Porosity is a frequently observed defect in metal additive manufacturing due to the large thermal gradients caused by the repeated rapid melting and solidification. This work presents a physics-based model to predict the part porosity in powder bed metal additive manufacturing (PBMAM) per given process parameters, materials properties, powder size distribution. The molten pool dimensions were first calculated by a closed-form temperature solution considering the laser heat input and part boundary heat loss. The porosity evolution was calculated by subtracting the volume fraction of the preprocessed powder bed void from the postprocessed porosity. The volume fractions of powder and void in packed powder bed were calculated by advancing front approach (AFA) and image analysis considering powder size variation and statistical distribution. The porosity model was developed based on the correlation between molten pool dimensions and porosity evolution using regression analysis. Experimental validation of porosity values under different process conditions was included in PBMAM of Ti6Al4V. A close agreement was observed upon validation. Different from the previous statistical model, the presented model was developed based on the physical relationship between thermal behaviors porosity formation, which leads to an improved prediction accuracy with a small number of calibration data. Moreover, the short computational time of the closed-form temperature solution allows a fast porosity prediction for various processing window.

Keywords: porosity prediction, closed-formed temperature solution, powder size variation and packing, regression analysis, metal additive manufacturing

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