Multi-objective optimization of grinding process parameters for improving gear machining precision

Abstract

The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads, which puts forward higher precision essentials for gear manufacturing. However, machining process parameters can cause changes in cutting force/heat, resulting in affecting gear machining precision. Therefore, this paper studies the effect of different process parameters on gear machining precision. A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations, tooth profile deviations, and tooth lead deviations through the cutting speed, feed rate, and cutting depth of the worm wheel gear grinding machine. The response surface method (RSM) is used for experimental design, and the corresponding experimental results and optimal process parameters are obtained. Subsequently, gray relational analysis-principal component analysis (GRA-PCA), particle swarm optimization (PSO), and genetic algorithm-particle swarm optimization (GA-PSO) methods are used to analyze the experimental results and obtain different optimal process parameters. The results show that optimal process parameters obtained by the GRA-PCA, PSO, and GA-PSO methods improve the gear machining precision. Moreover, the gear machining precision obtained by GA-PSO is superior to other methods.

References

[1] GuptaK, LaubscherR F, DavimJ P, et al.. Recent developments in sustainable manufacturing of gears: A review [J]. Journal of Cleaner Production, 2016, 112: 3320-3330

[2] LiuF, ChenY-h, LuB-b, et al.. Study on the vibration characteristics of worm helical gear drive [J]. Mechanism and Machine Theory, 2024, 191: 105484

[3] YadavR D, SinghA K. A novel magnetorheological gear profile finishing with high shape accuracy [J]. International Journal of Machine Tools and Manufacture, 2019, 139: 75-92

[4] XiangS-t, LiH-m, DengM, et al.. Geometric error analysis and compensation for multi-axis spiral bevel gears milling machine [J]. Mechanism and Machine Theory, 2018, 121: 59-74

[5] ZhouY-s, TangZ-w, ShiX-l, et al.. Efficient and accurate worm grinding of spur face gears according to an advanced geometrical analysis and a closed-loop manufacturing process [J]. Journal of Central South University, 2022, 29(1): 1-13

[6] SunS-l, WangS-l, WangY-w, et al.. Prediction and optimization of hobbing gear geometric deviations [J]. Mechanism and Machine Theory, 2018, 120: 288-301

[7] ChuC H, ZhouY-s, ZhangJ-h, et al.. Computational approaches for improving machining precision in five-axis flank milling of spiral bevel gears [J]. Computers & Industrial Engineering, 2023, 177: 108984

[8] LiuC-y, MeertenY, DeclercqK, et al.. Vibration-based gear continuous generating grinding fault classification and interpretation with deep convolutional neural network [J]. Journal of Manufacturing Processes, 2022, 79: 688-704

[9] WangS-l, ZhouB-c, FangC-g, et al.. Research on thermal deformation of large CNC gear profile grinding machine tools [J]. The International Journal of Advanced Manufacturing Technology, 2017, 91(1): 577-587

[10] XiaC-j, WangS-l, WangS-b, et al.. Geometric error identification and compensation for rotary worktable of gear profile grinding machines based on single-axis motion measurement and actual inverse kinematic model [J]. Mechanism and Machine Theory, 2021, 155: 104042

[11] LiuJ-l, MaC, GuiH-q, et al.. Geometricthermal error control system for gear profile grinding machine [J]. Advanced Engineering Informatics, 2022, 52: 101618

[12] ChenG S, MeiX S, LiH L. Geometric error modeling and compensation for large-scale grinding machine tools with multi-axes [J]. The International Journal of Advanced Manufacturing Technology, 2013, 69(9): 2583-2592

[13] XiaC-j, WangS-l, MaC, et al.. Crucial geometric error compensation towards gear grinding accuracy enhancement based on simplified actual inverse kinematic model [J]. International Journal of Mechanical Sciences, 2020, 169: 105319

[14] TangZ-w, ZhouY-s, WangS-h, et al.. An innovative geometric error compensation of the multi-axis CNC machine tools with non-rotary cutters to the accurate worm grinding of spur face gears [J]. Mechanism and Machine Theory, 2022, 169: 104664

[15] LiuJ-l, GuiH-q, MaChi. Digital twin system of thermal error control for a large-size gear profile grinder enabled by gated recurrent unit [J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(2): 1269-1295

[16] GuiH-q, LiuJ-l, MaC, et al.. New mist-edge-fog-cloud system architecture for thermal error prediction and control enabled by deep-learning [J]. Engineering Applications of Artificial Intelligence, 2022, 109: 104626

[17] GuiH-q, LiuJ-l, MaC, et al.. Mist-edge-fog-cloud computing system for geometric and thermal error prediction and compensation of worm gear machine tools based on ONT-GCN spatial-temporal model [J]. Mechanical Systems and Signal Processing, 2023, 184: 109682

[18] LiX-g, YangY, ZouZ, et al.. Study on the effect of force-thermal coupling error on the gear hobbing accuracy and its visualization [J]. The International Journal of Advanced Manufacturing Technology, 2019, 102(1): 583-594

[19] ZhangG-q, LiJ-q, JinX, et al.. Robust adaptive neural control for wing-sail-assisted vehicle via the multiport event-triggered approach [J]. IEEE Transactions on Cybernetics, 2022, 52(12): 12916-12928

[20] JiangX-h, KongX-j, HeS-r, et al.. Modeling the superposition of residual stresses induced by cutting force and heat during the milling of thin-walled parts [J]. Journal of Manufacturing Processes, 2021, 68: 356-370

[21] GuerriniG, LandiE, PeifferK, et al.. Dry grinding of gears for sustainable automotive transmission production [J]. Journal of Cleaner Production, 2018, 176: 76-88

[22] YadavR D, SinghA K, AroraK. Parametric analysis of magnetorheological finishing process for improved performance of gear profile [J]. Journal of Manufacturing Processes, 2020, 57: 254-267

[23] LinX-c, LiuY-h, SunS-l, et al.. Prediction and optimization of gear skiving parameters and geometric deviations [J]. The International Journal of Advanced Manufacturing Technology, 2022, 121(5): 4169-4185

[24] HanJ, JiangH, TianX-q, et al.. Online gear hobbing error estimation based on shaft vibration signal analysis [J]. Mechanical Systems and Signal Processing, 2022, 167: 108559

[25] HanJ, YouT-f, TianX-q, et al.. Active disturbance rejection controller for electronic gearbox [J]. Control Engineering Practice, 2023, 135: 105485

[26] ViswanathanR, RameshS, ManirajS, et al.. Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique [J]. Measurement, 2020, 159: 107800

[27] KantG, SangwanK S. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining [J]. Journal of Cleaner Production, 2014, 83: 151-164

[28] XuF, TseP W. A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis [J]. Journal of Central South University, 2019, 26(9): 2404-2417

[29] HuY-s, LiJ-g, HongM-n, et al.. Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm: A case study of papermaking process [J]. Energy, 2019, 170: 1215-1227

[30] ZhangW-z, LiG-q, ZhangW-w, et al.. A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization [J]. Swarm and Evolutionary Computation, 2019, 50: 100569

[31] WuB-h, CuiD, HeX-d, et al.. Cutting tool temperature prediction method using analytical model for end milling [J]. Chinese Journal of Aeronautics, 2016, 29(6): 1788-1794

[32] KimD H, LeeC M. A study of cutting force and preheating-temperature prediction for laser-assisted milling of Inconel 718 and AISI 1045 steel [J]. International Journal of Heat and Mass Transfer, 2014, 71: 264-274

[33] LuoM, WangJ, WuB-h, et al.. Effects of cutting parameters on tool insert wear in end milling of titanium alloy Ti6Al4V [J]. Chinese Journal of Mechanical Engineering, 2016, 30: 53-59

[34] ÖzlerL. The influence of variable feed rate on bushing and surface roughness in friction drilling [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019, 41(8): 308

[35] LiJ-q, WangC-yu. Effect of the feed rate on three-dimensional topography of Ti-6Al-4V based on dynamic mechanical model analysis in cutting process [J]. The International Journal of Advanced Manufacturing Technology, 2019, 105(1): 1539-1547

[36] KoseE, KurtA, SekerU. The effects of the feed rate on the cutting tool stresses in machining of Inconel 718 [J]. Journal of Materials Processing Technology, 2008, 196(1–3): 165-173

[37] YildizY, NalbantM. A review of cryogenic cooling in machining processes [J]. International Journal of Machine Tools and Manufacture, 2008, 48(9): 947-964

[38] KuntoğluM, SağlamH. Investigation of progressive tool wear for determining of optimized machining parameters in turning [J]. Measurement, 2019, 140: 427-436

[39] SutterG, RancN. Temperature fields in a chip during high-speed orthogonal cutting: An experimental investigation [J]. International Journal of Machine Tools and Manufacture, 2007, 47(10): 1507-1517

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Central South University