Quantifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models and Simulations

Published

November 6, 2018

Author(s)

Tesfaye M. Moges, Wentao Yan, Stephen Lin, Gaurav Ameta, Jason C. Fox, Paul W. Witherell

Abstract

Various sources of uncertainty that can potentially cause variability in the product quality exist at different stages of the laser powder bed fusion (L-PBF) process. To implement computational models and simulations for quality control and process optimization, quantitative representation of their predictive accuracy is required. In this study, a methodology to estimate uncertainties in L-PBF models and simulations is presented. The sources of uncertainty, including those due to modeling assumptions, numerical approximation, input parameters, and measurement error, are discussed in detail and quantified for low and high-fidelity melt pool simulation models. A design of experiments (DOE) approach is leveraged to quantify uncertainty due to input parameters and investigate their effects on output quantities of interest (QoIs). The result of this work is essential for understanding the tradeoffs in model fidelity and guiding the selection of a model suitable for its intended purpose.

Proceedings Title

Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium

Conference Dates

August 13-15, 2018

Conference Location

Austin, TX

Conference Title

Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference

Pub Type

Conferences

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Keywords

Additive manufacturing, Powder bed fusion, Uncertainty quantification, Melt pool model, Design of experiment

Citation

Moges, T. , Yan, W. , Lin, S. , Ameta, G. , Fox, J. and Witherell, P. (2018), Quantifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models and Simulations, Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium, Austin, TX, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926423 (Accessed December 14, 2024)