Transactions of Materials and Heat Treatment

Title：Prediction on deep drawing of fuel-tank end cap based on BP artificial neural network

Authors： Li Bing Jiang Hailin Liu Kuiwu Gao Peng

Unit： Jiangsu Food ＆ Pharmaceutical Science College Huaiyin Institute of Technology Occupational Skill Testing Authority Ministry of Huai′an

KeyWords： fuel-tank end cap BP artificial neural network deep drawing orthogonal test numerical simulation

ClassificationCode：TG385

year,vol(issue):pagenumber：2017,42(11):177-180

Abstract：

For fuel-tank end cap, the deep drawing process of fuel-tank end cap was simulated by software DYNAFORM. The deep drawing test indicates that the relative errors between experiment value and simulation value of the maximum thinning ratio and the maximum thickening rate of sheet metal are 9.26% and 8.32% respectively, and the correctness of finite element model is verified. Then, the design of finite element simulation test was carried out by orthogonal test, and the quality of sheet metal forming was predicted based on BP artificial neural network. Furthermore, three layers of 3-11-1 for BP artificial neural network were established with the input layer of stamping speed, die clearance and blank holder force and the output layer of the maximum thinning rate of sheet metal forming. The error between predicted value of BP artificial neural network and numerical simulation value is 2.15% by the training and testing of BP artificial neural network. Therefore, the accuracy of BP artificial neural network applied to deep drawing of fuel-tank end cap was verified.

Funds：

淮安市重点研发计划(工业及信息化)(HAG201614)

作者简介：李兵（1983-），男，硕士，讲师 E-mail：spxylb@163.com

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