Understanding grain evolution in additive manufacturing through modeling

Variability in the mechanical properties of additively manufactured metal parts is a key concern for their application in service. One of the parameters affecting the above-mentioned property is solidification texture which is driven by scan patterns and other process variables. Understanding of how these textures arise in the AM process can provide a pathway to control these features which ultimately decide the final structural material properties. In this work, a Cellular Automata (CA) based two-dimensional microstructure model is formulated and implemented to understand grain evolution in AM. Grain evolution in multilayer depositions using various scan patterns in Directed Energy Deposition (DED), Metal Laser Sintering/Selective Laser Melting (MLS/SLM), and Electron Beam Melting (EBM) is presented and qualitatively compared with reported literature. Results show strong correlation of scan patterns with evolving grain orientations. Variability in grain size and orientation evolution during SLM and EBM processing of metallic materials showed direct influence by exposure to different cooling rates and thermal gradients. The similarities between the simulated and reported results lead us to conclude CA based modeling for predicting grain orientation and size in metal AM processes is useful for prediction of continuum level structural properties at global and local length scales.