Review of Recent Additive Manufacturing and Welding Research with Application of Physics-Informed Neural Networks

Abstract

This review introduces recent research on applying physics-informed neural networks (PINNs) to additive manufacturing and welding. PINNs, which are artificial intelligence models, integrate governing equations containing physical information with artificial neural networks, enabling the modeling of complex physical phenomena at a lower computational cost than traditional numerical models. Although PINNs have been employed in a limited number of studies on welding processes, they have been extensively used in various studies within the field of additive manufacturing. This study reviews the theoretical background of PINNs to explore their effective application to welding processes, examining 12 research cases in additive manufacturing and two research cases in welding processes. The analysis included the structure of the PINN, governing equations, and prediction results of each study. Results indicate that PINNs provide faster computation speeds and higher prediction accuracies than numerical models. Moreover, they could perform analyses without additional training even when process parameters and materials changed. Additionally, PINNs have been effectively applied to predict the mechanical properties of the molten zone. Consequently, PINNs are anticipated to be actively applied in future research on welding process modeling and mechanical property prediction.