A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing

作者：Cao, W. D.; Yan, C. P.^*; Wu, D. J.; Tuo, J. B.

来源：International Journal of Advanced Manufacturing Technology, 2017, 93(9-12): 4099-4110.

DOI：10.1007/s00170-017-0823-y

• Low carbon manufacturing
• Multi-objective optimization
• Machining parameters
• Small sample problem
• Multi-class support vector machine
• Ant lion optimizer

Low carbon gear hobbing is an environmentally friendly way to machine massive workpieces. The appropriate process parameter decision-making is of great significance to improve processing quality, reduce the machining time, production cost, and carbon emission in gear manufacturing. This paper first proposes a support vector machine/ant lion optimizer/gear hobbing (SVM/ALO/GH) integrated approach to do the multi-objective optimization of machining parameters for solving small sample problem of batch production. The first population of process parameters is generated by the multi-class SVM method. Pareto improvement and ALO algorithm are employed to obtain the optimal process parameters. Finally, the case study is presented to give a clear picture of the application of the optimization approach. The results uncover that the proposed SVM/ALO/GH method has better performance than the improved back propagation neural network/differential evolution (IBPNN/DE) algorithm over the small sample problem.

• 出版日期2017-12
• 单位机械传动国家重点实验室; 重庆大学