Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model

Abstract

In this work, a Gaussian process (GP)-based machine learning model is developed to predict the remelted depth of single tracks, as a function of combined laser power and laser scan speed in a laser powder bed fusion process. The GP model is trained by both simulation and experimental data from the literature. The mean absolute prediction error magnified by the GP model is only 0.6 μm for a powder bed with layer thickness of 30 μm, suggesting the adequacy of the GP model. Then, the process design maps of two metals, 316L and 17-4 PH stainless steels, are developed using the trained model. The normalized enthalpy criterion of identifying keyhole mode is evaluated for both stainless steels. For 316L, the result suggests that the $\frac{\Delta H}{{h_{s} }} \ge 30$ criterion should be related to the powder layer thickness. For 17-4 PH, the criterion should be revised to $\frac{\Delta H}{{h_{s} }} \ge 25$.