Constructing Surrogate Models for Springback in U-Bending Process

Abstract

In this study, surrogate models are constructed to approximate the behavior of simulation models for springback angles, sidewall curl, and sheet thickness reduction in U-bending process. The surrogate-modeling techniques used here are: (i) polynomial response surface (PRS), (ii) Kriging (KR) and (iii) radial basis functions (RBF). While constructing surrogate models, the following procedure is pursued. First, a set of training points is generated using Latin hypercube sampling method, and finite element simulations are performed at these points. Then, surrogate models are constructed utilizing the training data. The accuracies of the surrogate models are evaluated by using the leave-one-out cross validation errors. First-order PRS is found to be most accurate surrogate model for prediction of the springback angles, side wall curl, and sheet thickness reduction.