Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by integrating models of varying complexity, that operate on heterogeneous input spaces. The first thermal model incorporates five input parameters i.e., laser power, scan velocity, powder flow rate, carrier gas flow rate, and nozzle height. In contrast, the second thermal model can only handle laser power and scan velocity. A mapping is established between the heterogeneous input spaces so that the five-dimensional space can be morphed into a pseudo two-dimensional space. Predictions are then blended using a Gaussian process-based co-kriging method. The resulting heterogeneous multi-fidelity Gaussian process (Het-MFGP) surrogate not only improves predictive accuracy but also offers computational efficiency by reducing evaluations required from the high-dimensional, high-fidelity thermal model. The results underscore the benefits of employing Het-MFGP for modeling melt pool behavior in L-DED. The framework successfully demonstrates how to leverage multimodal data and handle scenarios where certain input parameters may be difficult to model or measure.
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）
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）
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）
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）
