along the specimen longitudinal direction had a marginal effect on the flow stress data. For torsion test, a short gauge with thin wall geometry was designed to improve temperature and deformation uniformity. The elevated temperature compression and torsion testing were conducted by the research partner to generate a series of flow stress data for Ti-6Al-4V. These flow stress data are integrated into the existing DEFORM® database along with other lower temperature (sub -solvus) flow stress data for Ti-6Al-4V available in the literatures to model the LFW processes in

Chapters 5 and 6. Together, this database represents one of the most completed ones for

Ti-6Al-4V, covering the extremes of the temperature and strain rate window applicable for solid-state welding processes.

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Chapter 4 Linear Friction Welding and Modeling of Sub- Scaled Ti-6Al-4V Coupons

In this chapter, a series of sub-scaled Ti-6Al-4V coupons with one square inch of weld area were linear friction welded together based on different energy inputs. In-situ measured thermal profiles via embedded thermocouples and deformation profiles via high- speed photography were conducted during welding. Computationally efficient 2.5-D transverse process models were developed to simulate these experimental welds.

Developed models were validated through thermal profiles, flash shapes, and material burn-off and burn-off rate. All the process models and modeling approaches in this chapter were performed in DEFORM®, a commercially available finite element code. As to be shown later, the critical gaps identified in chapter 2 are addressed in both experimental and modeling aspects for LFW of Ti-6Al-4V.

As described previously, the sub-scaled coupon welding experiments were performed by EWI. Boeing Research & Technology supplied materials and processing parameters. Thermocouple measurements and post-weld metallographic examination were performed in collaboration with OSU’s CEMAS.

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4.1 Design of Experiments

Twenty-four identical Ti-6Al-4V coupons with specific dimensions shown in

Figure 4.1 were machined and used to make a total of twelve linear friction welds. The weld surface area was approximately one square inch. The material’s composition and initial microstructure were introduced in section 3.1. Of those twelve welds, the first six were completed to verify the machine’s ability to meet Boeing’s welding parameters. After verification, the remaining six welds, embedded with Type-K thermocouples, were made at different energy inputs. A high-speed camera was used to record the entire welding process for these six welds.

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Figure 4.1: (a) Dimensions of sub-scaled Ti-6Al-4V coupon for LFW, and (b) five machined Ti-6Al-4V blanks

4.2 Experimental Procedures

4.2.1 Welding processes

All twelve welds were performed on an APCI 20-ton mechanically driven linear friction welder located at EWI (Columbus, OH, USA) as shown in Figure 4.2. The utilized welding parameters for the sub-scaled coupons followed the Boeing process specification

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(BAC5695) for LFW of Ti-6Al-4V. These parameters were Boeing proprietary information and thus, their values were not explicitly provided in this dissertation. Table

4.1 summarizes the three sets of welding parameters used which were arbitrarily named as

‘Low’, ‘Medium’, and ‘High’ energy ratings. The medium energy rating corresponded to the nominal parameters specified in BAC5695. The low and high energy ratings represented a set of parameters that would result in lower and higher power input to the joint than the medium rating, respectively. It is noted that the welding processed stopped whenever the preset burn-off was met, and that time was recorded as the total welding time.

Table 4.2 summarizes the energy rating applied to each of the twelve welds of sub-scaled coupons. For each energy rating, at least three welds were made for consistency check.

Figure 4.2: Linear friction welder at EWI

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Table 4.1: Relation between welding parameters and energy ratings

Table 4.2: Summary of the applied energy ratings for each weld

4.2.2 In-situ measurements

Type-K thermocouples were instrumented during welding of R4-6 to R4-12 (see

Table 4.2) for in-situ temperature measurement. Five thermocouple holes were drilled via wire plunge electrical discharge machining into each Ti-6Al-4V blank assigned to the forging side of the weld pair (i.e., non-oscillating piece) as illustrated in Figure 4.3. These precise holes allowed for repeatable measurement locations and consistently anchor of the thermocouples to achieve an uninterrupted signal throughout the dynamic LFW process. 130

Although five holes were drilled, due to thermocouple availability, only four thermocouples were assigned to each weld to measure the temperature profiles at four distinct offset distances (0.84 mm, 1.39 mm, 1.96 mm, and 3.07 mm) from the weld interface (see Figure 4.3). The Type-K thermocouples used were OMEGA™ SCAXL-

020G-24, which was a grounded junction covered by a stainless steel sheath with 0.02” diameter as shown in Figure 4.4. It should be noted that this type of thermocouples generally had a response time (latency) of approximately 0.9 s (defined as the time reaching

63.2% of an instantaneous temperature change). Prior to welding, each pair of thermocouples was secured within the holes with adhesive and high temperature ceramic paste. Continuity of each pair of thermocouples was verified with a multi-meter to ensure the contact of thermocouples with the bottom of the drilled cavity, and all the thermocouples were then attached to a data acquisition device for collection of temperature profiles. The final setup of secured thermocouples is shown in Figure 4.5.

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Figure 4.3: Schematics of hole drilling layout and photograph of holes on one coupon

Figure 4.4: Type-K thermocouples used for temperature measurement

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Figure 4.5: Photographs of the weld blanks embedded with thermocouples

During welding, the entire LFW processes were also monitored in-situ using a high- speed camera to capture deformation features for each weld. As shown in Figure 4.6, the high-speed camera used was a FASTCAM SA-X2 1000K equipped with a Nikon all-in- one telephoto zoom lens (AF-S NIKKOR 28-300mm f/3.5-5.6G ED VR). Due to the specific assembly of the ACPI LFW machine, the only way to observe the work-pieces was to set the high-speed camera at a high location and pointed downward with an angle of 45° as illustrated in Figure 4.7. During recording, the camera was set to a frame rate of 3000 fps, and optical filters were added to address the issue of metal glowing. It should be noted that a fan was also used and secured on top of the machine to blow away the smoke that generated during welding.

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Figure 4.6: High-speed camera and zoom-in lens used for in-situ monitoring of LFW

Figure 4.7: Setup of the high-speed camera

4.3 Modeling Approaches

As described previously, LFW was a highly transient process involving rapid heating and severe plastic deformation. 3-D models are computationally expensive and

134 prone to divergence. 2-D models in the literature are based on the simulation domain parallel to the oscillation direction, thus not accounting for the flash flow transverse to the oscillation direction. As discussed below, computationally efficient 2.D numerical models were developed based on DEFORM® and were applied to simulate three experimental cases: low, medium, and high heat input welds made at EWI.

4.3.1 Model setup

The modeling procedure consisted of two stages in sequence: the conditioning stage and the merged stage. Modeling of the conditioning stage involved two work-pieces rubbing against each other under the applied forge force. It was utilized to generate temperature profiles across the interface representing frictional heating where little to none plastic deformation was expected. The generated temperature profiles and other solution variables were then mapped as initial conditions to the subsequent model for the merged stage, which involved only a single work-piece. The geometry of the single piece was taken from the combination of the two work-pieces at the last step of the conditioning stage. This new model for the merged stage was utilized to simulate the deformation heating behavior where severe plastic deformation as well as flash formation were expected. Detailed mapping procedures in DEFORM® were discussed in Appendix A.

Most 2-D LFW models in the literature, as introduced in chapter 2, considered only a computational domain parallel to the welding oscillation direction as shown in Figure

4.8. Since the dimension of the work-piece in the direction perpendicular to the oscillation direction (and thus the computational domain) was typically small compared to the

135 dimension parallel to the oscillation direction, the validity of 2-D representation was questionable. The 2-D models developed in the present research considered a computational domain perpendicular to the welding oscillation direction. The use of such transverse section seemed to be justified given the large dimension of work-piece along the oscillation direction (analogous to 2-D plane strain situation). As it accounted for the out- of-the-plane oscillation, the 2-D models were termed as 2.5-D. To author’s best knowledge, this transverse-type 2.5-D LFW model is the first of its kind, which has a potential for balanced accuracy and computational speed.

Figure 4.8: Schematics of (a) 3-D view of LFW process showing directions parallel (blue) and perpendicular (red) to the oscillation direction, and (b) 2-D view of LFW process/modeling showing computational domain parallel (blue) and perpendicular (red) to the oscillation direction

Detailed schematics of 2.5-D transverse model setup for simulating the conditioning stage is shown in Figure 4.9. This setup was based on the 2.5-D torsion model available in DEFORM® for rotary friction welding. Extending this setup to LFW process required creating a tube section with a very large radius (e.g., 2.005 m) such that the

136 curvature at the transverse section can be assumed as zero (negligible). Therefore, a linear motion of the work-piece in and out of the plane can be represented by applying a back- and-forth twisting. Additional parts (see Figure 4.9) of the model included two rigid dies tied with the two deformable work-pieces respectively. A constant compressional force was applied onto the top die. This force was calculated by using the experimental (machine) recorded normal pressure times the cross-sectional area of the designed tube section (blue ring in Figure 4.9). The linear movement was exerted in the form of angular velocity as a function of time on the bottom die. By setting the experimental (machine) recorded oscillator positions as individual arc lengths, the twisting angles ( ) in radians can be calculated through Equation 4.1. The angular velocity can then be calculated from the change of angles divided by the change of time.

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Figure 4.9: Illustration of 2.5-D transverse model setup for simulation the conditioning stage

휃 (4.1) 푎푟푐 푙푒푛푔푡ℎ = 2휋푟 360

The setup of the 2.5-D transverse model for the merged stage was similar to that for the conditioning stage, as shown in Figure 4.10. In this model, the two work-pieces were merged into a single deformable work-piece. The top die applied the same constant compressional force as used in the conditioning stage, while the bottom die continued the linear movement in the form of angular velocity as a function of time from where the conditioning stage ended.

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Figure 4.10: Illustration of 2.5-D transverse model setup for simulating the merged stage

Both models in the conditioning and merged stages had the welding interface region discretized or seeded with much finer mesh compared to those regions closer to the tooling

(dies). The minimal element size was approximately 0.3 mm. The surrounding environment and initial temperature of work-pieces were set to a constant value of 20 °C.

The heat loss from the surface of the work-piece(s) to the surrounding environment was prescribed via a heat convection coefficient of 50 푊푚−2퐾−1, representing a forced air cooling condition that was consistent with the experiments. At the interface of work- piece(s) to dies, a very high value of 10000 푊푚−2퐾−1 was set for the heat transfer coefficient to account for the heat sinking from the tooling. A minimal time step size of

0.002 s (or 2 ms) was used, which matched well with the sampling rate of the data acquisition system in the ACPI LFW machine. For modeling of the conditioning stage, the friction coefficient as a function of time was defined based on Coulomb’s Friction Law at

139 the weld interface. Detailed calculation method was discussed later in section 4.3.2 for welds made by different heat inputs. For modeling of the merged stage (no friction coefficient needed), a conversion factor for mechanical work to heat of 0.9 (90%) was selected to account for the plastic deformation heating.

Temperature dependent thermal conductivity, specific heat, and emissivity data from the DEFORM® library for Ti-6Al-4V were employed for models of both stages.

Combinations of the flow stress data from literature collections and mechanical tests

(Gleeble® hot compression and torsion tests described in Chapter 3) were defined in a tabular format into DEFORM® as functions of temperature, strain, and strain rate. The combined flow stress data covered a temperature range from 20 °C to 1500 °C at an average temperature increment of 200 °C. This allowed a reasonable and simplified estimation of the flow stress by interpolation (log interpolation in DEFORM®) between the tabulated data. It also covered up to 70% of the true strain with strain rate as high as 100 s-1. A summary of the flow stress data used for all modeling works is shown in Figure 4.11. It should be noted that the flow stresses at 1200 °C came from the Gleeble® hot compression and torsion tests. The torsion testing results at strain rate of 100 s-1 (see Figure 3.11) were linearly smoothed, while the compression testing results covered the flow stress data at strain rates from 0.001 s-1 to 10 s-1.

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Figure 4.11: Flow stress data of Ti-6Al-4V used in the present study [12][53]

4.3.2 Calculation of friction coefficient

A crucial parameter in modeling the conditioning stage is the friction behavior at the interface, which determines the amount of heat generation at the interface. In the

141 literatures, there exist divergent opinions on whether or not the friction heating dominates the initial and transition phases of LFW. This is mainly due to the difficulties for obtaining the friction measurements as a function of temperature directly from the LFW experiments.

In the present research study, an effective friction coefficient was calculated from the recorded LFW machine data via Coulomb’s Friction Law as shown in Equation 2.6.

An example of the recorded data for the high energy heat input weld was shown in Figure

4.12. Specifically, the oscillator position (x) and in-plane shear force (퐹𝑖푛) data (see Figure

4.12(b)) were applied to calculate the effective friction coefficient as a function of time as described in Equation 2.7. As the oscillation direction in the ACPI LFW machine was up and down, the effect of gravity (weight of the moving parts including machine tooling and one work-piece) was added for the calculation of the interface shear force, as shown in

Equation 4.2:

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Figure 4.12: Example of ACPI LFW machine recorded data for high energy input weld

퐹𝑖푛푡 = 퐹𝑖푛 + 푚푔 − 푚푎 (4.2)

143 where 퐹𝑖푛푡 is the interface shear force, and 퐹𝑖푛 is the in-plane shear force. The total weight of the moving parts on the ACPI LFW machine was 136.65 kg (301.26 lbs), and the weight of the work-piece was 0.35 kg (0.77 lbs).

The instantaneous nominal friction coefficient calculated for the high heat input weld was shown in Figure 4.13. It can be seen that the data in general exhibited significant amount of fluctuation and some of the peak values were above 1.0. This was primarily due to the calculated friction coefficient being directly linked to the measured in-plane shear force, which typically had large variation. To simplify the data input into DEFORM®, distinct moving average approach was applied in Matlab with a period of 200 to smooth the data. In this approach, every 200 data points were grouped together and the averaged values of each group were used. The final smoothed data of the effective friction coefficient as a function of welding time for high heat input weld is shown in Figure 4.14. The same approach to estimate the nominal friction coefficient was also applied into the low and medium energy input welds. The variations of such friction coefficient are discussed later in Figure 4.24.

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Figure 4.13: Calculated absolute instantaneous effective friction coefficient as a function of welding time for the high heat input weld

Figure 4.14: Smoothed effective friction coefficient for high heat input weld

4.3.3 Determination of transition time

Another key parameter affecting the entire modeling results is the transition point

(time) at which the conditioning stage is over, and the merged stage begins. In principle, this time corresponds to the complete contact and bonding of two work-pieces beyond which friction sliding ceases. As discussed in Chapter 2, some researchers set this threshold

145 time to the point when the material burn-off rate reached constant. However, interruption test revealed that the coalescence of two work-pieces did not occur uniformly at that point.

In the present research, a new option to determine the threshold time was established based on the in-situ high-speed videos. With a frame rate of 3000 fps, the high- speed camera was able to clearly capture every single cycle during LFW. An example of the monitored medium energy input LFW process is shown in Figure 4.15. These images show that the medium energy input LFW process did not heat up the Ti-6Al-4V plates uniformly. Hotter region was found to first start from the bottom of each plate. After 0.967 second of welding, the heat seemed to be built up through the entire interface and more flash was coming out of the entire interface till the end of the process (at 2.02 s). Therefore, the threshold time for modeling this medium energy input case was determined to be 0.967 s. In other words, the duration of the conditioning stage and merged stage models was

0.967 s and 1.053 s respectively. It should be noted that a small amount of flash in all directions had already formed at the threshold time. The same approach to determine the threshold time for low and high energy input welds was also conducted, and the results were discussed in section 4.4.

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Figure 4.15: High-speed imaging of medium energy input LFW process (plates were painted with black and white speckle patterns)

4.4 Experimental Results and Discussion

The linear friction welded Ti-6Al-4V blanks with and without thermocouple instrumentations were displayed in Figure 4.16. As shown in Figure 4.16(a), relative small amount of flash with ripples on the flash surface formed in the direction parallel to the oscillation direction (red arrow). Much thicker and smoothed flash formed in the direction perpendicular to the oscillation direction, which was also strongly curved up on one side and curved down on the other side. This was likely due to misalignment of blanks in the

LFW machine. It can be seen in Figure 4.16(b) that most of the embedded thermocouples were completely merged into the interface and secured inside even after the welding process.

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As observed in Figure 4.17, the peak temperatures and heating rates were significantly affected by the welding energy ratings that applied. The low energy weld resulted in the lowest observed peak temperature and slowest heating rate. The medium and high energy welds had similar peak temperatures, although the high energy weld experienced the most rapid heating rate among the three energy conditions. There were some fluctuations (peaks and valleys) at or close to the peak temperature levels that indicated the onset of deformation. In other words, as the interface temperature increased, the material began to plasticize and the thermocouples closest to the interface experienced some signal interruption due to the local deformation. It is interesting to note that the thermocouples placed outside the plastic deformation region (i.e., offset distances 1.96 mm and 3.07 mm as represented by the blue and green lines) did not experience any signal interruption and also recorded high-quality heating and cooling curves.

In order to examine the accuracies of the obtained thermal profiles, the cross- sectional microstructures of several welded blanks with drilled thermocouple holes were further analyzed in scanning electron microscopy (SEM) as shown in Figure 4.18. It can be seen that in one case (see Figure 4.18(a)) the thermocouples directly penetrated through the weld interface with undestroyed tip and merged body parts left in the BM and TMAZ respectively. While in another case (see Figure 4.18(b)), the thermocouple was totally squashed at the weld interface resulting in complete loss of the signal. Both of the cases indicated that the measured peak temperatures were very close to the weld interface and also explained the temperature variation observed in Figure 4.17.

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Figure 4.17: Thermal profiles measured by embedded thermocouples for three weld process energy ratings and in five unique welds (two low, two medium, and one high)

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The in-situ monitored results of the LFW processes by the high-speed camera for the three energy inputs are displayed in Figure 4.19, Figure 4.20, and Figure 4.21. Each Ti-

6Al-4V plate was painted with black and white speckle patterns for better imaging contrast and potential digital image correlation (DIC) analysis. However, since the camera was positioned to the plates with an angle for all welding cases, the DIC analysis was not

151 proceeded due to the undesired strain error introduced by the titled image plane. For the same reason, only half of the plates could be focused well in the camera due to shallow focus depth. These images show that the high energy process generated higher temperature

(brighter flash) in a much shorter time comparing to those obtained in the low energy process. In addition, the heating during the LFW process for all three cases was not uniform. The bottom part of the interface seemed to be heated faster than the top part of the interface. This could be attributed to the micro-swing effect inside the machine tooling where the work-pieces were not tightly clamped or perfectly aligned [2].

According to the images, the transition time from conditioning to merged stage was

0.52 s for high energy process, 0.967 s for medium energy process, and 2.681 s for low energy process. It should be noted that a small amount of flash formed in all directions at the determined threshold time for all three cases. These correlated well with LFW machine recorded plate upset data illustrated in Figure 4.22 where a small amount of upset

(generally below 0.5 mm) was also reached in all three cases. In addition, it is surprised to see that the determined transition times based on high-speed images resulted in the same transition upset (0.38 mm) for all three energy inputs. In other words, the upset at the transition point seemed to be independent to the process energy ratings. However, future work is still needed to examine if this upset can be standardized for similar LFW cases

(energy input and plate geometry) to determine the transition time from the conditioning to the merged stages.

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Figure 4.19: High-speed images for high energy LFW process (0 – 1.24 s) (R4-12); red arrow shows the welding oscillation direction

Figure 4.20: High-speed images for medium energy LFW process (0 - 2.02 s) (R4-7); red arrow shows the welding oscillation direction

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Figure 4.21: High-speed images for low energy LFW process (0 - 4.15 s) (R4-11); red arrow shows the welding oscillation direction

Figure 4.22: Machine recorded upset as a function of welding time for the three energy inputs. Dashed lines mark the transition times determined from the high-speed videos.

To generate the friction data for all the conditioning stage models, the instantaneous effective friction coefficient as a function of welding time was calculated for low, medium,

154 and high energy input welds based on the approaches introduced in section 4.3.2. As shown in Figure 4.23, all three sets of data experienced strong variations on the peak friction values. In order to simplify the data input into DEFORM®, moving average approach was taken over every 200 data points to smooth the data and the results are compared in Figure

4.24. It can be seen that the obtained effective friction coefficient values from current LFW experiments were relatively independent of the welding energy input as most friction coefficient values fell between 0.3 and 0.5. The portions of the effective friction coefficient data used in the conditioning (friction dominated) stage for the three energy inputs are further compared in Figure 4.25.

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Figure 4.23: Calculated instantaneous effective friction coefficient for (a) low energy weld (R4-11), (b) medium energy weld (R4-7), and (c) high energy weld (R4-12)

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Figure 4.24: Comparison of smoothed effective friction coefficient for three energy input welds

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4.5 Modeling Results and Validations

The simulation results of the conditioning stage for the three energy inputs were shown in Figure 4.26. The predicted peak temperatures in these three cases increased as the applied energy input rose. In other words, the use of higher welding pressure and oscillation amplitude resulted in a higher interface temperature. It should be noted that the predicted peak temperatures for medium and high heat inputs were slightly higher than the results in the literatures (around 1200 °C) [2][9]. Such differences could be due to a combination of several factors, such as the differences in the process parameters, friction coefficient, and material property data between the current model and the literature models.

In addition, all three models were nearly un-deformed (negligible flash formation) at the end of the conditioning stage which under-predicted the transition upset (0.38 mm) observed in the experimental data shown in Figure 4.22. Since the experimentally determined transition upset was relatively small at this point and the majority of the upset occurred beyond the transition point, the current modeling predictions in the conditioning stage were considered acceptable. Further investigation is still needed to improve the accuracy of the conditioning stage simulation in DEFORM®.

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The simulation results of the merged stage for the three energy inputs are shown in

Figure 4.27. As described in section 4.3, the merged stage started with the initial conditions from the last step of the conditioning stage (via the mapping procedure described in

Appendix A). Figure 4.27 shows the initial temperature and geometry of the single part in the merged model that were correctly mapped from the two separated work-pieces at the end of the conditioning stage model shown in Figure 4.26.

At the end of the merged stage (thus end of welding), Figure 4.27 shows severe plastic deformation in the form of flash formation for all three energy input ratings. The predicted flash showed smooth morphology with strongly curved flash shapes up on one side and down on the other side. The weld transverse cross sections were cut after welding and compared to the modeling predictions as shown in Figure 4.28. It was found that the

2.5-D simulation well captured the unique flash shape that was consistent to the experimental observations of flash formation transverse to oscillation direction. In 159 addition, higher energy input resulted more flash formation which was also captured by the merged stage models. It is interesting to note that the peak temperatures predicted in the merged stages were similar to those in the conditioning stages, indicating the deformation heat generation rate being comparable to the friction heat in LFW.

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Figure 4.27: Modeling predictions of temperature and deformation for the merged stage for: low (top row), medium (middle row), and high (bottom row) energy rating processes. Oscillation direction was in and out of the plane 161

Figure 4.28: Comparisons of predicted and observed flash shapes (on the transverse section) for: low (top), medium (middle), and high (bottom) energy rating processes

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The modeling results in Figure 4.26 and Figure 4.27 show that the temperature gradient across the weld interface (vertically) was quite different among the three energy inputs. The quantitative analysis of the predicted thermal profiles away from the weld interface was illustrated in Figure 4.29. The low energy rating weld seemed to exhibit a more gradual temperature change with relatively larger HAZ, while the high energy rating weld experienced a steeper temperature gradient with smaller HAZ at the end of both conditioning and merged stages.

For model validation, the cut weld cross-sections were further analyzed in optical microscope (OM) as shown in Figure 4.30. Samples were polished through the steps of

240, 320, 400, 600, and 800 grit silicon carbide sand papers, followed by a progress of 9

μm, 3μm, and 1 μm diamond paste on Leco LeCloth with Leco Diamond Extender

Compound. Etching of each sample entailed a process of swabbing with Kroll’s Reagent for 5 to 10 seconds.

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Figure 4.29: Predicted thermal profiles for three energy rating LFW processes along the center of the interface (at end of merged stage)

Figure 4.30: Preparation of welded samples (transverse sections) for analysis in optical microscope

The transverse macrographs of welded samples are shown in Figure 4.31. The plastically-affected zone (PAZ) which consists of both weld-center zone (WCZ) and thermal-mechanically-affected zone (TMAZ) can be clearly identified after etching. In the literature, it is generally accepted that the PAZ experiences temperature around and above the β-transus temperature of Ti-6Al-4V [2]. It should be noted that the heat-affected zone 164

(HAZ) was less noticeable in the optical macrographs of all the three welds due to the structural stability of Ti-6Al-4V below temperatures of 800 °C (800 °C was used by many researchers in literature to indicate the boundary of HAZ for linear friction welds of Ti-

6Al-4V [2]). High-resolution imaging in SEM is necessary to resolve the boundary of

HAZ.

Figure 4.31 shows that the PAZ thickness was not uniform along the sample width direction, where the center region exhibited the thinnest PAZ while the regions around the edges had the thickest PAZ. The predicted material flow evolution (see Figure 4.32) indicated that the “hotter” material was squeezed to flow outwards from the center region.

As a result, the cooler material from BM, originally located away from the interface, was pushed into the center region. Since titanium alloy in generally has relatively low thermal conductivity, the heat kept accumulating around edges, resulting in a larger area around the edges that was thermal-mechanically affected. This behavior correlated well with that observed by McAndrew et al. [13] who found that the thickness of the flash was directly related to the extent of material that was heated above the β-transus temperature. Moreover, the uneven PAZ thickness was also consistent to the observations by Schroder et al. [11] who found that the heat was initially developed faster at the interior center of the welding interface.

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Figure 4.31: Macrographs of the LFW welds (transverse sections); red dashed circles represent the PAZ in each sample

Figure 4.32: Predicted temperature and material flow evolution during medium energy rating LFW process. Oscillation direction was in and out of the plane

The PAZ thicknesses at both center and edges were measured and compared with the modeling predictions as shown in Figure 4.33, Figure 4.34, and Figure 4.35 for low, medium and high energy inputs, respectively. It was clear that the sizes of the PAZ were strongly dependent on the processing energy inputs used. Quantitative comparison illustrated in Figure 4.36 show that the increase of the rubbing velocity and applied pressure reduced the size of the PAZ significantly. The developed models also captured this trend and correlated relatively well with the experimental measurements.

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The above results are also consistent with the literature findings that an increase in the welding energy reduces the size of PAZ and HAZ in LFW [2][11][12][26]. Such behavior might seem somewhat counterintuitive at first as it is well known that a higher heat input would result in a larger HAZ size in fusion welding. The reason for this behavior in LFW is that hot material would be squeeze out as flash and the heat is thus lost due to the metal flow. In particular, it is generally recognized that a certain percentage increase in pressure would result in a higher material burn-off rate than the same percentage increase in the energy input [2][18]. Although more heat went into the weld, the material was also expelled at a faster rate. This increased the speed of much cooler material (farther back from the interface) moving into the welding interface [2]. Hence, the size of the PAZ was reduced and the temperature gradient was steeper across the weld interface when the applied pressure was increased. This also explained why the temperature gradient was much more gradual in the low energy rating weld (low rubbing velocity and pressure). In that case, the welding time was longest to reach the set burn-off and therefore the greatest extent of the weld material was heated above 800 °C for longest time among the three energy inputs.

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Figure 4.33: Comparison of PAZ and flash thickness for low energy rating weld and modeling results at the end of merged stage

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Figure 4.34: Comparison of PAZ and flash thickness for medium energy rating weld and modeling results at the end of merged stage

169

Figure 4.35: Comparison of PAZ and flash thickness for high energy rating weld and modeling results at the end of merged stage

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Figure 4.36: Effects of rubbing velocity and pressure on the minimum thickness of plastically-affected zone

For the linear friction welding experiments at EWI, several thermocouples were embedded via drilled holes as described in section 4.2. The thermocouple location 0.033”

(0.84 mm) from the interface was also monitored and tracked in all three modeling cases at both conditioning and merged stages as shown in Figure 4.37. It is noted that the

Lagrangian tracking was used in the merged models where a massless particle followed the flow of the extruded material. In all three cases, this massless particle traveled through the weld zone and was squeezed out from the interface as flash. For the real thermocouples, they were moved but not squeezed out possibly due to the constraint by the thermocouple wires. In other words, the temperature results in the merged stage likely corresponded to different locations in the models and the experiments.

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As shown in Figure 4.37, the peak temperatures calculated by the models were higher than those measured experimentally by thermocouples (by about 110 to 150 °C). In addition, the experimental temperature profiles displayed a rapid initial rise and then a more gradual increase to the peak temperatures. The predicted results from the low and medium energy input models exhibited a relatively linear rise to the peak temperatures, although the high energy input model seemed to capture the temperature rise relatively well. The differences in heating between the experimental measurements and model predictions could be caused by many factors including the simplification of 2-D heat flow, the accuracy of the effective friction coefficient, as well as the accuracy in temperature measured by thermocouples themselves. Hence, further investigation on the friction behavior is still needed in the future work.

Despite the difference in the experimentally measured and predicted temperature profiles, the predicted material upset correlated relatively well with the LFW machine measurement as shown in Figure 4.38. As shown in the figure, the conditioning stage models in general exhibited very little deformation. The majority of the deformation in all the current models came from the merged stage models. Moreover, the experimentally measured and predicted upset rate are compared as shown in Figure 4.39. The modeling results showed slightly higher average upset rates than those measured during welding

(especially for low energy weld). The difference is largely due to the fact that the 2.5-D transverse model setup was not capable of predicting the deformation (flash formation) parallel to the oscillation direction. Such limitation also existed for 2-D longitudinal models in the literature which did not consider the deformation in the transverse direction. Overall

172 the model predictions captured well the experimental trend that the deformation rate increased significantly with increasing welding energy inputs (both rubbing velocity and applied pressure).

173

Figure 4.37: Comparisons of predicted and measured temperature profiles at a monitored

location located initially 0.84 mm way from the interface for: low (top), medium

(middle), and high (bottom) energy rating processes

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Figure 4.38: Comparisons of predicted and measured plate upsets for: low (top), medium (middle), and high (bottom) energy rating processes

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Figure 4.39: Comparison of predicted and measured average plate upset rate for three energy rating LFW processes

4.6 Summary

In this chapter, several critical tasks have been successfully accomplished to develop 2.5-D transverse models for LFW of sub-scaled Ti-6Al-4V coupons. The key findings are summarized in the following.

1. Linear friction welding with designed low, medium, and high energy ratings were

applied to join the Ti-6Al-4V sub-scale coupons. Type-K thermocouples were

carefully embedded near the joint interface through drilled holes, and successfully

measured the temperature profiles in the regions near the weld interface. This was

one of the most complete sets of thermal profiles in the literature to date for LFW

of Ti-6Al-4V.

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2. A high-speed camera was used for in-situ observation of the entire LFW process.

The obtained images were analyzed to estimate the threshold time at which the

work-pieces were completely merged together. This threshold time was a crucial

parameter for modeling. It was also interested to find that the estimated threshold

time for welds joined at the three different energy ratings yielded the same material

upset.

3. The effective friction coefficient as a function of welding time for different energy

rating welds was calculated based on Coulomb’s Friction Law from LFW machine

recorded data. The friction values, varied between 0.25 and 0.5 for the most part,

were relatively independent of the energy ratings.

4. The computationally-efficient 2.5-D transverse LFW process models were

developed considering oscillation in and out of the simulation domain. The

modeling procedure consisted of sequentially coupled conditioning stage mode and

merged stage model. The solution variables at the end of the conditioning stage

model were mapped to the merged stage model as the initial conditions. The tested

flow stress data (see Chapter 3) and the effective friction coefficient were inputted

into the models.

5. The predicted temperature profiles, upsets, and upset rates as well as the final flash

shapes were consistent with the respective data measured experimentally for the

three energy inputs, indicating the validity of the 2.5-D models.

6. The effect of different processing energy ratings on interface material flow as well

as heat generation was investigated. The predicted PAZ and flash thicknesses

177 correlated well with those obtained experimentally. Moreover, high energy input resulted in narrower PAZ and HAZ as more hot metal was squeezed out as flash and consequently more BM moved into the joint area.

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Chapter 5 Linear Friction Welding and Modeling of Full- Scaled Ti-6Al-4V Net-shape Pre-forms

In this chapter, full-scaled Ti-6Al-4V coupons and net-shape pre-forms with weld area of five and nine square inches respectively were linear friction welded. The full-scaled coupons had the similar geometry as the sub-scaled coupons (introduced in chapter 4) but with much larger dimensions. The LFW of net-shape pre-forms involved joining a full- scaled Ti-6Al-4V coupon into a large plate to form a “T-joint”. Black and white speckle patterns were painted onto the side surfaces to map the surface deformation in-situ by DIC.

An infrared camera was also utilized to measure the temperature profiles near the interface and along freshly formed flash during heating and cooling of the LFW processes. The 2.5-

D transverse process models (described in chapter 4) were applied to simulate these full- scaled joints and the simulations were validated using the experimentally measured temperature profiles, flash shapes, and material burn-off and burn-off rate (or upset and upset rate). The heating and deformation behaviors during LFW of these production-level parts (e.g., a fitting in Boeing 737 airplane) were studied by the experimental measurements and model predictions.

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As described previously, the full-scaled coupon welding experiments were performed by MTI, and Boeing Research & Technology supplied materials and processing parameters.

5.1 Experimental Procedures

5.1.1 Design of Ti-6Al-4V pre-forms

The joint geometry of the net-shape pre-form, supplied by Boeing Research &

Technology (Berkeley, MO) as a demonstration piece for a fitting in Boeing 737 airplane, is shown in Figure 5.1. Specific dimensions of the T-joint are not provided in this dissertation as they are Boeing’s proprietary information. The Ti-6Al-4V composition and initial microstructure are the same as those discussed in section 3.1.

Figure 5.1: Net-shape pre-form design of a demonstration piece for Boeing 737 fitting (courtesy of Boeing Research & Technology)

5.1.2 Welding processes

The full-scaled joint, with a weld area of 9 in2 and required specialized tooling

(clamping), was no longer suitable in the ACPI LFW machine at EWI. Instead, the LFW 180 experiments of these full-scaled coupons were conducted at LIFT (Lightweight

Innovations for Tomorrow, Detroit, MI, USA). A LF35-75 LFW machine was employed as shown in Figure 5.2. This gigantic machine is one of the world’s largest universal linear friction welder manufactured by MTI (Manufacturing Technology, Inc.) with 35 ton oscillating and 75-ton forge capacity. The machine dimension is approximately 60 ft by 40 ft (18.3 m × 12.2 m) with an approximately 9 ft (2.7 m) deep foundation.

Figure 5.2: MTI LF35-75 LFW machine at LIFT facility in Detroit, MI

A series of twelve welds were conducted in the MTI machine. The first six welds utilized the manufacturer’s standard coupon geometry in order to tune the machine and verify its ability to meet the Boeing specified welding parameters. The geometry of the standard MTI coupon was shown in Figure 5.3. The second set of six welds were focused on manufacturing the pre-forms illustrated in Figure 5.2. The table of welding process parameters represented by energy ratings is shown in Table 5.1. Following welding, the T-

181 joints were post weld heat treated in a vacuum furnace for stress relieving at Boeing

Research & Technology (Berkeley, MO). Off-gassing procedures were applied to each weld prior to the heat treatment for removing oxides and increasing cleanliness in an actively pumping vacuum environment.

Figure 5.3: Standard geometry of MTI testing coupon for LF35-75 LFW machine

Table 5.1: Overview of welding parameters for full-scaled coupon welding trials

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5.1.3 In-situ measurements

The deformation and thermal histories for all twelve LFW welds were monitored in-situ by a high-speed camera and an infrared camera respectively; these cameras were used previously in the sub-scaled coupon welding trials. For completeness of this chapter, the images of the cameras are shown in Figure 5.4. The high-speed camera was FASTCAM

SA-X2 1000K equipped with a Nikon all-in-one telephoto zoom lens, and the infrared camera was FLIR A6751sc with 50 mm lens (f/2.5) and addition of a ND2 filter for temperature measurement between 250 °C and 2000 °C. During welding, the high-speed camera was set to record at a frame rate of 2000 fps with resolution of 1024 × 1024 pixels, while the infrared camera was set to a frame rate of 50 fps (50 Hz) with resolution of 640

× 512 pixels. It should be noted that the purpose of reducing the frame rate of the high- speed camera from 3000 fps used previously in the sub-scale coupon welds to current 2000 fps was to increase the total recording time (from 7 s to approximately 10 s). The positions of both camera relative to mounted work-pieces in the LF35-75 LFW machine are shown in Figure 5.5. Both cameras were approximately 1 m away from the work-pieces located on two sides of the LFW machine. Prior to welding, the infrared camera was pre-focused by using a solder iron which was heated to 350 °C and placed near the expected location of the weld interface.

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Figure 5.4: Images of the high-speed camera (left) and infrared camera (right)

Figure 5.5: Camera setup for in-situ measurement at LIFT

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5.2 Modeling Approaches

There were a total of five models involved in this chapter; one for each case shown in Table 5.1. Specifically, three modeling cases (low, medium and high energy ratings) were developed for LFW processes of MTI coupon plates (symmetrical), and two modeling cases (low and medium energy ratings) were created for LFW of T-shaped pre-forms (non- symmetrical). The validated modeling approaches from chapter 4, including considerations of computational domain perpendicular to welding oscillation direction (2.5-D transverse setup) and sequentially-coupled models (conditioning stage and merged stage), were applied to simulate all five LFW modeling cases in the current chapter. In addition, previously defined material property data, boundary conditions/data transfer (mapping), and the approach of determining the transition time (from conditioning to merged stages) were the same as those reported in Chapter 4.

It is noted that there were some minor changes made to the modeling approaches in Chapter 4, as discussed in the following. The detailed modeling setup for MTI coupon plates and T-shaped pre-forms are shown in Figure 5.6 and Figure 5.7 respectively. It can be seen that the defined radius from the axisymmetric axis to the work-piece center was much larger (increased from 2 m to 3 m) for modeling the full-scaled geometries. This was mainly due to the much larger overall dimensions for the full-scaled coupons than the sub- scaled coupons (in chapter 4). The larger radius was used to ensure a negligible curvature for the full-scaled geometries (for linear oscillation). In addition, the roles of the dies were reversed for modeling the T-shaped pre-forms to be consistent with the experimental setup, where the thicker plate was held stationary with a compressive force while the thinner plate

185 was oscillating. It should be noted that except for the thickness, the height and length of the plates involved in T-shape pre-forms were set to some arbitrarily values due to Boeing’s proprietary information.

Figure 5.6: Modeling setup for MTI standard coupon plates (2.5-D transverse setup)

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Figure 5.7: Modeling setup for T-shape pre-forms (2.5-D transverse setup)

For all five modeling cases in the current chapter, the effective friction coefficient determined from the sub-scale coupons (see Chapter 4) was used in the conditioning stage modeling. This was due to the difficulties in obtaining the necessary information of the

MTI LFW machine for the friction coefficient calculation. It served as an interesting test of the dependence of friction heating on the sample geometry at the same energy input rating.

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5.3 Experimental Results and Discussion

The first six linear friction welded Ti-6Al-4V full-scaled coupons are displayed in

Figure 5.8 and zoomed-in views of the flash shapes are shown in Figure 5.9. Similar to the flash formation in sub-scaled Ti-6Al-4V coupons (chapter 4), relative narrow and long flash with ripples on the flash surface formed in the direction parallel to the oscillation direction (red arrow). Much thicker and smooth flash formed in the direction perpendicular to the oscillation direction. It should be noted that the flash formed on both sides perpendicular to oscillation was relative flattened and not curved as strongly as that found in sub-scaled coupon welds (see Figure 4.16). This could be attributed to faster flash cooling and slower flash formation rate resulted from much larger weld area (five square inches) as well as better alignment for the full-scaled coupon welds than the sub-scaled welds.

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Figure 5.8: First group of six LFW welds from welding trials on LIFT LF35-75 (MTI coupon design with five square inches weld area); red arrow shows the welding oscillation direction

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Figure 5.9: Flash shapes for MTI coupon welds made at (a) low energy input, (b) medium energy input, and (c) high energy input

The linear friction welded T-shaped pre-forms at the low and medium energy ratings are displayed in Figure 5.10. The thicker bottom plate was held stationary while the thinner top plate was rubbing against the bottom plate. Relatively thin flash with smooth flash surface formed in the direction parallel to the welding oscillation direction (red arrows), which also seemed to be squeezed and stacked. Thick and smooth flash formed in the direction perpendicular to welding oscillation direction, where the flash tip was strongly curved upwards while the flash body seemed to be flatten. As shown in Figure 5.11, larger weld area resulted in slower flash formation rate (or upset rate) at the same energy input level.

190

191

Figure 5.11: Comparison of LFW machine recorded material upset profiles for welds of different weld areas and welded at low and medium energy inputs

As discussed previously, after welding, the T-shaped pre-form joints were subjected to post-weld heat treatment. One of such samples is shown in Figure 5.12. It should be noted that the increased cleanliness and loss of oxidation was due to off-gassing process during heat treatment in an actively pumping vacuum environment. The heat treated weld was tensile tested at IMR Test Lab (Lansing, NY), and the testing results are displayed in Table 5.2. The welded pre-form with low energy input easily met the minimum required values established for the parent material (Aerospace Material

Specification 4911 – Ti-6Al-4V Sheet, Strip, and Plate). Further testing of dynamic properties such as fatigue is still needed to better understand the weld quality.

Another interesting study performed by Boeing was to evaluate the time needed to completely machine the finished part from linear friction welded pre-form (see Figure 5.13) versus two other forms (hand forged block and die forging). Boeing’s analysis showed a

192 remarkable return-on-investment (ROI) factor of 26:1 (5-year based) for the hypothetical production change to a linear friction welded pre-form from the current manufacturing options.

Figure 5.12: Image of a post-weld heat treated (stress-relieved) pre-form

Table 5.2: Tensile testing results of low energy input welded pre-form

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Figure 5.13: Fifty percent machined pre-form welded at low energy input (courtesy of Boeing Research & Technology)

The high-speed images for linear friction welded full-scaled coupons, and T-shaped pre-forms are displayed in Figure 5.14 - Figure 5.16, and Figure 5.17 - Figure 5.18 respectively. Each group of images covered the entire LFW process where the first and last frames represented the start and end times for the individual weld. It can be seen that the required welding time was much longer for work-pieces that had larger weld area at the same level of energy input. For welding of three full-scaled coupons (Figure 5.14 - Figure

5.16), the interface seemed to be heated faster at the top side, and within a short period of time (generally less than 0.8 s) the entire interface was heated uniformly. This was likely due to non-perfect alignment and thus non-uniform initial contact between two work- pieces. Compared to those results reported previously for the sub-scaled coupon welds (for example see Figure 4.19), the non-uniform heating was much reduced for the full-scaled coupon welds as the adverse micro-swing effect is negligible in LF35-75 LFW machine.

During welding of the two T-shaped pre-forms (Figure 5.17 and Figure 5.18), the interface 194 seemed to be heated faster at the bottom side, but the entire interface needed much longer time (generally more than 2 s) to be heated uniformly. This resulted in the discontinuous formation of the flash tip that was very distinct from the observations for both low and medium energy inputs.

The transition times (a crucial input for modeling the three full-scaled coupon welds and two T-shaped pre-forms) were determined from the high-speed images using the same approach described in section 4.3.3. The determined transition times for linear friction welded full-scaled coupons were 3.83 s, 1.99 s, and 1.54 s for low, medium and high energy inputs respectively. Those for T-shaped pre-forms were 5 s and 2.69 s for low and medium energy inputs respectively. The determined transition times were plotted with machine recorded upset data in Figure 5.19. At their respective transition times, three full-scaled coupon welds had the same transition upset of 0.25 mm and two T-shaped pre-forms had the same upset of 0.5 mm. As discussed previously in Chapter 4, the three sub-scaled coupon welds had the same transition upset of 0.38 mm at their respective transition times

(see Figure 4.22). Hence, these results obtained in two different LFW machines, three different work-piece geometries, and three energy inputs suggest that the absolute upset could be utilized to determine the transition point from conditioning stage to merged stage under the same energy input. Further work is needed to understand the relation between the transition time and the energy input to improve the predictive capability of the models.

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Figure 5.14: High-speed images for full-scaled coupon weld made at low energy input; red arrow shows the welding oscillation direction

196

Figure 5.15: High-speed images for full-scaled coupon weld made at medium energy input; red arrow shows the welding oscillation direction

197

Figure 5.16: High-speed images for full-scaled coupon weld made at high energy input; red arrow shows the welding oscillation direction

198

Figure 5.17: High-speed images for T-shaped pre-form welded at low energy input; red arrow shows the welding oscillation direction

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Figure 5.18: High-speed images for T-shaped pre-form welded at medium energy input; red arrow shows the welding oscillation direction

200

As mentioned in section 5.1.3, one group of full-scaled coupon plates from each energy rating were painted with high-temperature black and white speckle patterns for digital image correlation (DIC). The recorded high-speed videos were split into a series of images, which were then analyzed in Ncorr (an open-source 2-D DIC software based on

MATLAB) to determine the surface displacement and strain maps at different welding times.

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Figure 5.20 shows the DIC-determined surface shear strain for full-scaled coupon welds made at the three energy ratings where the ROIs (regions of interest) were selected on the stationary work-piece to take advantage of much better image quality there than the oscillating work-piece. It should be noted that the DIC analysis for each energy rating ceased at the point where either the plastic deformation first occurred or the glowing of metal overwhelmed the contrast in ROIs. Hence, the total DIC duration for LFW processes made at each energy rating was different. The DIC-determined shear strain for all three cases showed some extent of noises across the side interface, indicating the shear strain distributions were somewhat non-uniform across the weld length direction. Despite the noises, these shear strain maps did shown that the shear strains increased as the welding time progressed.

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In order to better quantify the strain distribution, a series of x-y plots (shear strain vs. welding time) from point tracking at two different locations along the welding interface were taken, and the results for all three cases are as illustrated in Figure 5.21. As DIC analysis was time consuming, groups of consecutive images, each lasting about 0.15 s, were extracted from the videos for analysis. These groups were selected at ratio of welding time to total time equal to 0%, 15%, 30%, and 40% for ease of comparing three energy inputs. It should be noted that the DIC analysis for medium energy rating process at 40%

203 completion was missing due to poor image quality from metal glowing. As displayed in

Figure 5.21, the monitored shear strain at both locations fluctuated back and forth consisting with the welding oscillation. Moreover, it was clear that the monitored shear strain and strain accumulation at the top corner was relatively larger than those obtained around the center of the interface. This seemed to be consistent with the observations from the high-speed videos (Figure 5.14 - Figure 5.16) that the top side of the interface for all full-scaled coupon welds was heated faster than the center portion. In general, the peak shear strain values increased at both locations as the welding processes were progressing.

A local peak value of approximately 0.3% shear strain was captured for high energy input welding process prior to severe plastic deformation.

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Figure 5.21: Quantitative DIC (shear) strain analysis based on points tracking at two different points along the welding interface

The IR-measured temperature maps at different time frames for full-scaled coupons welds were displayed in Figure 5.22, Figure 5.23, and Figure 5.24 for low, medium and high heat inputs respectively, and for T-shaped pre-forms were displayed in Figure 5.25 and Figure 5.26 for low and medium heat inputs respectively. As the infrared camera was positioned in the opposite direction to the high-speed camera (Figure 5.5) during welding, the direct correlation of thermal images to high-speed images was not proceeded. This was mainly due to the reason that the flash formation (perpendicular to oscillation) on the two opposite sides of the weld interface was different (Figure 5.9 and Figure 5.10). It should 205 be noted that the infrared camera was not sensitive to temperature below 300 °C due to the addition of the high temperature filter lens. In addition, the monitored thermal profiles were focused on the outer edge of the interface prior to severe plastic deformation. After flash formation however, the infrared images were then capturing the temperature fields mostly on the flash. Therefore, the initial temperature (when it was lower than 300 °C) and the interface temperature development after flash formation were not able to be analyzed from the current measurements.

Figure 5.22: Infrared camera measured temperature maps for full-scaled coupon weld made at low energy input

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Figure 5.23: Infrared camera measured temperature maps for full-scaled coupon weld made at medium energy input

Figure 5.24: Infrared camera measured temperature maps for full-scaled coupon weld made at high energy input

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Figure 5.25: Infrared camera measured temperature maps for T-shaped pre-form made at low energy input

Figure 5.26: Infrared camera measured temperature maps for T-shaped pre-form made at medium energy input

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The thermal images (Figure 5.22 - Figure 5.26) show that the temperature development for all five welding processes were not uniform across the interface. Regions that experienced relatively higher peak temperatures during the welding of full-scaled coupons were found to be around the top side of the interface. On the contrary, the regions that had higher peak temperatures throughout the welding processes of T-shaped pre-forms were found around the bottom side of the interface. These observations were consistent with the recorded high-speed videos (based on local brightness), although the high-speed images were showing the opposite side to the thermal images.

The measured peak temperatures for low, medium, and high energy rating processed full-scaled coupons were 1173 °C, 1190.5 °C, and 1227.7 °C respectively, and for low and medium energy rating processed T-shaped pre-forms were 1148.5 °C and

1229.9 °C respectively. Even though all the peak temperatures were found on the freshly formed flash, their values still showed somewhat similar to the thermocouple measured temperature profiles for sub-scaled coupon welds in chapter 4 (Figure 4.17). In addition, the flash cooling rates of 38.3 °C/s and 54 °C/s were calculated for medium energy rating welded full-scaled coupon and T-shaped pre-form respectively (Figure 5.23 and Figure

5.26). This is expected given the much larger substrate and thus higher heat sink in the T- shaped pre-form than the full-scaled coupon.

5.4 Modeling Results and Validations

The simulation results of the conditioning stage for low, medium, and high energy rating welded full-scaled coupons are shown in Figure 5.27. The predicted peak temperatures were slightly higher (80 °C) than the results obtained for sub-scaled coupons 209

(Figure 4.26) at the same energy rating. This was likely due to the same friction coefficient used in both sub-scaled and full-scaled coupon welds. In addition, the peak temperature increased as the energy input was increased.

The modeling results of the conditioning stages for low and medium energy rating welded T-shaped pre-forms are shown in Figure 5.28. Even though the same friction coefficients were applied, much higher peak temperatures were predicted for these non- symmetrical LFW welds at the same energy rating. This was due to a much longer transition time (Figure 5.19) that was required for the T-shaped joints. In addition, all five models had very little deformation (negligible flash formation) at the end of the conditioning stage (0.05 mm and 0.3 mm shortened in full-scaled coupons and T-shaped pre-forms respectively), which were under-predicting the experimentally-recorded transition upset (0.25 mm and 0.5 mm for MTI coupons and T-shaped pre-forms respectively) shown in Figure 5.19. This issue was similar to what has been observed in the conditioning stage models for sub-scaled coupons in Chapter 4. Hence, further investigation is still needed to improve the accuracy of the conditioning stage simulation in DEFORM ®.

210

The simulation results of the merged stage for the three energy inputs of full-scaled coupons are shown in Figure 5.29. As described in section 4.3, the merged stage started with the initial conditions from the last step of the conditioning stage (via the mapping procedure described in Appendix A). Figure 5.29 shows the initial temperature and geometry of the single part in the merged models that were correctly mapped from the two

211 separated work-pieces at the end of the conditioning stage model shown in Figure 5.27. At the end of merge stage, severe plastic deformation in the form of flash formation was predicted for all three energy inputs, and the predicted flash showed smooth morphology with strongly curved flash. Figure 5.30 shows that the flash formed in the actual welding also exhibited some extent of randomness in its growth directions. The flash shapes from the actual welds behaved in either slightly curved or completely flattened manner that was not captured by the predicted flash shapes. The differences can be attributed to a combination of factors such as material flow behaviors, cooling conditions, mesh settings, and numerical accuracy in mechanical contact for full-scaled geometries. Thus, further model development is needed to improve the accuracy of the predicted flash shape.

It should be noted that the flow stress, convective heat transfer, and re-meshing criteria for modeling full-scaled coupons were very similar with those applied for modeling the sub-scaled coupons. As work-piece dimensions changed from the sub-scaled to full- scaled coupons, the temperature profiles (peak temperature and temperature gradient) were also different (see Figure 5.31). Such difference in temperature subsequently affected the material flow and cooling conditions.

212

213

Figure 5.30: Comparisons of predicted flash with actual welds for full-scaled coupons

Figure 5.31: Predicted temperature profiles at the center of the weld interface along the longitudinal direction (at end of merged stage). All welds plotted here were symmetrical

The modeling results of the merged stage for low and medium energy rating processed T-shaped pre-forms are shown in Figure 5.32, and the comparisons of predicted plastic deformation with actual welds are presented in Figure 5.33. It can be seen that the

214 simulated flash exhibited smooth morphology with relatively flattened flash body and strongly curved flash tip. In general, these predictions correlated well with experimental observations in terms of flash growth direction, flash shape, and amount of flash formation at the two different energy ratings. The modeling results show that the interface temperature and temperature gradient were no longer symmetrical about the interface for these T-shaped joints. As shown in Figure 5.34, the temperature dropped more slowly in the upper (thinner) plate than in the bottom (thicker) plate for both cases. Such steeper temperature gradient across the bottom plate thickness direction was likely due to the large mass and thus heat sink capacity of the bottom plate [16]. Moreover, the majority of the flash formation appeared to be originated from the upper plate. These results were consistent with the observations made by Lee et al. and McAndrew et al. [16] that the in- balanced material expulsion in the T-joint can be attributed to the “burrowing” effect of the upper plate being pressed into the bottom plate. Due to the large heat sink of the bottom plate, the region adjacent to the periphery of the joint was relatively cool, thus restricting the metal of the bottom plate being extruded.

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Figure 5.32: Modeling predictions of temperature and deformation for the merged stage for T-shaped pre-forms at: low (top) and medium (bottom) energy rating processes. Oscillation direction was in and out of the plane

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Figure 5.33: Comparisons of predicted flash with actual welds (transverse view) for T- shaped pre-forms

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Figure 5.34: Predicted temperature profiles for non-symmetrical (T-shaped) welds along the direction perpendicular to the weld interface (at end of merged stage)

As discussed in the previous chapter, the increase of the process energy ratings resulted significant reduction of the plastically-affected zone (PAZ) sizes for sub-scaled coupon welds. The same behavior was also obtained from the modeling results for full- scaled coupon and T-shaped joints as shown in Figure 5.35. In the current study, the increase of the energy rating came from the increase of both rubbing velocity and applied pressure. The increase of the rubbing velocity resulted in higher interface temperature (see

Figure 5.27). Combined with higher pressure, burn-off rate was much higher for higher energy rating (see Figure 5.44). As the hot metal was squeezed out carrying heat away from the joint, the PAZ thickness was thus reduced as the process energy input increased.

Figure 5.36 shows the predicted PAZ thickness was much larger for the full-scaled coupon welds and pre-forms than the sub-scaled coupon welds. For example, the T-shaped

218 joint in general exhibited a PAZ size that was 3 times larger than that obtained in the sub- scaled coupon weld at the same energy input. On the other hand, it can be found that the

PAZ was no longer thinnest at the interior center of the weld interface for non-symmetrical weld joint, which was typically the case for symmetrical weld joint (sub-scaled and full- scaled coupon welds). Interestingly, the PAZ was found to be the thickest at the center of the interface for all T-shaped welds. As shown in Figure 5.35, the “hotter” material in the

T-joints seemed to have had more resistance to travel (flow) towards the edge and as a result, more heat was accumulating over time at the interior center of interface than the edges. This further addressed the “burrowing” effect discussed previously. It should be noted that the flash in the T-shaped joint (especially for medium heat input) also experienced much lower temperature (compared to full-scaled coupons) at the end of the process, and accordingly, more flattened flash was obtained.

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Figure 5.35: Comparisons of predicted PAZ for full-scaled coupons (top) and T-shaped pre-forms (bottom) at the end of merged stage

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Figure 5.36: Effect of rubbing velocity and pressure on the minimum thickness of plastically-affected zone for all modeling cases in this study

The previously obtained DIC results for MTI full-scaled coupon welds (see Figure

5.20 and Figure 5.21) showed that the development of shear strain was much more intensive at the edge of the interface than at the center, and a peak strain of 0.3% was observed prior to severe plastic deformation. This quantitative data potentially created a new way for model validation as conventional strain measurement (by strain gauge or extensometer) was not suitable for highly dynamic processes like LFW. Representing a transverse section in the mid-length of the coupon, the current 2.5-D models were not able to resolve the strain at the center and edge of the interface. Therefore, 2-D longitudinal models were developed for comparison with the DIC results. The setup of the 2-D longitudinal model is shown in Figure 5.37. This model setup is very typical in the literature in that the computational domain is parallel to oscillation along x direction, and the work- 221 pieces are in plane strain condition. As most of the DIC results were obtained prior to severe plastic deformation and as such, only the conditioning stage models were considered for this particular study. The 2-D longitudinal results are shown in Figure 5.38 and Figure

5.39 for medium and high energy ratings respectively. According to these modeling results, the predicted shear strain was also unevenly distributed along the weld interface for both

LFW processes, which was fairly consistent with the results shown in the DIC contour maps (see Figure 5.20). Moreover, the predicted results also captured higher strain development at the outer edge of the interface than at the center as the welding process was progressing. The general trend of strain accumulation at both the center and corner regions from the models correlated relatively well with DIC results for high energy input weld especially after 20% completion of welding (see Figure 5.38). It should be noted that the modeling results exhibited very small strain distribution at early stages of the welding process, while DIC measurement showed much higher values. Hence, further image calibration is needed for DIC analysis to address the potentially strain errors due to strong machine vibration encountered even at the stationary work-piece during actual welding.

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Figure 5.37: Setup of 2-D longitudinal LFW process modeling; computational domain is parallel to oscillation direction on plane (along x direction)

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Figure 5.38: Comparisons of predicted and DIC calculated shear strain distributions at the center and outer edge of the interface for high energy rating LFW process of full-scaled MTI coupon up to 40% completion of welding

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The direct comparison between infrared camera measured and model predicted flash temperature was difficult since the IR measurement was on the longitudinal plane whereas 2.5-D transverse model was on a transverse plane. Hence, only peak temperatures were compared and the results for full-scaled coupons and T-shaped pre-forms are shown in Figure 5.40 and Figure 5.41 respectively. The measured peak temperatures were typically on the freshly formed flash (and the interior temperature was blocked by the flash). Thus, the same region in the models (yellow dashed cycles) were focused. It can be seen that the peak temperature ranges on the flash for all five models correlated well with those measured by the IR camera, supporting the validity of the 2.5-D models.

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Figure 5.40: Comparisons of modeling predicted (cross-sections) and infrared measured temperatures for MTI coupons at: low (left column), medium (middle column), and high (right column) energy rating processes; red arrows are oscillation directions and brown arrows are loading directions

The predicted material upset correlated relatively well with the LFW machine recorded data as shown in Figure 5.42 and Figure 5.43 for full-scaled coupons and T-

226 shaped pre-forms respectively. Same as the results of sub-scaled coupons, the majority of the deformation in full-scaled weld models came from the merged stages, although T-joint model showed slightly increased upset at the end of the conditioning stage. The peak upset in general was under-predicted by the current models, mainly due to the reason that the

2.5-D transverse model setup was not capable of predicting the deformation (flash formation) parallel to the oscillation direction. The average upset rate for all process models are compared and illustrated in Figure 5.44. It can be seen that the full-scaled welds experienced much smaller deformation rate with T-joints having the lowest rate at the same energy input level. Despite some differences between the measured and predicted peak upset, the overall consistence in the upset rate across the different energy ratings and work- piece geometries supports the validity of the 2.5-D models, making it a valuable tool for studying the large T-shaped joints for which there existed very little knowledge in the open literature.

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Figure 5.42: Comparisons of predicted and measured plate upsets for full-scaled coupons

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Figure 5.43: Comparisons of predicted and measured plate upsets for T-shaped pre-forms

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Figure 5.44: Comparison of predicted and measured average plate upset rate for all LFW processes conducted in this study

The results discussed thus far are now analyzed together to understand the effects of different heat inputs and work-piece geometries on material upset and upset rate, two important parameters for sound design of LFW process. The key LFW process variables for these cases are summarized in Table 5.3. Due to the proprietary nature of the welding process parameters supplied by Boeing, only the normalized values are given in this table.

The heat flux is calculated using Equation 5.1:

푞̇ = 4 × 휇 × 휎푁 × 퐴 × 푓 (5.1) where 푞̇ is the heat flux (per area) averaged over a cycle, 휇 is the friction coefficient, 휎푁 is the normal pressure, 퐴 is the oscillation amplitude, and 푓 is the oscillation frequency.

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Table 5.3: Matrix of LFW process parameters and heat generation

The effects of heat input and work-piece geometry on material upset and upset rate are shown in Figure 5.45. For welds that had the same weld area, the increase of the heat input (due to a combination of high oscillation amplitude and forge pressure) directly resulted in the increase of the weld upset rate. The modeling results also well captured the dependence of upset rate on heat input. The predictions showed that there were more than

30% increase in the material upset rate as the welding heat input was doubled. The effect of weld area on the upset rate was opposite to that of the heat input. In other words, for welds made at the same amount of heat input (heat flux), the increase of weld area decreased the weld upset rate. For example, the modeling predictions showed that there was more than 50% reduction in the upset rate as the weld area increased more than 4 times. It should be noted that welding of the T-shaped pre-forms required longest time due to heat sink of the bottom plate and thus, resulted in the smallest upset rate comparing to sub-scaled and full-scaled welds.

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Figure 5.45: Effect of welding heat input on material upset and upset rate from (a) and (c) experimental measurements, and (b) and (d) modeling predictions

As shown in Equation 5.1, the average heat flux is a combined parameter that includes the contribution of pressure and oscillation amplitude. In the actual welds, these two parameters were adjusted together to produce the low, medium and high energy inputs discussed previously while the oscillation frequency was held constant. As these two parameters can be adjusted independently, it is of interest to see how they individually

232 affect the temperature and deformation during LFW. To this end, a purely computational study was performed based on the medium heat input processed full-scaled coupon and T- shaped pre-form. Particularly, for each weld, two additional cases were modeled for the merged stage in such a way that only one of the two inputs was changed. In one case, the applied pressure was reduced to 50% while maintaining the same amplitude as the medium

(or baseline) heat input. Similarly, in the other case, the oscillation amplitude was reduced to 50% while maintaining the same pressure as the baseline. As shown by Equation 5.1, both cases resulted in a 50% reduction in the average heat input.

The calculated temperature distribution and deformed shape for the baseline and the two additional cases with reduced heat input are shown in Figure 5.46 and Figure 5.47 for the full-scaled coupon welds and T-shaped pre-forms, respectively. With less pressure, both the PAZ size and peak temperature of the weld interface were larger due to the hot plasticized material not being squeezed out. The upset versus welding time profiles for these cases are shown in Figure 5.48. The 50% reduction on applied pressure resulted in approximately 50% reduction on upset and upset rate for both symmetrical and non- symmetrical welds.

Similarly, the 50% reduction on oscillation amplitude also reduced the upset and upset rate but the extent of reduction was more significant than that due to the reduced forging pressure. Moreover, with smaller oscillation amplitude, the weld PAZ was very thin with peak temperature even below 1100 °C. This phenomenon indicated that the interface material was not efficiently heated up under smaller amplitude. The actual effect of these two parameters are expected to be even more complicated as a change in pressure

233 would likely change the friction heat generation. Further study of these parameters’ individual effect on the upset profile is needed.

Figure 5.46: Effect of pressure and amplitude on thermal and deformation fields of medium energy processed full-scaled coupon; (a) normal pressure and amplitude, (b) 50% reduction of normal pressure, and (c) 50% reduction of normal amplitude

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Figure 5.47: Effect of pressure and amplitude on thermal and deformation fields of medium energy processed T-shaped pre-form; (a) normal pressure and amplitude, (b) 50% reduction of normal pressure, and (c) 50% reduction of normal amplitude

Figure 5.48: Theoretical results showing effect of pressure and amplitude on material upset and upset rate for medium energy processed (a) full-scaled coupon welds and (b) T- shaped pre-forms

Finally, calculations were carried out to investigate the mesh size effect on the simulated results (i.e., a mesh convergence study). This allowed for a suitable element size 235 to be selected for the 2.5-D models for balanced computational time and accuracy. To this end, the high heat input processed full-scaled coupon weld was simulated with smaller mesh size at the weld interface and thus higher number of elements. The baseline model took 4 hours to complete using a single core while the model with smallest element size took 6 hours.

The modeling results of predicted temperature and deformed shapes and upset profiles are shown in Figure 5.49 and Figure 5.50 respectively. As illustrated in these figures, it can be seen that the reduction of minimal mesh size resulted in slight increase of the peak interface temperature and slight decrease of the peak upset. However, the flash shapes seemed to be influenced largely by the mesh size. It indicates that the flash shape could be quite sensitive to the numerical accuracy of mechanical contact and re-meshing.

Further improvement to the finite element solver is thus needed to improve the consistence in predicted flash shape under different mesh sizes.

236

237

Figure 5.50: Effect of mesh and minimal element size on material upset and upset rate of high energy processed full-scaled coupon

5.5 Summary

In this chapter, the 2.5-D transverse models established in Chapter 4 were extended for LFW of Ti-6Al-4V full-scaled coupons and pre-forms. The key findings are summarized in the following:

1. The previously developed LFW process parameters in terms of energy ratings

were successfully implemented into the production-scale LFW machine and

able to generate sound LFW welds for full-scaled Ti-6Al-4V plates. According

to studies by the research collaborator Boeing, the welded pre-form exhibited

excellent strength and return-on-investment ratio.

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2. High-speed digital image correlation and infrared temperature measurement

were implemented in LFW for in-situ observation of surface deformation and

temperature, respectively. These valuable data are important for not only

studying the process transients (e.g., non-uniform heating at the beginning of

the weld) but also developing and validating the numerical models.

3. The 2.5-D transverse models predicted the peak temperatures and upset profiles

that were consistent with the respective experimental data for full-scaled Ti-

6Al-4V coupons and pre-forms, indicating the validity of such models. Due to

the different sample geometries, the friction heating and deformation heating

stages took place at different welding times and affected the upset rate.

Particularly, the larger the weld area, the lower the upset rate at the same energy

input. For symmetrical joints, the flash formed equally from the two work-

pieces. On the other hand, for unsymmetrical T-shaped joint, the flash was

extruded mostly from the small plate.

4. Modeling results with individually adjusted pressure and oscillation amplitude

showed that the change of pressure mainly affected the total amount of material

that could be squeezed out, while the change of amplitude had a large influence

on heating up the interface material.

5. Mesh sensitivity study indicated that the change of total element number and

mesh size had minuscule effects on the weld temperature and upset profiles, but

relatively large influence on the flash shape.

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Chapter 6 Concluding Remarks

6.1 Summary of Present Research

Several linear friction welding cases of Ti-6Al-4V were investigated. These cases included different process parameters, different work-pieces geometries including both symmetrical and non-symmetrical work-pieces with different weld areas, and two different types of LFW machines.

During LFW, deformation and thermal fields were experimentally measured in-situ using tools including embedded Type-K thermocouples, a high-speed camera with telephoto zoom lens and LED illuminations, and an infrared camera with high temperature filter lens. For several work-pieces, the side surfaces were also sprayed with black and white speckle patterns for DIC study near the weld interface.

Gleeble® based high-temperature mechanical tests including hot compression and torsion were conducted on Ti-6Al-4V specimens. The testing conditions covered a wide range of temperature, strain, and strain rate levels. A series of high temperature flow stress data as functions of temperature, strain, and strain rate were obtained. Thermo-electrical- mechanical models were developed based on Abaqus® to improve the accuracy of the tested flow stress data.

2.5-D transverse LFW process models were developed based on DEFORM®, a commercially available finite element code. The transverse models considered a 2-D 240 computational domain perpendicular to the welding oscillation direction, and it accounted for the motion of the work-pieces in and out of the plane. A two-staged modeling approach was used and it involved separate considerations of frictional-sliding dominated stage and subsequent plastic deformation dominated stage in LFW processes. Such sequential modeling approach was consistent with the physical processes taken place in LFW including the frictional sliding at the early stage of welding followed by plastic material flow at the later stage of welding. The overall modeling results on both thermal and deformation fields were consistent with those obtained in the welding experiments.

The contributions of present research from the standpoint of understanding the fundamentals of LFW process based on both welding and modeling procedures are summarized as followings:

1. Work-piece geometry was found to significantly influence the deformation rate,

thermal profiles, material flow behaviors, and final flash shapes at the same energy

input level.

2. Thermocouple and infrared camera were used to successfully measure the

temperature transients in the interior of the work-pieces close to the interface and

on the work-piece side surface respectively. The infrared camera was also able to

measure the temperature distribution along freshly formed flash. High-speed video

was able to observe rubbing of work-pieces and plastic deformation behavior

throughout the welding process. These results showed that the rubbing process and

resultant temperature profiles were generally not uniform initially along the

241

longitudinal direction of the work-pieces and moreover they were strongly affected

by the part dimension and joint types.

3. DIC with high-speed imaging was successfully used to measure the surface strain

distribution in-situ during LFW, a technique that was attempted for the first time.

The DIC results showed some extensive amount of strain accumulation especially

at edges of the interface with peak shear strain of approximately 0.3% prior to

severe plastic deformation. This measurement also correlated relatively well with

modeling results, indicating potential capabilities of DIC for both model validation

and process quality control.

4. When used in a “building block” fashion, the net-shape linear friction welded pre-

forms can potentially increase the efficiency and thus reduce cost for manufacturing

of the titanium components. Analyses by Boeing Research & Technology showed

a high return-on-investment ratio of 26:1 for the T-shaped pre-forms welded by

LFW for an airline structure fitting.

5. Critical inputs to the process model included friction coefficient, transition of

conditioning and merged stages, and material property definition especially the

flow stress as functions of temperature, strain, and strain rate were also carefully

addressed in this research. The effective friction coefficient was calculated based

on LFW machine recorded process data via Coulomb’s Friction Law. Due to the

high noise level of the measured data, distinct moving average method was used to

smooth the calculated friction values. It was found that the obtained effective

friction coefficient was relatively independent to the process energy ratings that

242

were applied. Moreover, when the calculated friction values established from the

sub-scaled coupons were applied into work-pieces that had much larger weld area,

consistent thermal profiles were still obtained.

6. The transition point (time) at which the conditioning stage ends and the merged

stage begins was a crucial parameter for the process simulation. In the literature,

the methods for determining such point had certain limitations. In this research, a

new approach was developed to determine the transition point for each individual

LFW process based on high-speed imaging. Particularly, the observed time frame

from the high-speed video where the entire welding interface was heated up was

selected as the transition time for that welding process and inputted into the

respective model. The obtained process transition time was relatively independent

of the process energy ratings that were applied.

7. Gleeble® based mechanical tests including hot compression and torsion testing

were conducted by the research collaborator to generate the flow stress data as

functions of temperature, strain, and strain rate for Ti-6Al-4V especially at elevated

temperature levels (above -transus). Wide ranges of strain and strain rates were

also applied. Such data was very limited in open literatures for Ti-6Al-4V.

Thermal-electrical-mechanical models were developed to simulate both

mechanical tests for improving the accuracy of tested data. From the hot

compression model, it was found that the resistive heating in Gleeble® resulted in

a temperature gradient existed along the specimen length direction, which would

potentially influence the flow stress that was obtained from the force and stroke

243

data. The hot torsion model indicated that the shorter and thinner the specimen

gauge section resulted in more uniform temperature and deformation.

8. Due to complex mechanical contacts and severe plastic deformation, existing 3-D

models of LFW suffer from many issues including poor solution convergence, and

extensive computational cost. Hence, development computationally-efficient yet

accurate 2-D models is still important. The innovative 2.5-D models were

developed and applied to study LFW experiments with different process parameters

and work-piece geometries. The predictions in general were consistent with the

experimental data including peak interface temperature, flash temperatures,

material burn-off and burn-off rate (or upset and upset rate), and most importantly

the final flash shapes. For welding symmetrical work-pieces with smaller weld area,

the flash seems to be strongly curved up in one side and down to the other side.

However, more flattened or less curved flash formed during the welding of

symmetrical work-pieces that has much larger weld area. For the T-shaped joints,

the modeling results indicated that the flash in the T-shaped joints originated from

the thinner work-piece.

Due to the proprietary nature of the welding process parameters supplied by the research collaborator, these parameters had to be presented in a “normalized” form. It is hoped that the specific values will be available in future journal papers based on this dissertation research to improve the knowledge of LFW. Nevertheless, the developed in- situ measurement and modeling approaches can be applied to LFW with various welding parameters.

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6.2 Suggested Future Works

During the course of this research, a number of areas were identified that required further investigation, and they are listed as followings:

1. Mechanical testing such as tensile test is needed for sub-scaled coupon welds

and full-scaled welds to check if consistent joint strength is obtained for T-

shaped fitting pre-forms.

2. Detailed microstructural analysis on weld cross-section is needed for both full-

scaled coupons and T-shaped fitting pre-forms for the purpose of evaluating

flash formation mechanisms in the large testing pieces, and further validating

the modeling predictions.

3. 2-D longitudinal LFW process models, which consider the computational

domain parallel to welding oscillation direction are needed to obtain the flash

information in the direction parallel to oscillation as well as material burn-off

and heat transfer information at both the conditioning and merged stages.

4. Further image calibration is needed for more accurate DIC analysis to prevent

the introduced strain errors from machine vibration.

5. Friction coefficient calculation can be further investigated in the way that the

moving average RMS values can be taken and applied into models to see if they

will match the more gradually changed heating process. Moving average RMS

values in general are larger than regular moving average values.

245

6. The method for determining transition time requires actual welding

experiments to be performed prior to modeling. Thus, it needs to establish an

improved method to determine such transition time based on bond formation

without experiments a priori.

7. Further investigations are needed for full-scaled symmetrical models on flash

shapes. The current models showed some discrepancies on the predicted flash

shape to those observed experimentally. Many factors can potentially be

analyzed such as the definition of both thermal conductivity and convective

heat loss, flow stresses, re-meshing criteria that applied in current models.

8. The same Gleeble® hot compression modeling and analysis can be further

extended to other peak temperature levels to match each actual testing condition

used at Boeing Research & Technology.

9. Several boundary conditions such as convective heat loss were determined by

fitting to experimental data. Hence, an improved understanding of the boundary

conditions as a function of process parameters is essential to establish true

predictive model, rather than descriptive model

10. There are also areas to refine in the material database such as interpretation of

Gleeble® stress-strain data, as well as experimental data of physical properties

(thermal conductivity, emissivity, etc.).

11. Further research into microstructural evolution and transient material

phenomena characteristic of the LFW process will lead to a better

understanding of joint properties. This could apply to welding of Ti-6Al-4V as

246 well as dissimilar alloys in which solid-state joining methods such as LFW are extremely beneficial.

247

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Appendix A. Data Mapping in DEFORM®

Thermal profile mapping in DEFORM  Run conditioning stage model in DEFORM  Save keyword files for two work-pieces at the last step in conditioning stage model

 Open two keyword files in notepad++ or any other text editor

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 Copy all the RZ data from one keyword file and paste it to the end of the RZ data in another keyword file

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 Repeat above step for DRZ, BCCDEF, URZ, NDTMP, ELEMCON, STRAIN, STNCMP, DAMAGE, STRESS and MATAXI data (few examples are showing below)

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 Save the combined keyword file into a .txt file  Open the .txt file in Excel with tab and space delimitated

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 Re-number combined RZ, DRZ, BCCDEF, URZ, NDTMP data and make their node numbers in a sequential order (few examples are shown below)

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 Re-number combined ELECON, STRAIN, STNCMP, DAMAGE, STRESS, MATAXI data and make their elemental numbers in a sequential order (few examples are shown below)

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 Change total numbers at the call line for each of the combined data above 260

 Save the edited Excel file back to .txt file and change the filename extension to .KEY file  Start a new simulation in DEFORM and open the .KEY file  Generate a database for the .KEY file and save it

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 Start a new simulation again and copy-paste geometry coordinates from two work- pieces at the last step in conditioning stage model, and create a new geometry  Delete co-linear points and the line between two work-pieces to make geometry continues loop  Generate a mesh with defined mesh window if necessary  Interpolate data from previous created database for the .KE  Y file

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 Check temperature, mesh and boundary condition

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