Process parameter optimization plays a vital role in additive manufacturing, as the processing conditions can directly influence the quality of the synthesized materials and components. The current optimization methods involve a Design of Experiments (DoE) approach within a parameter window that emphasizes on three primary variables, viz., laser power, powder feed rate and scanning velocity. However, selection of the initial parameter window is itself a challenge and theory-guided methodologies, which can facilitate the baseline parameters, are sparse. We present results from a computational framework that is able to predict the process parameter window for advanced metals and alloys based on their solidification behavior, employing classical atomistic simulations of material properties (such as, diffusion coefficient, phase segregation) that are crucial during laser deposition. The predictive model correlates the atomic scale material features to the macroscopic manufacturing conditions.
Keywords: Metal Additive Manufacturing, Processing Parameters, Molecular Dynamics