Machine learning is significantly changing the view on existing problems in many application areas, including forming technology. As experts in the field of material characterization, modeling and simulation of forming processes, we investigate the use of machine learning in solving common and future problems in industry. The main advantage of machine learning over numerical simulations is its ability to provide real-time predictions. We use this to solve typical optimization problems in a time-efficient manner, such as the calibration of material models. We also use machine learning to solve more complex problems, such as the design of materials and processes. Moreover, machine learning enables us to digitally represent processes and components based on numerical simulations in real-time including their complex material behavior. For the development of machine learning models we use experimental data as well as simulation data and rely on the integration of expert knowledge.
Our research in the field of machine learning concentrates on