Machine learning for the improvement of springback modelling

Abstract

New demands in the automotive industry have led to an increase in the use of Advanced High-Strength sheet metal materials. However, higher values of strength are usually achieved at the expense of reduced formability and increased sensitivity of the springback. Today, springback is one of the more important factors that influence the quality of sheet metal forming products. During the forming process, sheet metal undergoes a complicated deformation history, which is why the accurate prediction of the springback level can be very difficult. Today, a good compromise between the finite element method (FEM) simulation and the real stamping process can be achieved, but there is still limited reliability of the FEM springback prediction. In this paper, the machine learning (ML) approach was used to update the FEM for springback modelling. Combined models are tuned to better reflect the measured experimental data.