This study presents novel predictive models using Graph Neural Networks (GNNs) for simulating thermal dynamics in Laser Powder Bed Fusion (L-PBF) processes. By developing and validating Single-Laser GNN (SL-GNN) and Multi-Laser GNN (ML-GNN) surrogates, the research introduces a scalable data-driven approach that learns fundamental physics from small-scale Finite Element Analysis (FEA) simulations and applies them to larger domains. Achieving a Mean Absolute Percentage Error (MAPE) of 3.77% with the baseline SL-GNN model, GNNs effectively learn from high-resolution simulations and generalize well across larger geometries. The proposed models capture the complexity of the heat transfer process in L-PBF while significantly reducing computational costs. For example, a thermomechanical simulation for a 2 mm x 2 mm domain typically requires about 4 hours, whereas the SL-GNN model can predict thermal distributions almost instantly. Calibrating models to larger domains enhances predictive performance, with significant drops in MAPE for 3 mm x 3 mm and 4 mm x 4 mm domains, highlighting the scalability and efficiency of this approach. Additionally, models show a decreasing trend in Root Mean Square Error (RMSE) when tuned to larger domains, suggesting potential for becoming geometry-agnostic. The interaction of multiple lasers complicates heat transfer, necessitating larger model architectures and advanced feature engineering. Using hyperparameters from Gaussian process-based Bayesian optimization, the best ML-GNN model demonstrates a 46.4% improvement in MAPE over the baseline ML-GNN model. In summary, this approach enables more efficient and flexible predictive modeling in L-PBF additive manufacturing.

翻译：本研究提出了利用图神经网络（GNN）模拟激光粉末床熔融（L-PBF）工艺热动力学的新型预测模型。通过开发并验证单激光GNN（SL-GNN）与多激光GNN（ML-GNN）代理模型，该研究引入了一种可扩展的数据驱动方法，该方法从小尺度有限元分析（FEA）仿真中学习基础物理规律，并将其应用于更大尺度域。基准SL-GNN模型实现了3.77%的平均绝对百分比误差（MAPE），表明GNN能有效从高分辨率仿真中学习，并在更大几何结构上表现出良好的泛化能力。所提模型在显著降低计算成本的同时，准确捕捉了L-PBF中传热过程的复杂性。例如，针对2 mm × 2 mm区域的热力学仿真通常需要约4小时，而SL-GNN模型几乎能即时预测温度分布。将模型校准至更大尺度域可提升预测性能，在3 mm × 3 mm和4 mm × 4 mm区域中MAPE显著下降，凸显了该方法的可扩展性与高效性。此外，当模型针对更大尺度域调优时，均方根误差（RMSE）呈现下降趋势，表明其具备发展为几何无关模型的潜力。多激光相互作用使传热过程复杂化，需要更大的模型架构与先进的特征工程。采用基于高斯过程的贝叶斯优化超参数后，最优ML-GNN模型相较于基准ML-GNN模型的MAPE提升了46.4%。总之，该方法为L-PBF增材制造实现了更高效灵活的预测建模。