Modeling and prediction of 316 L stainless steel relative density in L-PBF process using machine learning (opens in new tab)

This study investigates the prediction of relative density in L‑PBF parts using machine learning models trained on a literature-based dataset comprising 287 experimental measurements collected from previously published studies. The input parameters for the proposed models included laser power, scanning speed, and hatch spacing. To model the nonlinear relationship between process variables and relative density, k-nearest neighbours (KNN), adaptive boosting decision trees (AdaBoost-DT), and fou...