锻压技术

基于BP神经网络的传力片冲裁凸模磨损仿真预测

英文标题：Simulation prediction on blanking punch wear of leaf spring based on BP neural network

作者：庞敬礼

单位：江阴职业技术学院

分类号：TG385

出版年,卷(期):页码：2016,41(12):114-117

摘要：

以离合器盖总成中的传力片作为研究对象，借助Deform-3D仿真软件模拟了传力片冲裁过程中的凸模磨损情况，依据正交仿真试验的数据以及BP人工神经网络对传力片冲裁凸模的磨损量进行仿真预测。将冲裁间隙、凹模刃口圆角半径与冲裁速度作为BP神经网络的输入层，将冲裁凸模的最大磨损深度作为BP神经网络的输出层，建立3-12-1的3层BP神经网络。BP神经网络通过训练之后，仿真预测的最大误差为1.14%。基于正交试验的仿真数据对BP神经网络的性能进行检验，BP神经网络的仿真预测值与数值模拟值之间的误差为2.09%，并利用冲压级进模对BP神经网络的仿真预测值进行试验验证，两者之间的相对误差为8.25%，验证了BP人工神经网络应用于传力片冲裁凸模磨损仿真预测的准确性。

For leaf spring of clutch cover assembly, the punch wear in the blanking process was simulated by Deform-3D, and the punch wear was predicted based on orthogonal experiment and BP neural network. Then, blanking clearances, fillet radius and blanking speed were taken as the input layer, the wear depth of punch was taken as output layer, and BP neural network with three layers of 3-12-1 was established, and the maximum error of the prediction was 1.14% by training of BP neural network. Then, the performance of BP neural network was tested based on simulation data of orthogonal experiment, the error between BP neural network and simulation value reached 2.09%. Furthermore, the predicted value of BP neural network was verified by the progressive die of leaf spring, and the error between BP neural network and experimental value was 8.25%. Therefore, the accuracy of BP neural network predicting the punch wear of leaf spring was verified.

基金项目：

基金项目:江苏省中高等职业教育衔接课程体系建设项目(苏教职［2015］-19)

作者简介：庞敬礼（1982-），男，本科，讲师 E-mail：309212175@qq.com

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