Grain structure predictions for metallic additive manufacturing processes

Abstract

Additive manufacturing has transformed the way we think about component fabrication. Generating a geometry in a layer-by-layer fashion presents many advantages over traditional subtractive methods, but also presents many challenges pertaining to the highly localised and energetic nature of the heat source. Since the material passes through multiple heating and cooling cycles throughout the build, some of which completely melt and erase the microstructure, a dynamic simulation is necessary to determine the grain structure that emerges. Grains are generally, but not exclusively, highly textured with columnar grains commonly spanning multiple layers. Fast, efficient and parallelised envelope cellular automata based models are used to simulate the nucleation and growth of the individual crystals that comprise the grain structure, with trade-offs being made between intra-grain detail and computational efficiency so that meso-scale simulations are possible. Simplified, but physically sound thermal models are used to predict the thermal conditions at the melt pool periphery, which are weakly coupled to the grain growth model. Dendrite tip kinetics models are used to determine alloy specific growth laws as a function of local undercooling. The effect of various processing parameters on as-solidified grain size, morphology and texture are investigated for aluminium alloys 3D printed by laser powder bed fusion.