On Characterizing Uncertainty Sources in Laser Powder Bed Fusion Additive Manufacturing Models
Tesfaye M. Moges, Paul W. Witherell, Gaurav Ameta
Tremendous effort has been dedicated to computational models and simulations of Additive Manufacturing (AM) processes to better understand process complexities and better realize high-quality parts. However, understanding whether a model is an acceptable representation for a given scenario is a difficult proposition. With metals, the laser powder bed fusion (L-PBF) process involves complex physical phenomena such as powder packing, heat transfer, phase transformation, and fluid flow. Models based on these phenomena will possess different degrees of fidelity as they often rely on assumptions that may neglect or simplify process physics, resulting in uncertainty in their prediction accuracy. Predictive uncertainty and its characterization can vary greatly between models. This paper characterizes sources of L-PBF model uncertainty, including those due to modeling assumptions (model form uncertainty), numerical approximation (numerical uncertainty), and model input parameters (input parameter uncertainty) for low and high-fidelity models. The characterization of input uncertainty in terms of probability density function (PDF) and its propagation through L-PBF models, is discussed in detail. The systematic representation of such uncertainty sources is achieved by leveraging the Web Ontology Language (OWL) to capture relevant knowledge used for interoperability and reusability. The topology and mapping of the uncertainty sources establish fundamental requirements for measuring model fidelity and guiding the selection of a model suitable for its intended purpose.
Proceedings of the ASME 2019
November 11-14, 2019
Salt Lake City, UT
International Mechanical Engineering Congress and Exposition (IMECE2019)
, Witherell, P.
and Ameta, G.
On Characterizing Uncertainty Sources in Laser Powder Bed Fusion Additive Manufacturing Models, Proceedings of the ASME 2019, Salt Lake City, UT, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928087
(Accessed May 29, 2023)