Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks

The objective of this paper is to develop a method for the rapid estimating springback in the hydroforming process of circular sheets. First, the springback behavior has been studied with using finite element simulations for various configurations such as sheet thickness, sheet diameter, and deformation pressure. The results obtained shows an excellent correlation with the experimental data. Next, the springback of circular sheets in the setting of hydroforming has been predicted using the artificial neural networks (ANN) approach. Statistical measures, specifically the mean square error (MSE) and the coefficient (R2) are implemented for evaluating this approach. The results reveal that artificial neural networks provide an accurate, high-performance model for predicting the springback of circular sheets.