Transactions of Materials and Heat Treatment

Title：Multi-objective optimization on process parameters of power spinning for connecting rod bushing based on genetic algorithms

Authors： She Yong Zhan Gang Fan Wenxin Mao Weixiu Yu Shijie

Unit： Guizhou Vocational Technology College of Electronics & Information Guizhou University North University of China

KeyWords： power spinning connecting rod bushing neural network genetic algorithm multi-objective optimization

ClassificationCode：TG376

year,vol(issue):pagenumber：2019,44(12):187-191

Abstract：

For the complex relationship between process parameters and mechanical property of connecting rod bushing formed by power spinning, the non-linear relationship of RBF neural network between process parameters (thinning rate, feeding ratio) and mechanical properties (tensile strength, elongation) was established, and the neural network was trained and tested by the results of orthogonal experiment. Then, comparing the measured values with the predicted values, it was found that the established neural network model had high prediction accuracy. Furthermore, this non-linear relationship was regarded as the fitness function, and the multi-objective optimization model (tensile strength, elongation) of the process parameters (thinning rate, feeding ratio) was established based on the genetic algorithm theory. Finally, the multi-objective Pareto optimal solution set was obtained, and the feasibility of the optimal solution set was verified by experimental analysis to effectively improve the design efficiency of process parameters and the mechanical properties of products.

Funds：

国家自然科学基金资助项目(51665007)；贵州省经济和信息化委员会资助项目(2017GH063)

佘勇（1990-），男，硕士，助教 E-mail：sy09020641@163.com 通讯作者：占刚（1979-），男，博士，教授 E-mail：zhangangbmw@163.com

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