Abstract
Trochoidal milling is an alternative path planning strategy with the potential of increasing material removal rate per unit of tool wear and therefore productivity cost while reducing cutting energy and improving tool performance. These characteristics in addition to low radial immersion of the tool make trochoidal milling a desirable tool path in machining difficult-to-cut alloys such as nickel-based superalloys. The objective of this work is to study the dynamic stability of trochoidal milling and investigate the interaction of tool path parameters with stability behavior when machining IN718 superalloy. While there exist a few published works on dynamics of circular milling (an approximated tool path for trochoidal milling), this work addresses the dynamics of the actual trochoidal tool path. First, the chip geometry quantification strategy is explained, then the chatter characteristic equation in trochoidal milling is formulated, and chatter stability lobes are generated. It is shown that unlike a conventional end-milling operation where the geometry of chips remains constant during the cut (resulting in a single chatter diagram representing the stability region), trochoidal milling chatter diagrams evolve in time with the change in geometry (plus cutter entering and exiting angles) of each chip. The limit of the critical depth of cut is compared with conventional end milling and shown that the depth of cut can be increased up to ten times while preserving stability. Finally, the displacement response of the cutting tool is simulated in the time domain for stable and unstable cutting regions; numerical simulation and theoretical results are compared.
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Long H, Mao S, Liu Y, Zhang Z, Han X (2018) Microstructural and compositional design of Ni-based single crystalline superalloys ― a review. J Alloys Compd 743:203–220. https://doi.org/10.1016/J.JALLCOM.2018.01.224
Thakur A, Gangopadhyay S (2016) State-of-the-art in surface integrity in machining of nickel-based super alloys. Int J Mach Tools Manuf 100:25–54. https://doi.org/10.1016/j.ijmachtools.2015.10.001
Zhu D, Zhang X, Ding H (2013) Tool wear characteristics in machining of nickel-based superalloys. Int J Mach Tools Manuf 64:60–77
Corne R, Nath C, El Mansori M (2017) Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling. J Manuf Syst 43:287–295. https://doi.org/10.1016/J.JMSY.2017.01.004
Akhavan Niaki F, Feng L, Ulutan D, Mears L (2016) A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials. Int J Mechatronics Manuf Syst 9:97–121
Kong D, Chen Y, Li N, Tan S (2017) Tool wear monitoring based on kernel principal component analysis and v-support vector regression. Int J Adv Manuf Technol 89:175–190. https://doi.org/10.1007/s00170-016-9070-x
Wang J, Xie J, Zhao R, Zhang L, Duan L (2017) Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robot Comput Integr Manuf 45:47–58. https://doi.org/10.1016/J.RCIM.2016.05.010
Akhavan Niaki F, Michel M, Mears L (2016) State of health monitoring in machining: extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy. SI NAMRC 24(Part 2):361–369
Akhavan Niaki F, Ulutan D, Mears L (2015) Stochastic tool wear assessment in milling difficult to machine alloys. Int J Mechatronics Manuf Syst 8:134–159
Zhang J, Starly B, Cai Y, Cohen PH, Lee YS (2017) Particle learning in online tool wear diagnosis and prognosis. J Manuf Process 28:457–463. https://doi.org/10.1016/J.JMAPRO.2017.04.012
Yu J, Liang S, Tang D, Liu H (2017) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol 91:201–211. https://doi.org/10.1007/s00170-016-9711-0
Wu D, Jennings C, Terpenny J, Gao RX, Kumara S (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng 139:071018. https://doi.org/10.1115/1.4036350
Pleta A, Ulutan D, Mears L (2014) Investigation of trochoidal milling in nickel-based superalloy inconel 738 and comparison with end milling. In: ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, p V002T02A058-V002T02A058
Kardes N, Altintas Y (2007) Mechanics and dynamics of the circular milling process. J Manuf Sci Eng 129:21. https://doi.org/10.1115/1.2345391
Otkur M, Lazoglu I (2007) Trochoidal milling. Int J Mach Tools Manuf 47:1324–1332
Deng Q, Mo R, Chen ZC, Chang Z (2018) A new approach to generating trochoidal tool paths for effective corner machining. Int J Adv Manuf Technol 95:3001–3012. https://doi.org/10.1007/s00170-017-1353-3
Akhavan Niaki F, Pleta A, Mears L (2018) Trochoidal milling: investigation of a new approach on uncut chip thickness modeling and cutting force simulation in an alternative path planning strategy. Int J Adv Manuf Technol 97:641–656. https://doi.org/10.1007/s00170-018-1967-0
Yan R, Li H, Peng F, Tang X, Xu J, Zeng H (2017) Stability prediction and step optimization of Trochoidal milling. J Manuf Sci Eng 139:091006. https://doi.org/10.1115/1.4036784
Rauch M, Duc E, Hascoet J-Y (2009) Improving trochoidal tool paths generation and implementation using process constraints modelling. Int J Mach Tools Manuf 49:375–383. https://doi.org/10.1016/J.IJMACHTOOLS.2008.12.006
Toh CK (2003) Tool life and tool wear during high-speed rough milling using alternative cutter path strategies. Proc Inst Mech Eng Part B J Eng Manuf 217:1295–1304
Shixiong W, Wei M, Bin L, Chengyong W (2016) Trochoidal machining for the high-speed milling of pockets. J Mater Process Technol 233:29–43. https://doi.org/10.1016/J.JMATPROTEC.2016.01.033
Uhlmann E, Fürstmann P, Rosenau B, et al (2013) The potential of reducing the energy consumption for machining TiAl6V4 by using innovative metal cutting processes. In: 11th global conference on sustainable manufacturing. Berlin, pp 593–598
Altintaş Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Technol 44:357–362
Davies MA, Pratt JR, Dutterer B, Burns TJ (2002) Stability prediction for low radial immersion milling. J Manuf Sci Eng 124:217. https://doi.org/10.1115/1.1455030
Gradišek J, Govekar E, Grabec I, Kalveram M, Weinert K, Insperger T, Stépán G (2005) On stability prediction for low radial immersion milling. Mach Sci Technol 9:117–130. https://doi.org/10.1081/MST-200051378
Pradeep SA, Iyer RK, Kazan H, Pilla S (2017) Automotive applications of plastics: past, present, and future. Appl Plast Eng Handb:651–673. https://doi.org/10.1016/B978-0-323-39040-8.00031-6
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The authors would like to thank the National Science Foundation for supporting this work under Grant No. 1760809.
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Akhavan Niaki, F., Pleta, A., Mears, L. et al. Trochoidal milling: investigation of dynamic stability and time domain simulation in an alternative path planning strategy. Int J Adv Manuf Technol 102, 1405–1419 (2019). https://doi.org/10.1007/s00170-018-03280-y
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DOI: https://doi.org/10.1007/s00170-018-03280-y