Analytical Probabilistic Modeling of Additive Manufacturing-Induced Process Defects and Experimental Validation

Additive manufacturing is an innovative set of technologies known for their design flexibility and manufacturing process efficiency. Laser powder bed fusion is one of the most popular and rapidly advancing techniques being applied to metal printing of components. This paper introduces an analytical physics-based model for probabilistic defect prediction in laser powder bed fusion. This model provides many unique and enabling capabilities, including calculating process-induced part-level defects directly from first principles, in contrast to empirical methods. Additionally, it offers exceptional computational efficiency compared to slower numerical methods, such as finite element analysis. The validity of this model is evaluated through experimental data through analysis and comparison with physical prints with measured porosity. This research paves the way for a model-based material definition, significantly enhancing process design, control, and the qualification and certification of components in additive manufacturing.