Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion

Abstract

Efficient simulation of Laser Powder Bed Fusion (LPBF) is crucial for process prediction due to the lasting issue of high computational cost associated with traditional numerical methods such as finite element analysis (FEA). While a Physics-Informed Neural Network (PINN) can predict solution fields with small training data and enables the generalization of new process parameters via transfer learning, it suffers from accuracy degradation in time-dependent problems due to the accumulation of residual and the difficulty in capturing the steep spatial and temporal gradients inherent in the LPBF process. To overcome this issue, this study develops an efficient modeling framework, FEA-Regulated Physics-Informed Neural Network (FEA-PINN), to accelerate the prediction of melt pool dynamics phenomena in an LPBF process while maintaining the FEA accuracy. The innovation of FEA-PINN manifested itself in two aspects. First, a novel strategy has been developed within the PINN model to capture the dynamic phase change of powder-liquid-solid, enabling the tracking of material status during laser melting. The model further incorporates temperature-dependent material properties, phase change behavior of the powder bed, Marangoni convection, and natural convection within the melt pool. Second, the FEA-PINN framework integrates corrective FEA simulations during inference to enforce physical consistency, reduce error drift, and capture the steep gradients. A comparative analysis shows that FEA-PINN achieves accuracy comparable to FEA while significantly reducing computational cost. The framework has been validated against benchmark FEA data for single-track scanning in LPBF.