Transactions of Materials and Heat Treatment

Title：Prediction on springback angle and process parameter optimization in electro-assisted spinning for AZ31B magnesium alloy

Authors： Wang Hui Liao Xuzhou Cai Jiwen Zhan Yuting

Unit： Nanjing University of Aeronautics and Astronautics

KeyWords： electro-assisted spinning springback angle AZ31B magnesium alloy BP neural network orthogonal experiment

ClassificationCode：TG306

year,vol(issue):pagenumber：2022,47(8):29-34

Abstract：

Springback is one of the unavoidable forming defect in metal spinning process. Therefore, in order to reduce the springback of spinning parts，on the basis of electro-assisted spinning, for AZ31B magnesium alloy spinning parts, the relationship between current intensity, spindle rotate speed, spinning roller feeding rate and springback angle was explored by orthogonal experiment, and the experiment results were analyzed by range analysis and variance analysis to obtain the influence laws of process parameters on the springback angle and the process parameters combination of the minimum springback angle. Then, taking the experimental data as training sample, the BP neural network model was established to predict the springback angle, and the combination of the process parameters obtained in the experiment was used as input to predict the springback angle and conduct the experimental verification. The results show that the relative error between the BP neural network model prediction results and the experiment results is less than 3%, which can accurately predict the springback angle. Furthermore，it provides theoretical guidance for actual production and further experimental research.

Funds：

南京航空航天大学科技创新基金(NS2016052)

作者简介：王辉（1978-），男，博士，讲师，E-mail：wh508@nuaa.edu.cn；通信作者：廖旭洲（1995-），男，硕士研究生，E-mail：2909615591@qq.com

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