0
Review Article

A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations

[+] Author and Article Information
Tesfaye Moges

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: tesfaye.moges@nist.gov

Gaurav Ameta

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: gaurav.ameta@nist.gov

Paul Witherell

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: paul.witherell@nist.gov

1Corresponding author.

Manuscript received July 16, 2018; final manuscript received December 19, 2018; published online February 28, 2019. Assoc. Editor: Kevin Chou. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Manuf. Sci. Eng 141(4), 040801 (Feb 28, 2019) (14 pages) Paper No: MANU-18-1543; doi: 10.1115/1.4042789 History: Received July 16, 2018; Accepted December 19, 2018

This paper presents a comprehensive review on the sources of model inaccuracy and parameter uncertainty in metal laser powder bed fusion (L-PBF) process. Metal additive manufacturing (AM) involves multiple physical phenomena and parameters that potentially affect the quality of the final part. To capture the dynamics and complexity of heat and phase transformations that exist in the metal L-PBF process, computational models and simulations ranging from low to high fidelity have been developed. Since it is difficult to incorporate all the physical phenomena encountered in the L-PBF process, computational models rely on assumptions that may neglect or simplify some physics of the process. Modeling assumptions and uncertainty play significant role in the predictive accuracy of such L-PBF models. In this study, sources of modeling inaccuracy at different stages of the process from powder bed formation to melting and solidification are reviewed. The sources of parameter uncertainty related to material properties and process parameters are also reviewed. The aim of this review is to support the development of an approach to quantify these sources of uncertainty in L-PBF models in the future. The quantification of uncertainty sources is necessary for understanding the tradeoffs in model fidelity and guiding the selection of a model suitable for its intended purpose.

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Figures

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Fig. 1

Overview of L-PBF process with different physical phenomena and process parameters

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Fig. 2

Input, output, and characteristics of powder bed models: raindrop algorithm [3740] and discrete element method [3236]

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Fig. 3

Inputs, outputs, and characteristics of solidification models: phase field method [105108] and cellular automaton [101,108111]

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Fig. 4

Inputs, outputs, and characteristics of residual stress models: simplified mathematical model [116] and thermomechanical FEM model [50,76,121124]

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Fig. 5

Uncertainty sources in a fishbone diagram for laser power, scan speed, and layer thickness

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Fig. 6

Uncertainty sources in a fishbone diagram for powder bed absorptivity, powder bed thermal conductivity, and powder bed emissivity

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Fig. 7

Cascading effect of uncertainty in L-PBF

Tables

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