A fast and accurate fourier neural operator-based surrogate for melt-pool prediction in laser processing

High-fidelity simulations of laser welding capture complex thermo-fluid phenomena, including phase change, free-surface deformation, and keyhole dynamics, but 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 laser processing from multiphysics simulations generated with FLOW-3D WELD ® . Through a reformulation of the transient problem in a reference frame moving with the laser and the application of temporal averaging, the system is recast into a quasi-steady setting suitable for operator learning, even in the stable 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 an average temperature relative error of approximately $$2.5%$$ 2.5 % and intersection-over-union scores for melt-pool segmentation above 0.9. Compared with a fully connected coordinate network, U-Net, and DeepONet, LP-FNO provides the strongest overall accuracy–efficiency trade-off for the present task, while retaining the resolution-invariant evaluation capability characteristic of FNOs. We demonstrate that an 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 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 multiphysics simulations.