Physics-informed deep learning of gas flow-melt pool multi-physical dynamics during powder bed fusion

The effect of inert gas on melt pool dynamics has been largely overlooked but is crucial for laser powder bed fusion (LPBF). Physics-based simulation models are computationally expensive while data-driven models lack transpar-ency and need massive training data. This work presents a physics-informed deep learning (PIDL) model to accu-rately predict the temperature and velocity fields in the melting domain using only a small training data. The PIDL model can also learn unknown model constants (e.g., Reynolds number and Peclet number) of the governing equa-tions. Furthermore, the robust PIDL algorithm converges very fast by enforcing physics via soft penalty constraints.& COPY; 2023 CIRP. Published by Elsevier Ltd. All rights reserved.