Engineering Simulation Conference 2018

“Analysis of a Software Simulation of Discrete applied to using Software modulated assisted machining supported milling application on Titanium”

Airey, K-A. a Lobmeyer, L. b Vilakazi, N a Kuppuswamy, R. a Hoffmann, A. b

a University of Cape Town b Frankfurt University of Applied Sciences 1. Background

Industrial Demand Geological Reserves Scope for Knowledge Base 2 Industrial Demand → High Strength-to-weight ratio Titanium → High Performance: 880MPa 휎푦 Ti6Al4V → Low chemical reactivity → Thermal stability: 푇표푝푒푟푎푡푖푛푔 up to ± 400 °C

Automotive Aeronautical Medical

Can South Africa respond?

3 Natural Reserves Lucrative Industry Resourcing Income Source

Benefits of a Value Added High Industries South African Performance Industry Titanium Industry

Knowledge Base Expansion

4 Existing Knowledge Base Mining Refining Manufacturing

• Suitable reserves • Established • Powdered • Established Extraction Oxides Companies processes • Pigment • Established • CSIR Process Demand Table 1 South African Titanium Reserve Base (Van Vuuren, 2009)

What’s Next?

5 2. Introduction

Manufacturing Context Modelling Solution

6 Difficult-to- 1 Machine Material

2

7 Modulation Assisted Machining

(MAM) (a) (b) Figure 3 Reduction in (a) the cutting forces, and (b) the flank wear noted in ultrasonic vibration-assisted cutting (UVC) of difficult-to-machining Inconel 718 (Nath, 2008)

▪Tool Wear ▪Tool Life Addressing Titanium ▪Cutting Forces ▪Lubrication Penetration machining ▪Cutting Temperature ▪Chip Removal challenges ▪Surface Integrity ▪Throughput 8 Discrete

Machining 4

Horizontal 1D X t = Acos 2πft + Vt Position X′ t = −2πfAsin 2πft + V Velocity

Duty Cycle DC = f t2 − t1

Where 푋 represents the horizontal upfeed direction, and 퐴 represents the horizontal amplitude., 푓 is the frequency, 푡 the time and 푉 the machine speed.

A duty cycle in 1D VAM is defined as the proportion of time per vibration cycle that the tool is actively cutting the workpiece material. 9 Discrete Machining

2D 5

Z t = Bsin 2πft arc(θ2 − θ1ሻ DC = Z′ t = 2πfBcos 2πft 2π ൫A2 + B2 ሻΤ2

Where 푍 represents the vertical direction, and 퐵 represents vertical amplitude, 푓 is the frequency, 푡 the time and 푉 the machine speed

10 Discrete Machining

2D 5

V V f = HSR = Critical 2휋퐹퐴 < 푉 up f 2πfA Assumptions Separation Upfeed Horizontal Threshold Index Speed Ratio

11 3. Theoretical Approach

Current Challenge

12 Modulation-Assisted Machining Device Machining Develops ❖ Unique experimental capabilities measure Solution Challenge ❖ strain rate ❖ Strain Development ❖ Temperature

Modelling & FEA ❖ Model development and Validation Microstructure Models ❖ Characterisation of ❖ Advanced Finite Feeds Element Analysis to microstructure and predict SPD residual stress to investigate surface integrity

Creates

Predictive control of MAM machining 13

Assisted Device Machining Assisted

- Modulation Figure 7 Proposed HSM experimental system plan, utilising PCD/PcBN tools 14 4. ANSYS Modelling

Ti6Al4V Discrete Machining 15 Ti-6Al-4V

Composition

Material FEA & Modelling Properties

16 Ti-6Al-4V ANSYS

Figure 8 ANSYS Ti-6Al-4V Material Selection

Material FEA & Modelling Properties

Figure 9 ANSYS Ti-6Al-4V Material Properties 17

Figure 10 3D Milling Simulation Models Modelling & FEA & Modelling

Figure 11 CBCN Indexable (Left), CBCN High Speed cutting tool (Right)

18 ANSYS

Simulation Table 4.1 Piezo actuator P-840.10 Series Parameters Operating Parameter Value Unit Travel range at 0-100V 15 µm Resolution

Push Force Capacity 1000 N Pull Force Capacity 50 N Torque on tip 0.35 Resonant Frequency (No Load) 18 Hz Length 32 Mm

Table 4.2 Prior ANSYS Modelling: Discrete Machining Parameters

Parameter Symbol Value Unit Modelling & FEA & Modelling Amplitude 퐴 0.02 Frequency 푓 0, 200, 500, 1000 Hz Cutting Speed 푉 50, 100,175, 200 m/min Dynamic friction 휇 0.3 (assumption)

19 5. Preliminary Results

Simulation

20 Orthogonal Cutting

Figure 12 Discrete Machining model at 175 m/min at 200Hz and 0.02 amplitude.

21 Orthogonal Cutting A

Figure 13 Incomplete Withdrawal ANSYS Simulation

22 Oblique Cutting

Figure 14 Equivalent Stress 23 Oblique Cutting

Figure 15 Total Deformation 24 6. The Next Step

ANSYS Model Expansion 25 Model Evolution

 Evaluate Simulation Model with prior Experimental Parameters  Use preliminary Simulation Model to Optimise Experimental Machining Parameters  Test Model Against Optimised Machining Parameters  Calibrate Simulation Model  Use Calibrated Simulation Model to Optimise Experimental Machining Parameters  Test MAM Device with Optimised Machining Parameters  Evaluate MAM Device

26 References

❖ BREHL, D. E. & DOW, T. A. 2008. Review of vibration-assisted machining. Precision Engineering, 32, 153-172. ❖ BREHL, D. E. & DOW, T. A. 2013. Review of Vibration-Assisted Machining methods for precision fabrication. Precision Engineering Center: North Carolina State University, Raleigh, North Carolina, USA. ❖ DAMBON, O. K., F.; HESELHAUS, M.; BULLA, B.; WEBER, A.; SCHUG, R.; BRESSELER, B. 2007. Vibration-Assisted Machining Research at Fraunhofer IPT –Diamond Turning and Precision Grinding. Aachen, Germany: Fraunhofer Institute for Production Technology IPT. ❖ DOW, T. A., CERNIWAY, M., SOHN, A. & NEGISHI, N. 2001. Vibration assisted diamond turning using elliptical tool motion. Precision Engineering Center: North Carolina State University, Raleigh, NC. ❖ GAO, Y., SUN, R. L., CHEN, Y. N. & LEOPOLD, J. 2016. Mechanical and thermal modeling of modulation-assisted machining. The International Journal of Advanced Manufacturing Technology, 86, 2945-2959. ❖ KENNAMETAL 2017. Titanium_material_machining_guide_Aerospace. ❖ LI, A., ZHAO, J., ZHOU, Y., CHEN, X. & WANG, D. 2012. Experimental investigation on chip morphologies in high-speed dry milling of titanium alloy Ti- 6Al-4V. International Journal of Advanced Manufacturing Technology, 62, 933-942. ❖ LIAO. Y.S.; CHEN, Y. C. L., H.M. 2007. Feasibility study of the ultrasonic vibration assisted drilling of Inconel superalloy. International Journal of Machine Tools & Manufacture 47, 1988–1996. ❖ MAROPOULIS, P. A., B. 1996. Integrated tool life prediction and management for an intelligent tool selection system. Journal of Materials Processing Technology, 61, 225-230. ❖ MORIWAKI, T. S., EIJI. 1995. Ultrasonic Elliptical Vibration Cutting. Annuals of the CIRP, 44, 31-34. ❖ MORIWAKI, T., SUZUKI, H., MIZUGAKI, J., MAEYASU, Y., HIGASHI, Y. & SHAMOTO, E. 2004. Ultraprecision Cutting of Molybdenum By Ultrasonic Elliptical Vibration Cutting. ❖ NEUGEBAUER, R. S., A 2004. Ultrasonic application in drilling. Journal of Materials Processing Technology, 633-639. ❖ VIJAYARAGHAVAN , V. G., A.; GAO, L.; VIJAYARAGHAVAN, R.; LU, G. 2016. A finite element based data analytics approach for modeling turning process of Inconel 718 alloys. Journal of Cleaner Production 1-9. ❖ ZEMANN, R., KAIN, L. & BLEICHER, F. 2014. Vibration Assisted Machining of Carbon Fibre Reinforced Polymers. Procedia Engineering, 69, 536-543.

27 Acknowledgements

❖National Research Foundation for Personal funding ❖Lucas Lobmeyer for ANSYS model contributions to the project ❖Professor Kuppuswamy & Hoffmann for advice, expertise and project support.

28 Thank you very much for your time

If you have any questions about this document please don’t hesitate to contact me at:

֍ [email protected], or

֍ [email protected]

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