Transactions of Materials and Heat Treatment

Title：Springback behavior on sheet metal in gas bending predicted by machine learning coupled with finite element analysis

Authors： Xu Chengliang Zhang Xianglin Wang Dajun

Unit： 1.Industry College Guangzhou Vocational College of Technology and Busines 2. State Key Laboratory of Material Forming and Die Technology Huazhong University of Science and Technology 3. College of Automation Chongqing University of Posts and Telecommunications

KeyWords： air-assisted bending bending springback machine learning neural network finite element analysis

ClassificationCode：TG302

year,vol(issue):pagenumber：2022,47(6):107-112

Abstract：

A nonlinear springback model of bending process was constructed by machine learning neural network (NN) coupled finite element analysis (FEA), and considering different materials, process parameters and die geometry shapes, the bending springback behavior of workpiece could be predicted effectively and accurately. When the die opening amount V=11 mm and the sheet thickness t=3 mm, for structural steel HC220 material, the root mean square errors RMSE of prediction value (YNN) by machine learning NN model and analytical solution after springback (yJBP) were 0.28 and 1.70 respectively. For dual-phase steel DP590 material, the RMSE values of YNN and yJBP were 0.45 and 0.22 respectively. The CPU calculation time of NN model, analytical solution after springback (yJBP) and FEA methods were 3.1, 6.3 and 278 s respectively, and the NN model was of less CPU calculation time. The experimental results show that the NN model can achieve an optimal balance between good prediction accuracy and efficient solution speed.

Funds：

广东省普通高校特色创新项目（自然科学类）（2018GKTSCX053）；2021年度广州市基础研究计划基础与应用基础研究项目(2021-02-08-13-0018)；材料成形与模具技术国家重点实验室基金资助项目(P2021-016)

徐承亮(1970-)，男，硕士，高级工程师，副教授 E-mail：281552074@qq.com

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