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



Yan Lu, Zhuo Yang, Paul W. Witherell, Wentao Yan, Kevontrez Jones, Gregory Wagner, Wing-Kam Liu, Jason C. Fox


Computational modeling for additive manufacturing has proven to be a powerful tool to understand the 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, from high-fidelity thermal-fluid ow model resolving individual powder particles, to low-fidelity heat transfer model simplifying powder bed as a continuum material, and to an analytical thermal model using a point heat source model, are run with a variety of manufacturing process parameters, while experiments are performed on the NIST Additive Manufacturing Metrology Testbed (AMMT) to validate the models. The cross-comparison of the simulation results reveals the remarkable influence of fluid flow, while the powder layer possesses varied significance to different models. A data analytics-based methodology is utilized to characterize the models to estimate the error distribution. This study aims to provide guidance on model selection and the corresponding accuracy, and more importantly facilitate the development and integration of AM models.
Additive Manufacturing


Uncertainty quantication, Computational model, Modeling assumption, Powder bed, Additive manufacturing, Data analytics


Lu, Y. , Yang, Z. , Witherell, P. , Yan, W. , Jones, K. , Wagner, G. , Liu, W. and Fox, J. (2020), Data-driven characterization of computational models for powder-bed-fusion additive manufacturing, Additive Manufacturing, [online], (Accessed May 17, 2024)


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Created July 30, 2020, Updated October 3, 2020