A multi-level capture algorithm for accelerating cellular automata predictions of grain structure and texture in additive manufacturing

Abstract

Microstructure features including grain morphology and texture are key factors in determining the properties of laser additively manufactured metallic components. Beyond the traditional trial-and-error approach, which is costly and time-consuming, microstructure control increasingly relies on predictions from mechanistic models. However, existing mechanistic models to predict microstructure and texture are computationally expensive. Here, we present a cellular automata solidification model, which is up to two orders-of-magnitude faster than traditional models. By analytically calculating growth length and utilizing a multi-level capture algorithm, a large time step can be employed without compromising simulation accuracy. The model is validated through simulations of 316L steel and three NiTi cases, showing good agreement with experimental results. Our findings reveal that preferential orientations are selected by the vertical and the inclined temperature gradients from multi-pass temperature profiles, leading to different microstructures and textures. Three-dimensional additive manufacturing simulations demonstrate that orientation-dependent growth patterns govern grain growth, leading to columnar, planar and spiral shaped grains. This approach offers a significant reduction in computational cost while maintaining accuracy, contributing to the practical application of microstructure control in additive manufacturing. The insights gained on grain and texture evolution pave the way for customized microstructure design through additive manufacturing.