Point-based tool representations for modeling complex tool shapes and runout for the simulation of process forces and chatter vibrations

doi:10.1007/s40436-018-0219-8

Abstract

Abstract: Geometric physically-based simulation systems can be used for analyzing and optimizing complex milling processes, for example in the automotive or aerospace industry, where the surface quality and process efficiency are limited due to chatter vibrations. Process simulations using tool models based on the constructive solid geometry (CSG) technique allow the analysis of process forces, tool deflections, and surface location errors resulting from fiveaxis machining operations. However, modeling complex tool shapes and effects like runout is difficult using CSG models due to the increasing complexity of the shape descriptions. Therefore, a point-based method for modeling the rotating tool considering its deflections is presented in this paper. With this method, tools with complex shapes and runout can be simulated in an efficient and flexible way. The new modeling approach is applied to exemplary milling processes and the simulation results are validated based on machining experiments.

The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-018-0219-8.pdf

Key words: Modeling, Tool geometry, Milling

P. Wiederkehr, T. Siebrecht, J. Baumann, D. Biermann. Point-based tool representations for modeling complex tool shapes and runout for the simulation of process forces and chatter vibrations[J]. Advances in Manufacturing, 2018, 6(3): 301-307.

TrendMD

References

1. Altintas Y, Kersting P, Biermann D et al (2014) Virtual process systems for part machining operations. CIRP Ann Manuf Technol 63(2):585-605
2. Denkena B, Böß V, Nespor D et al (2015) Simulation and evaluation of different process strategies in a 5-axis re-contouring process. Procedia CIRP 35(31):31-37
3. Wiederkehr P, Siebrecht T (2016) Virtual machining:capabilities and challenges of process simulations in the aerospace industry. Procedia Manuf 6:80-87
4. Arrazola PJ, Özel T, Umbrello D et al (2013) Recent advances in modelling of metal machining processes. CIRP Ann Manuf Technol 62:695-718
5. Munoa J, Beudaert X, Dombovari Z et al (2016) Chatter suppression techniques in metal cutting. CIRP Ann Manuf Technol 65(2):785-808
6. Biermann D, Iovkov I (2015) Modelling, simulation and compensation of thermal effects for complex machining processes. Prod Eng 9(4):1-3
7. Odendahl S, Kersting P (2013) Higher efficiency modeling of surface location errors by using a multi-scale milling simulation. Procedia CIRP 9:18-22
8. Denkena B, Grove T, Ermisch A et al (2014) New simulation based method for the design of cut-off grinding segments for circular saws. Global Stone Congress, Antalya, Turkey, pp 146-156
9. Hense R, Baumann J, Siebrecht T et al (2015) Simulation of an active fixture system for preventing workpiece vibrations during milling. In:Proceedings of the 4th international conference on virtual machining process technology, 2015
10. Martelotti ME (1941) An analysis of milling process. Trans ASME 122:3-11
11. Kienzle O (1952) Die Bestimmung von Kräften und Leistungen an spanenden Werkzeugen und Werkzeugmaschinen. Z Ver Dtsch Ing 94:299-305
12. Koenigsberger R, Sabberwal AJP (1961) An investigation into the cutting force pulsations during milling operations. Int J Mach Tool Des Res 1:15-33
13. Kline WA, DeVor RE, Lindberg JR (1982) The prediction of cutting forces in the end milling with application to cornering cuts. Int J Mach Tool Des Res 22:7-22
14. Budak E, Altintas Y, Armarego EJA (1996) Prediction of milling force coefficients from orthogonal cutting data. Trans ASME 118:216-224
15. Kline WA, DeVor RE (1983) The effect of runout on cutting geometry and forces in end milling. Int J Mach Tool Des Res 2-3:123-140
16. Weinert K, Surmann T, Enk D et al (2007) The effect of runout on the milling tool vibration and surface quality. Prod Eng Res Devel 2:265-270
17. Merdol DS, Altintas Y (2004) Mechanics and dynamics of serrated cylindrical and tapered end mills. J Manuf Sci Eng 126:317-326
18. Budak E (2003) An analytical design method for milling cutters with nonconstant pitch to increase stability:part 2. J Manuf Sci Eng 125:35-38
19. Tunç LT, Ö zkirimli Ö , Budak E (2015) Generalized cutting force model in multi-axis milling using a new engagement boundary determination approach. Int J Adv Manuf Technol 77:341-355
20. Fu Z, Yang W, Wang X et al (2015) Analytical simulation of milling:influence of tool design on cutting forces. Procedia CIRP 31:258-263
21. Engin S, Altıntaş Y (2001) Mechanics and dynamics of general milling cutters, part I-helical end mills. Int J Mach Tools Manuf 41:2213-2231
22. Foley J, Feiner S, Hughes J (1993) Introduction to computer graphics. Addison-Wesley, Reading, MA, p 559
23. Weinert K, Surmann T (2003) Geometric simulation of the milling process for free formed surfaces, simulation aided offline process design and optimization in manufacturing sculptured surfaces. Witten 27:21-30
24. Surmann T, Biermann D, Kehl G (2008) Oscillator model of machine tools for the simulation of self excited vibrations in machining processes. In:Proceedings of the 1st international conference on process machine interactions, Hannover, Germany, pp 23-29

[1]	Qi-Sen Chen, Li Dai, Yu Liu, Qiu-Xiang Shi. A cortical bone milling force model based on orthogonal cutting distribution method [J]. Advances in Manufacturing, 2020, 8(2): 204-215.
[2]	Wei Zhao, Asif Iqbal, Ding Fang, Ning He, Qi Yang. Experimental study on the meso-scale milling of tungsten carbide WC-17.5Co with PCD end mills [J]. Advances in Manufacturing, 2020, 8(2): 230-241.
[3]	Xiao-Fen Liu, Wen-Hu Wang, Rui-Song Jiang, Yi-Feng Xiong, Kun-Yang Lin. Tool wear mechanisms in axial ultrasonic vibration assisted milling in-situ TiB₂/7050Al metal matrix composites [J]. Advances in Manufacturing, 2020, 8(2): 252-264.
[4]	Mohd Nazri Ahmad, Mohammad Khalid Wahid, Nurul Ain Maidin, Mohd Hidayat Ab Rahman, Mohd Hairizal Osman, Izzati Fatin Alis@Elias. Mechanical characteristics of oil palm fiber reinforced thermoplastics as filament for fused deposition modeling (FDM) [J]. Advances in Manufacturing, 2020, 8(1): 72-81.
[5]	Wen-Hua Ye, Yun-Xia Guo, Heng-Fei Zhou, Rui-Jun Liang, Wei-Fang Chen. Thermal error regression modeling of the real-time deformation coefficient of the moving shaft of a gantry milling machine [J]. Advances in Manufacturing, 2020, 8(1): 119-132.
[6]	Xiao-Guang Guo, Ming Li, Zhi-Gang Dong, Rui-Feng Zhai, Zhu-Ji Jin, Ren-Ke Kang. Smooth particle hydrodynamics modeling of cutting force in milling process of TC4 [J]. Advances in Manufacturing, 2019, 7(4): 364-373.
[7]	Saroj Kumar Padhi, S. S. Mahapatra, Rosalin Padhi, Harish Chandra Das. Performance analysis of a thick copper-electroplated FDM ABS plastic rapid tool EDM electrode [J]. Advances in Manufacturing, 2018, 6(4): 442-456.
[8]	Anton Germashev, Viktor Logominov, Dmitri Anpilogov, Yuri Vnukov, Vladimir Khristal. Optimal cutting condition determination for milling thin-walled details [J]. Advances in Manufacturing, 2018, 6(3): 280-290.
[9]	Lutfi Taner Tunc, Orkun Ozsahin. Use of inverse stability solutions for identification of uncertainties in the dynamics of machining processes [J]. Advances in Manufacturing, 2018, 6(3): 308-318.
[10]	Michael Löser, Andreas Otto, Steffen Ihlenfeldt, Günter Radons. Chatter prediction for uncertain parameters [J]. Advances in Manufacturing, 2018, 6(3): 319-333.
[11]	Ehsan Jafarzadeh, Mohammad R. Movahhedy, Saeed Khodaygan, Mohammad Ghorbani. Prediction of machining chatter in milling based on dynamic FEM simulations of chip formation [J]. Advances in Manufacturing, 2018, 6(3): 334-344.
[12]	Kundan K. Singh, Ramesh Singh. Chatter stability prediction in high-speed micromilling of Ti6Al4V via finite element based microend mill dynamics [J]. Advances in Manufacturing, 2018, 6(1): 95-106.
[13]	Jyotirmoy Nandy, Hrushikesh Sarangi, Seshadev Sahoo. Microstructure evolution of Al-Si-10Mg in direct metal laser sintering using phase-field modeling [J]. Advances in Manufacturing, 2018, 6(1): 107-117.
[14]	Saroj Kumar Padhi, Ranjeet Kumar Sahu, S. S. Mahapatra, Harish Chandra Das, Anoop Kumar Sood, Brundaban Patro, A. K. Mondal. Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy [J]. Advances in Manufacturing, 2017, 5(3): 231-242.
[15]	Feng-Tian Li, Li Ma, Lin-Tao Mi, You-Xuan Zeng, Ning-Bo Jin, Ying-Long Gao. Friction identification and compensation design for precision positioning [J]. Advances in Manufacturing, 2017, 5(2): 120-129.