On Characterizing Uncertainty Sources in Laser Powder-Bed Fusion Additive Manufacturing Models

Abstract

Tremendous efforts have been made to use computational models of, and simulation models of, Additive Manufacturing (AM) processes. The goals of these efforts are to better understand process complexities and to realize better, high-quality parts. However, understanding whether any model is a correct representation for a given scenario is a difficult proposition. For example, when using metal powders, the laser powder bed fusion (L-PBF) process involves complex physical phenomena such as powder morphology, heat transfer, phase transformation, and fluid flow. Models based on these phenomena will possess different degrees of fidelity since they often rely on assumptions that may neglect or simplify process physics, resulting in uncertainties in their prediction accuracy. Predictive accuracy and its characterization can vary greatly between models due to their uncertainties. This paper characterizes several sources of L-PBF model uncertainty for low, medium, and high-fidelity thermal models including modeling assumptions (model-form uncertainty), numerical approximations (numerical uncertainty), and input parameters (parameter uncertainty). This paper focuses on the input uncertainty sources, which we model in terms of a probability density function (PDF), and its propagation through all other L-PBF models. We represent uncertainty sources using the Web Ontology Language (OWL), which allows us to capture the relevant knowledge used for interoperability and reusability. The topology and mapping of the uncertainty sources establish fundamental requirements for measuring model fidelity and for guiding the selection of a model suitable for its intended purpose.