Assessing Volumetric Energy Density as a Predictor of Defects in Laser Powder Bed Fusion 316L Stainless Steel

Abstract Minimizing porosity is a common challenge in powder bed fusion-laser bed (PBF-LB), so predictive modeling to enable parameter selection free of porosity is of great value. Porosity formation may occur through several mechanisms, include keyholing and lack of fusion. Volumetric energy density is often used in the literature to predict defect formation. However, volumetric energy density does not account for the various mechanisms by which porosity forms. In this work, nine LPBF parameter sets spanning variation in laser power, scanning velocity, and hatch spacing, all with the same volumetric energy density, are evaluated with 316L stainless steel. It was found that there are systematic variations in the type and amount of pores between these parameter sets that have the same volumetric density. We show that defect maps comprised of analytical models for defect formation can predict parameter sets with minimal porosity. A modified interpass lack-of-fusion (LOF) porosity criteria and a new spatter-induced intrapass LOF criteria are proposed to improve predictions at low laser powers and scanning velocities, and at high laser powers and scanning velocities, respectively. The results of this work are expected to help accelerate parameter selection for laser powder bed fusion 316L with minimal porosity defects.