汽车踏板横梁翻边过程中的回弹预测

Springback prediction of automobile pedal beam during flanging process

以汽车踏板横梁为研究对象,结合数值模拟技术与GRNN神经网络对零件翻边过程中的回弹情况进行预测。首先采用Autoform对踏板横梁翻边过程进行模拟,并与相同参数下实际零件回弹角进行对比,验证模拟结果的准确性和可替代性。再通过设计正交试验获取不同参数组合下各检测点的回弹角数据作为样本数据,并在MATLAB中对GRNN神经网络进行训练。为保证预测精度,设置多组光滑因子进行训练,发现光滑因子为0.1时,网络具有最优的逼近性能和预测性能,并作为最终网络模型进行检验。通过预测结果与真实结果进行对比,发现预测误差最大为4.3%,满足生产要求。研究表明,GRNN神经网络对板料翻边回弹预测既具有较高效率,又具有较高的精度。

For automobile pedal beam, the springback in flanging process was predicted by combining numerical simulation technology with GRNN neural network. At first, the process of pedal beam flanging was simulated by software Autoform, and their springback angles were compared with the actual ones under the same parameters to verify the accuracy and substitutability of simulation results. Then, the springback angle data of each detection point with different parameters were obtained by the orthogonal test, and the GRNN neural network was trained by MATLAB. In order to ensure the accuracy of prediction, the multiple sets of spread factors were trained. Furthermore, when the spread factor is O. 1, the network has the best approximation performance and prediction performance, which is regarded as the final network model to test, and the maximum error between prediction results and actual measurement results is 4. 3% which satisfies re- quirements of production. The results show that the GRNN neural network is of high efficiency and high precision for the springback prediction in sheet metal flanging.