Efficient simulation of Laser Powder Bed Fusion (LPBF) is crucial for process prediction due to the lasting issue of high computation cost using traditional numerical methods such as finite element analysis (FEA). This study presents an efficient modeling framework termed FEA-Regulated Physics-Informed Neural Network (FEA-PINN) to accelerate the thermal field prediction in a LPBF process while maintaining the FEA accuracy. A novel dynamic material updating strategy is developed to capture the dynamic phase change of powder-liquid-solid in the PINN model. The PINN model incorporates temperature-dependent material properties and phase change behavior using the apparent heat capacity method. While the PINN model demonstrates high accuracy with a small training data and enables generalization of new process parameters via transfer learning, it faces the challenge of high computation cost in time-dependent problems due to the residual accumulation. To overcome this issue, the FEA-PINN framework integrates corrective FEA simulations during inference to enforce physical consistency and reduce error drift. A comparative analysis shows that FEA-PINN achieves equivalent accuracy to FEA while significantly reducing computational cost. The framework has been validated using the benchmark FEA data and demonstrated through single-track scanning in LPBF.

翻译：由于采用传统数值方法（如有限元分析）存在计算成本高昂的长期问题，激光粉末床熔融（LPBF）的高效模拟对于工艺预测至关重要。本研究提出了一种高效建模框架，称为有限元分析调控的物理信息神经网络（FEA-PINN），以在保持有限元分析精度的同时，加速LPBF过程中的热场预测。该研究开发了一种新颖的动态材料更新策略，以在PINN模型中捕捉粉末-液体-固体的动态相变。PINN模型采用表观热容法，结合了温度相关的材料属性和相变行为。虽然PINN模型在少量训练数据下表现出高精度，并能通过迁移学习泛化到新的工艺参数，但由于残差累积，其在时间相关问题中面临高计算成本的挑战。为克服此问题，FEA-PINN框架在推理过程中集成了校正性有限元分析模拟，以强化物理一致性并减少误差漂移。对比分析表明，FEA-PINN在显著降低计算成本的同时，达到了与有限元分析相当的精度。该框架已使用基准有限元分析数据进行了验证，并通过LPBF中的单道扫描进行了演示。