Innovative modeling strategies for additive manufacturing processes - iMAT

Purpose and goal:

iMAT aims to increase the competitiveness of the manufacturing industry by utilising advanced digital tools and systems to further develop sustainable additive manufacturing (AM) for challenging components. The industry´s need to efficiently design for AM according to the "first time right" principle forms the basis for the project´s research activities on Powder Bed Fusion-Laser Beam (PBF-LB). The ultimate goal is to enable the manufacturing of components that are free from design defects.

Expected results and effects:

The project intends to achieve predictability, or "predictive capability," of manufacturing-induced defects and deviations by developing numerical models. The numerical models and methods are verified and validated experimentally. The project also evaluates the application of Physics-Informed Machine Learning (PIML), which potentially strengthens the predictive capability further.

Approach and implementation:

By combining high-resolution models at a detailed level with full-scale models at a component level, we create the right conditions for sufficient computational accuracy. The project places great emphasis on generating validating data, from melt pool monitoring for instance. Activities connected to Physics-Informed Machine Learning (PIML) show great potential.