BAYESIAN MODEL UPDATING FOR LASER POWDER BED FUSION (LPBF) ADDITIVE MANUFACTURING: UNCERTAINTY QUANTIFICATION OF HEAT SOURCE AND MATERIAL PARAMETERS

Numerical simulation of metal additive manufacturing processes is a key technology for revealing the intrinsic relationship between process parameters, melt pool dynamics, and forming quality, thereby enabling controlled shaping and property preservation during fabrication. Due to the complexity of physical processes such as heat conduction and the limited availability of experimental data, various sources of uncertainty inevitably exist, casting doubt on the predictive accuracy of models. To enhance the predictive accuracy of heat transfer models and fully quantify model parameter uncertainties, this paper addresses the challenge of high-fidelity simulation modelling for the heat transfer process in Laser Powder Bed Fusion (LPBF). A Bayesian correction method for heat transfer models is developed, quantifying uncertainties in heat source and material parameters based on melt