Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning

Variation in the local thermal history during the laser powder bed fusion (LPBF) process in additive manufacturing (AM) can cause microporosity defects, which add to the uncertainty of the properties of the built materials. in-situ sensing has been proposed to monitor the AM process to minimize defects, but the success requires establishing a quantitative relationship between the sensing data and the porosity, which is especially challenging for a large number of variables. Physics-based modeling can simulate such a relationship, but they are computationally costly. In this work, we develop machine learning (ML) models that can use in-situ thermographic data to predict the microporosity of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold (τ) and the