OptimUm

In high-paced industrial forming of sheet metals, process parameters are usually not optimized relative to the product properties. The aim of this joint research project is to stabilize production lines within the cold forming of sheet metals through proactive control of the machine parameters. This concept combines data-driven statistical (machine learning) and process-driven physical models (FEM) in order to map the behavior of the forming process and thus dynamically adjust the plant parameters.

The material behavior is estimated by FEM calculations and combined with machine parameters and historical quality data. In this manner, the behaviour of intrinsic material properties can be estimated in a model and the system learns which parameter combinations lead to a robust production process despite fluctuating process conditions (e.g. degree of oiling, geometry, roughness, etc.) and how it can avoid errors.

The project is carried out together with Selmatec Systems GmbH and Gestamp Automoción S.L., and is funded by the European Fund for Regional Development.