A Physics-Informed Two-Level Machine-Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

Abstract

Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine-learning (ML) model to predict the melt-pool size during the scanning of a multitrack build. To account for the effect of thermal history on melt-pool size, a so-called (prescan) initial temperature is predicted at the lower-level of the modeling architecture and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the autodesk's netfabb simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance, and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.