Factorial design analytics on effects of material parameter uncertainties in multiphysics modeling of additive manufacturing

Abstract

A bottleneck in Laser Powder Bed Fusion (L-PBF) metal additive manufacturing (AM) is the quality inconsistency of its products. To address this issue without costly experimentation, computational multi-physics modeling has been used, but the effectiveness is limited by parameter uncertainties and their interactions. We propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics models. Data is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material parameters. Crucial physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge, which are used to establish a validation checkpoint for our study. Initial data visualization with half-normal probability plots, interaction plots and standard deviation plots, is used to assess if the checkpoint is being met. We then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable selection. Analytics yield statistically and phyiscally validated findings with practical implications, emphasizing the importance of parameter interactions under uncertainty, and their relation to the underlying physics of L-PBF.