Optimization of process parameters at part-scale in L-PBF process of Fe-Si soft magnetic alloys

Abstract

In the laser powder bed fusion (L-PBF) process, since the combination of process parameters causes defects or affects performance of products, studies have been conducted recently to find the optimal process parameters. However, while the optimal process parameters vary depending on the geometry of the product, most studies have optimized process parameters only at the cube or specimen scale. In this study, the method for optimizing process parameters applicable to part scale is proposed. In this method, in order to minimize the number of experiments for part scale products, the process area is narrowed in the order of cube, specimen, and part, and a data driven machine learning model is constructed using the latin hypercube sampling (LHS) and constrained sampling method. Applying proposed method to the stator of an axial flux motor made of Fe-4.5Si material, it shows that it is the optimization method applicable to part scale.