Data-driven characterization of thermal models for powder-bed-fusion additive manufacturing

Computational modeling for additive manufacturing has proven to be a powerful tool to understand physical mechanisms, predict fabrication quality, and guide design and optimization. Varieties of models have been developed with different assumptions and purposes, and these models are sometimes difficult to choose from, especially for end-users, due to the lack of quantitative comparison and standardization. Thus, this study is focused on quantifying model uncertainty due to the modeling assumptions, and evaluating differences based on whether or not selected physical factors are incorporated. Multiple models with different assumptions, including a high-fidelity thermal-fluid flow model resolving individual powder particles, a low-fidelity heat transfer model simplifying the powder bed as a continuum material, and a semi-analytical thermal model using a point heat source model, were run with a variety of manufacturing process parameters. Experiments were performed on the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT) to validate the models. A data analytics-based methodology was utilized to characterize the models to estimate the error distribution. The cross comparison of the simulation results reveals the remarkable influence of fluid flow, while the significance of the powder layer varies across different models. This study aims to provide guidance on model selection and corresponding accuracy, and more importantly facilitate the development of AM models.