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：激光焊接的高保真仿真能够捕捉复杂的热-流体现象，包括相变、自由表面变形与小孔动力学，但其计算成本限制了大规模工艺探索与实时应用。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，可从FLOW-3D WELD（注册商标）生成的多物理场仿真数据中学习多种激光工艺的参数化解算子。通过将瞬态问题重构于移动激光坐标系并应用时间平均的新方法，系统转化为适于算子学习的准稳态设定，即使在小孔焊接区域亦适用。所提出的LP-FNO采用无量纲归一化焓公式，将工艺参数映射到跨越传导与小孔区域的宽工艺窗口内的三维温度场与熔池边界。该模型实现了约1%量级的温度预测误差，以及超过0.9的熔池分割交并比得分。我们证明，在粗分辨率数据上训练的LP-FNO模型可在更细网格上进行评估，在网格收敛的传导区域产生精确的超分辨率预测，而小孔区域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为激光焊接提供了一个高效的代理建模框架，能够在数十毫秒内预测宽参数范围内的完整三维场与相界面，其速度比传统有限体积多物理场软件快达十万倍。