Aiming for modeling-assisted tailored designs for additive manufacturing

Abstract

It is well recognized that there are gaps in knowledge on the strongly intertwined process–microstructure–property–performance relationships inherent in the metallic additive manufacturing processes. Computational modeling can assist with filling in some of these gaps by increasing in-depth understanding of these relationships and highlighting cause-and-effect. Additionally, it can capture the knowledge of materials scientists and engineers and apply established physics-based rules to simulate the processes and thus predict the final outcomes. Modeling can also help optimize processes. Some even predict that future generations of additive manufacturing machines will employ ‘model-assisted feed forward algorithms’ that would leapfrog feedback control methods. In the current article the authors describe the several computational efforts sponsored by CSIRO’s ‘Lab 22—Australia’s Centre for Additive Innovation’ aimed at modeling-assisted tailored design. The models in development, e.g. microstructure prediction (both fundamental and empirical), powder bed raking, and residual stress predictions, are described in some detail, and representative results are presented.