Parametric Analysis and Designing Maps for Powder Spreading in Metal Additive Manufacturing

Abstract

Powder bed fusion (PBF) in metallic additive manufacturing offers the ability to produce intricate geometries, high-strength components, and reliable products. However, powder processing before energy-based binding significantly impacts the final product’s integrity. Processing maps guide efficient process design to minimize defects, but creating them through experimentation alone is challenging due to the wide range of parameters, necessitating a comprehensive computational parametric analysis. In this study, we used the discrete element method to parametrically analyze the powder processing design space in PBF of stainless steel 316L powders. Uniform lattice parameter sweeps are often used for parametric analysis, but are computationally intensive. We find that non-uniform parameter sweep based on the low discrepancy sequence (LDS) algorithm is ten times more efficient at exploring the design space while accurately capturing the relationship between powder flow dynamics and bed packing density. We introduce a multi-layer perceptron (MLP) model to interpolate parametric causalities within the LDS parameter space. With over 99% accuracy, it effectively captures these causalities while requiring fewer simulations. Finally, we generate processing design maps for machine setups and powder selections for efficient process design. We find that recoating speed has the highest impact on powder processing quality, followed by recoating layer thickness, particle size, and inter-particle friction.