springback prediction of sandwich panel using machine learning methods

The purpose of this paper is to obtain a model that quickly predicts springback in the three-point bending process of steel / pur / steel sandwich panels. firstly, based on the finite element simulation, the springback behavior for different punch radius, length between supports, and foam thickness is established. the results obtained by the finite element analysis show a satisfactory agreement with the experimental results. secondly, three machine learning approaches are applied, including linear regression (lr), artificial neural network (ann), and support vector machine (svm) in order to predict the springback of sandwich panels in the three-point bending process. the performance of these approaches is investigated by using some statistical tools like mean absolute error (mae), root mean square error (rmse), and coefficient of determination (r2). the obtained results show that the ann approach is the best model for predicting the springback of sandwich panels when considering accuracy.