Applied machine learning for rapid alloy development

Kurzfassung

Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. One major challenge is driven by electrical vehicles replacing combustion engines: It is expected that the amount of aluminium scrap alloys from conventional automotive sector increases tremendously in the near-future [2]. Such alloys are very complex (often > 10 alloying elements) and are highly specialised for their current application. Consequently, to keep the metals in the materials life cycle, fast design of new alloys with different materials properties is mandatory. In our work, we demonstrate how machine learning can be applied to boost the exploration of the whole alloy design space. We demonstrate that the total time needed to find promising candidate compositions for a set of target properties can be reduced to a minimum using the combination of CALPHAD, thermo-physical modelling, and machine learning.