汽车后围板拉深成形过程中的回弹预测

以汽车后围板为对象,运用BP神经网络对其拉深过程中的回弹量进行预测。通过CATIA建立CAD模型,运用Dynaform软件对板料冲压过程进行仿真分析,借助正交试验获取不同参数组合下的回弹数据,并通过试验验证了关键数据的可靠性,建立了4-9-6的3层BP神经网络回弹预测模型。通过对数据样本进行训练学习,控制其预测的精度为0.01,将预测结果与实际测量结果进行对比,显示预测误差最大为5.62%。说明运用BP神经网络可以实现对复杂拉深件成形的回弹预测,可以大量节省仿真预测的时间,对模具的设计具有很好的指导作用。

Springback prediction in deep drawing of automobile back panel

For automobile back panel, the springback in deep drawing was predicted by BP neural network. Then, a CAD model was es-tablished 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, compa-ring with the prediction results and the actual measurement results, its maximum error is 5. 62%. Therefore, it indicates that the BP neu-ral 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.