基于SGPLVM-LSSVM算法的U形折弯件模型参数优化研究

Research on Parameter Optimization of U-shaped Bending Parts Model Based on SGPLVM-LSSVM Algorithm

影响高强度U形折弯件回弹的因素众多,比如工件尺寸、力学性能和负载条件等,使得高强度折弯件的弯曲回弹难以控制。把回弹角α和最小弯曲回弹半径R作为双目标函数,首先利用监督学习-高斯过程隐变量模型(SGPLVM)进行变量筛选和降维,构建U形折弯件的最小二乘支持向量机模型(LSSVM);再把SGPLVM-LSSVM实验结果分别与SVM、FEM、实际零件进行比较,验证了此算法模型的可行性。

There are many factors influencing springback of high strength U-shaped bending parts,such as workpiece size,mechanical properties and load conditions,which make bending springback of high-strength bending parts be difficult to control.The minimum bending radius R and the springback angleαwere taken as two objective functions.Firstly,supervised gaussian process latent variable model(SGPLVM)was used for variable selection and dimensionality reduction,the least squares support vector machine(LSSVM)model for U-shaped bending part was constructed.The prediction results of SGPLVM-LSSVM were compared with SVM,FEM prediction results and actual engineering parts to verify the feasibility of the proposed model.