Engineering Simulation Conference 2018
“Analysis of a Software Simulation of Discrete Machining applied to Milling using ANSYS 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 Cutting Tool (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:
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