Aiming for Modeling-Assisted Tailored Designs for Additive Manufacturing

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