Using Data from Physical Experiments to Train Machine Learning Material Models

Structural analysis of mechanical components, such as predicting the deformation behavior of sheet metal or assessing the crash safety of a vehicle, typically relies on finite element analysis (FEA). One critical aspect influencing the quality of these simulations are the material models that describe the relationship between strains and stresses. However, the development and selection of the most appropriate models is a significant challenge that involves costly and time-consuming testing and calibration procedures.