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This article presents a data-driven approach using a Gaussian Process Regression (GPR) model to predict temperature fields in the Powder Bed Fusion (PBF) process, addressing the high computational costs associated with traditional Finite Element Analysis (FEA). The study demonstrates that the GPR model can accurately predict thermal distributions and melt pool dimensions in significantly less time than FEA, validated through experiments with Inconel 625. The findings highlight the influence of process param

Author

lili loulou

Language

EN