Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions

Tapia, Gustavo; King, Wayne; Johnson, Luke; Arroyave, Raymundo; Karaman, Ibrahim; Elwany, Alaa

doi:10.1115/1.4041179

Title: Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions

Journal Article · Fri Oct 05 00:00:00 EDT 2018 · Journal of Manufacturing Science and Engineering

DOI:https://doi.org/10.1115/1.4041179· OSTI ID:1527295

Tapia, Gustavo ^[1]; King, Wayne ^[2]; Johnson, Luke ^[1]; Arroyave, Raymundo ^[1]; Karaman, Ibrahim ^[1]; Elwany, Alaa ^[1]

Texas A & M Univ., College Station, TX (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Computational models for simulating physical phenomena during laser-based powder bed fusion additive manufacturing (L-PBF AM) processes are critical for enhancing our understanding of these phenomena, enable process optimization, and accelerate qualification and certification of AM materials and parts. It is a well-known fact that such models typically involve multiple sources of uncertainty that originate from different sources such as model parameters uncertainty, or model/code inadequacy, among many others. Uncertainty quantification (UQ) is a broad field that focuses on characterizing such uncertainties in order to maximize the benefit of these models. Although UQ has been a center theme in computational models associated with diverse fields such as computational fluid dynamics and macro-economics, it has not yet been fully exploited with computational models for advanced manufacturing. The current study introduces one among the first efforts to conduct uncertainty propagation (UP) analysis in the context of L-PBF AM. More specifically, we present a generalized polynomial chaos expansions (gPCE) framework to assess the distributions of melt pool dimensions due to uncertainty in input model parameters. We develop the methodology and then employ it to validate model predictions, both through benchmarking them against Monte Carlo (MC) methods and against experimental data acquired from an experimental testbed.

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Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344; NNX15AD71G

OSTI ID:: 1527295

Report Number(s):: LLNL-JRNL-769893; 961399

Journal Information:: Journal of Manufacturing Science and Engineering, Vol. 140, Issue 12; ISSN 1087-1357

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 27 works

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