New biomimetic approach for multi-objective optimization decision-making of collaborative gear hobbing and grinding

Abstract

Multiple process variable parameters such as cutting parameters, tool parameters, and machine tool parameters in gear hobbing and subsequent gear grinding processes directly affect gear machining accuracy and efficiency, as confirmed through historical processing experience or manual decision-making. To determine effective parameters quickly, this study proposes a new biomimetic approach for optimization and decision-making based on the improved multi-objective grasshopper optimization algorithm (MOGOA) and the information entropy technique for order preference by similarity to ideal solution (information entropy-TOPSIS) for gear hobbing and gear grinding collaborative machining. Specifically, the parameter optimization problem under collaborative machining of gear hobbing and gear grinding is presented. Then, a multi-objective model oriented to the optimization of gear accuracy and processing efficiency is constructed through optimization variables, i.e., hobbing and grinding process parameters. Furthermore, the improved MOGOA and information entropy-TOPSIS are used for optimal decision-making on the process parameter sets. Eventually, the effectiveness and practicality of the proposed multi-objective optimization decision-making method are verified via small module gear precision machining. Results and comparison demonstrate that the optimization and decision of multiple parameters for the collaborative machining of gear hobbing and gear grinding can be solved by the proposed method, whose efficiency and superiority are confirmed.

Graphical abstract

Keywords

gear hobbing / gear grinding / multi-objective grasshopper optimization / decision-makin / information entropy-TOPSIS

References

[1] Chaubey S K, Jain N K. State-of-art review of past research on manufacturing of meso and micro cylindrical gears. Precision Engineering, 2018, 51: 702–728

[2] Bagaiskov Yu. Effect of gear honing by tools with various elasticity on vehicle gear quality. Transportation Research Procedia, 2022, 63: 703–711

[3] Zhang W Q, Guo X D, Wang Y W, Wen Y K, Lin C C, Lim T C. A CNC tooth grinding method for formate face hobbed hypoid Gears. Mechanism and Machine Theory, 2020, 144: 103628

[4] Ding H, Wan Z G, Zhou Y S, Tang J Y. A data-driven programming of the human-computer interactions for modeling a collaborative manufacturing system of hypoid gears by considering both geometric and physical performances. Robotics and Computer-Integrated Manufacturing, 2018, 51: 121–138

[5] de Melo S A, Pereira R B D, da Silva Reis A F, Lauro C H, Brandão L C. Multi-objective evolutionary optimization of unsupervised latent variables of turning process. Applied Soft Computing, 2022, 120: 108713

[6] Pantoja H C, García A C, Rodríguez R P, Alfonso R D R, Acuña J A Y, Rondón R L Á. Datasets describing optimization of the cutting regime in the turning of AISI 316L steel for biomedical purposes based on the NSGA-II and NSGA-III multi-criteria algorithms. Data in Brief, 2023, 50: 109475

[7] Amorim L F, de Paiva A P, Balestrassi P P, Ferreira J R. Multi-objective optimization algorithm for analysis of hardened steel turning manufacturing process. Applied Mathematical Modelling, 2022, 106: 822–843

[8] de Toledo J V R, Pires T F, Pereira R B D, Ferreira J R. A multi-objective robust evolutionary optimization approach applied to the multivariate helical milling process of super duplex steel. Applied Soft Computing, 2023, 147: 110811

[9] Yang S C, Guo C Y, Ren W. Research on optimization of milling performance of V-groove micro-texture ball-end milling cutter. Journal of Mechanical Science and Technology, 2022, 36(6): 2849–2860

[10] Li W Y, Li B, He S P, Mao X Y, Qiu C C, Qiu Y, Tan X. A novel milling parameter optimization method based on improved deep reinforcement learning considering machining cost. Journal of Manufacturing Processes, 2022, 84: 1362–1375

[11] Singh S, Doddamani M, Powar S. Multi-objective optimization of machining parameter in laser drilling of glass microballoon/epoxy syntactic foams. Journal of Materials Research and Technology, 2023, 23: 3869–3879

[12] Delavar M R, Ramezanzadeh A, Gholami R, Sanei M. Optimization of drilling parameters using combined multi-objective method and presenting a practical factor. Computers & Geosciences, 2023, 175: 105359

[13] Saatçi E, Yapan Y F, Uslu Uysal M, Uysal A. Orthogonal turning of AISI 310S austenitic stainless steel under hybrid nanofluid-assisted MQL and a sustainability optimization using NSGA-II and TOPSIS. Sustainable Materials and Technologies, 2023, 36(6): e00628

[14] Sur G, Motorcu A R, Nohutçu S. Single and multi-objective optimization for cutting force and surface roughness in peripheral milling of Ti6Al4V using fixed and variable helix angle tools. Journal of Manufacturing Processes, 2022, 80: 529–545

[15] Cao W D, Chen X Z, Ni J J. Fuzzy decision-making approach of hobbing tool and cutting parameters. Engineering Applications of Artificial Intelligence, 2023, 125: 106655

[16] Cao W D, Ni J J, Jiang B Y, Ye C Q. A three-stage parameter prediction approach for low-carbon gear hobbing. Journal of Cleaner Production, 2021, 289: 125777

[17] Dixit Y, Kulkarni M S. Multi-objective optimization with solution ranking for design of spur gear pair considering multiple failure modes. Tribology International, 2023, 180: 108284

[18] Lv Y, Li C B, He J X, Li W, Li X Y, Li J. Energy saving design of the machining unit of hobbing machine tool with integrated optimization. Frontiers of Mechanical Engineering, 2022, 17(3): 38

[19] Yang Y C, Wu Y R, Tsai T M. An analytical method to control and predict grinding textures on modified gear tooth flanks in CNC generating gear grinding. Mechanism and Machine Theory, 2022, 177: 105023

[20] Wang L, Tian X L, Liu Q, Tang X J, Yang L J, Long H. Surface integrity analysis of 20CrMnTi steel gears machined using the WD-201 microcrystal corundum grinding wheel. The International Journal of Advanced Manufacturing Technology, 2017, 93(5–8): 2903–2912

[21] Zhang S Y, Zhang G B, Ran Y, Wang Z C, Wang W. Multi-objective optimization for grinding parameters of 20CrMnTiH gear with ceramic microcrystalline corundum. Materials, 2019, 12(8): 1352

[22] Ni H X, Yan C P, Cao W D, Liu Y F. A novel parameter decision approach in hobbing process for minimizing carbon footprint and processing time. The International Journal of Advanced Manufacturing Technology, 2020, 111(11–12): 3405–3419

[23] Ni H X, Yan C P, Ni S F, Shu H, Zhang Y. Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process. Advances in Manufacturing, 2021, 9(2): 216–234

[24] Cao B, Li G L, Fortunato A, Ni H X. Continuous generating grinding method for beveloid gears and analysis of grinding characteristics. Advances in Manufacturing, 2022, 10(3): 459–478

[25] Klocke F, Löpenhaus C, Sari D. Process concepts for gear finish hobbing. Procedia CIRP, 2016, 41: 875–880

[26] Kumar Parida A, Maity K. Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM). Measurement, 2019, 137: 375–381

[27] Ahmadi B, Ceylan O, Ozdemir A. Distributed energy resource allocation using multi-objective grasshopper optimization algorithm. Electric Power Systems Research, 2021, 201(3): 107564

[28] Zhou X J, Tan W, Sun Y, Huang T W, Yang C H. Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS. Expert Systems with Applications, 2024, 240: 122539

[29] AiX, XiaoS G. Concise Handbook of Cutting Parameters (3rd ed). Beijing: China Machine Press, 1994 (in Chinese)

[30] WangL T, Zhao X X, LiJ. Optimization of gear grinding parameters with worm grinding wheel. China Mechanical Engineering, 2021, 32(17): 2136–2141 (in Chinese)

Acknowledgements

This work was supported by the Natural Science Research Project of Anhui Educational Committee, China (Grant No. 2023AH050999); the Anhui Agricultural University Talent Research Funding Project, China (Grant No. rc412302); and the National Natural Science Foundation of China (Grant No. 52205079).

Conflict of Interest

The authors declare no conflict of interest.

RIGHTS & PERMISSIONS

2024 Higher Education Press