High-fidelity simulations of laser welding capture complex thermo-fluid phenomena, including phase change, free-surface deformation, and keyhole dynamics, however their computational cost limits large-scale process exploration and real-time use. In this work we present the Laser Processing Fourier Neural Operator (LP-FNO), a Fourier Neural Operator (FNO) based surrogate model that learns the parametric solution operator of various laser processes from multiphysics simulations generated with FLOW-3D WELD (registered trademark). Through a novel approach of reformulating the transient problem in the moving laser frame and applying temporal averaging, the system results in a quasi-steady state setting suitable for operator learning, even in the keyhole welding regime. The proposed LP-FNO maps process parameters to three-dimensional temperature fields and melt-pool boundaries across a broad process window spanning conduction and keyhole regimes using the non-dimensional normalized enthalpy formulation. The model achieves temperature prediction errors on the order of 1% and intersection-over-union scores for melt-pool segmentation over 0.9. We demonstrate that a LP-FNO model trained on coarse-resolution data can be evaluated on finer grids, yielding accurate super-resolved predictions in mesh-converged conduction regimes, whereas discrepancies in keyhole regimes reflect unresolved dynamics in the coarse-mesh training data. These results indicate that the LP-FNO provides an efficient surrogate modeling framework for laser welding, enabling prediction of full three-dimensional fields and phase interfaces over wide parameter ranges in just tens of milliseconds, up to a hundred thousand times faster than traditional Finite Volume multi-physics software.

翻译：高保真激光焊接仿真能够捕捉复杂的多相流热物理现象，包括相变、自由表面变形及匙孔动力学，但其计算成本限制了大规模工艺探索和实时应用。本文提出激光加工傅里叶神经算子（LP-FNO），这是一种基于傅里叶神经算子（FNO）的代理模型，通过学习由FLOW-3D WELD（注册商标）生成的多物理场仿真数据，获取各类激光工艺的参数解算子。通过创新性地将瞬态问题重构至移动激光参考系并应用时间平均，系统形成适用于算子学习的准稳态设定，即使在匙孔焊接条件下亦能成立。所提出的LP-FNO利用无量纲归一化焓公式，将工艺参数映射至跨传导与匙孔模式宽工艺窗口的三维温度场及熔池边界。模型实现了温度预测误差约1%以及熔池分割的交并比超过0.9。研究表明，基于粗网格数据训练的LP-FNO模型可在更细网格上评估，在网格收敛的传导模式下获得精确的超分辨率预测，而在匙孔模式下出现的差异则反映了粗网格训练数据中未解析的动力学特征。这些结果表明，LP-FNO为激光焊接提供了高效的代理建模框架，能在几十毫秒内完成宽参数范围的全三维场及相界面预测，速度比传统有限体积多物理场仿真软件快达十万倍。