Identifying Additive Manufacturing Process Windows by Artificial Intelligence

The project aims at developing a new method, which potentially leads to the accelerated establishment of new alloys for additive manufacturing.

Contact: Prof. Dr. Rolf Drechsler, Dr. Sebastian Huhn

Selective laser melting (SLM) is a technique that allows the production of parts and products with increased degrees of freedom not only regarding their geometry but also their material. While classical casting processes may limit possible alloy compositions, SLM processes are rapid solidification processes with cooling rates in the range of 10.000 to 100.000 K/s.

Today, quite a big share of the material potential remains unrevealed since only a few classical cast alloys are commercially available for SLM. A major obstacle is the effort involved in identifying suitable process windows for novel alloys. The development of an alloy and the testing of its processability must take place in a resource-intensive interaction.

In SLM, various typical defects occur depending on the selected process parameters. In addition to solidification cracks, these may be pores or incompletely melted particles. The assignment of these defect structures to the process parameters and also the interaction of different process parameters on these defects is not yet fully understood. Thus, the identification of optimized process parameters is still a manual, iterative process.