锻压技术

基于BP人工神经网络的油箱端盖拉深成形仿真预测

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

作者：李兵姜海林刘奎武高鹏

分类号：TG385

出版年,卷(期):页码：2017,42(11):177-180

摘要：

以油箱端盖作为分析对象，借助DYNAFORM仿真软件，对油箱端盖的拉深成形过程进行数值模拟，并通过拉深成形试验验证可知，板料最大减薄率与最大增厚率的试验值与模拟值之间的相对误差分别为9.26%与8.32%，验证了有限元模型的正确性。结合正交试验，进行有限元仿真试验的设计，基于BP人工神经网络，对板料的成形质量进行仿真预测。选择冲压速度、模具间隙以及压边力作为输入层，将板料成形的最大减薄率作为输出层，建立了3-11-1的3层BP人工神经网络。通过BP人工神经网络的训练与测试得知：BP人工神经网络仿真预测值与数值模拟值之间的相对误差为2.15%，验证了BP人工神经网络应用于油箱端盖拉深成形质量仿真预测的正确性。

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.

基金项目：

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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