Transactions of Materials and Heat Treatment

Title：Optimization on forming quality for zirconium alloy support frame based on MLP-PSO algorithm

Authors： Chu Yan1 Mao Jianzhong1 Zhang Xiaomin1 Yuan Jiajian2

Unit： 1. College of Mechanical and Vehicle Engineering Hunan University 2. College of Mechanical and Electrical Engineering Hunan Communication Engineering Polytechnic

KeyWords： zirconium alloy support frame stamping thinning rate multi-layer perceptron particle swarm optimization algorithm

ClassificationCode：TG386

year,vol(issue):pagenumber：2023,48(3):61-67

Abstract：

In order to improve the stamping quality of zirconium alloy support frame, based on Dynaform software and orthogonal experimental design method, taking the maximum thinning rate as the evaluation index, the influence laws of parameters such as bending radius, sheet thickness, friction factor, clearance between punch and die, blank holder force and stamping speed on the forming quality of support frame were investigated. Then, the sample data was obtained by numerical simulation, and the model for predicting the thinning rate of support frame was trained by using the multi-layer perceptron neural network. Furthermore, the correlation of each factor was analyzed, and the optimal parameter scheme was obtained by the particle swarm optimization algorithm. The results show that the multi-layer perceptron neural network model can effectively predict the thinning rate of support frame. Among the parameters affecting the stamping of support frame, the bending radius and friction factor have a greater influence, while the clearance between punch and die and the stamping speed have less influence. Using the parameter scheme optimized by particle swarm algorithm for stamping, the maximum thinning rate is reduced by 24.2%, which can effectively reduce the fracture rate of support frame and improve the stamping quality of support frame.

Funds：

国家科技重大专项子课题（761215007）；湖南省教育厅资助科研项目（22C0951）

作者简介：楚岩（1998-），男，硕士研究生 E-mail：chuy0807@163.com 通信作者：毛建中（1963-），男，博士，教授 E-mail：maojianzhong66@163.com

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