Previous work has shown that AI models can reliably predict simulation results for extrusion processes. However, the approach has so far focused on predicting individual target variables. For practical application, however, it is crucial to predict several relevant process variables simultaneously and to derive robust and economically viable process windows from them.

This work aims to expand the existing approach by developing models that take several result variables into account simultaneously. The goal is to use the predictions to identify optimal process windows in which component quality and process stability are guaranteed. This represents an important step towards holistic process optimisation in forming technology.

The work packages and the individual adaptation of the task will be determined in a personal interview. The applicant's personal interests and previous experience will be taken into account.

Research method

Experimental, theoretical, numerical