A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing
Laboratory for Advanced Materials Processing, Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室先进材料加工实验室）；Terra Quantum AG（Terra Quantum公司）
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.
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion
Dept. of Mechanical and Aerospace Engineering, Rutgers University-New Brunswick（罗杰斯大学机械与航空航天工程系）；New Jersey Advanced Manufacturing Institute, Rutgers University-New Brunswick（新泽西先进制造研究所）
Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams
McGill University（麦吉尔大学）；National Research Council Canada（加拿大国家研究委员会）；Braintoy AI；Apollo Machine & Welding Ltd（阿波罗机器焊接有限公司）；Fraunhofer Institute for Laser Technology ILT（弗劳恩霍夫激光技术研究所ILT）；BCT GmbH（BCT有限公司）
Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach
Industrial and National Institute of National Institute of Industrial and Manufacturing Engineering Standards and Technology Standards and Technology Manufacturing Engineering（工业与国家研究院国家标准化与技术研究所制造工程）
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability
McGill University（麦吉尔大学）；Georgetown University（乔治城大学）；National Cheng Kung University（国立成功大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）
