Transactions of Materials and Heat Treatment

Title：Springback prediction in deep drawing of automobile back panel

Authors： Bai Xue Hu Jianhua Fan Haosen Han Nian

Unit： Wuhan University of Technology

KeyWords： prediction of springback BP neural network automobile back panel numerical simulation Dynaform

ClassificationCode：TG386

year,vol(issue):pagenumber：2017,42(9):42-45

Abstract：

For automobile back panel, the springback in deep drawing was predicted by BP neural network. Then, a CAD model was established by CATIA, and the sheet metal stamping process was simulated by Dynaform. Based on springback data of different parameters obtained by orthogonal experiment, reliability of the key data was verified by actual experiment, and three-layer BP neural network of 4-9-6 was established. Through training and testing the data samples, the accuracy of the prediction was up to 0.01. Furthermore, comparing with the prediction results and the actual measurement results, its maximum error is 5.62%. Therefore, it indicates that the BP neural network can predict the springback of the complex drawing parts with higher precision, and less time, which provides a good guide for drawing part die design.

Funds：

华中科技大学材料成形与模具技术国家重点实验室开放基金课题（P2015-01

作者简介：白雪（1990-），女，硕士研究生 E-mail：susan_bai@foxmail.com 通讯作者：胡建华（1966-），男，博士，副教授 E-mail：hujianhua@whut.edu.cn

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