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

Abstract

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 experimentalmore » testbed.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [1]
  1. Texas A & M Univ., College Station, TX (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1527295
Report Number(s):
LLNL-JRNL-769893
Journal ID: ISSN 1087-1357; 961399
Grant/Contract Number:  
AC52-07NA27344; NNX15AD71G
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Manufacturing Science and Engineering
Additional Journal Information:
Journal Volume: 140; Journal Issue: 12; Journal ID: ISSN 1087-1357
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Tapia, Gustavo, King, Wayne, Johnson, Luke, Arroyave, Raymundo, Karaman, Ibrahim, and Elwany, Alaa. Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions. United States: N. p., 2018. Web. doi:10.1115/1.4041179.
Tapia, Gustavo, King, Wayne, Johnson, Luke, Arroyave, Raymundo, Karaman, Ibrahim, & Elwany, Alaa. Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions. United States. doi:10.1115/1.4041179.
Tapia, Gustavo, King, Wayne, Johnson, Luke, Arroyave, Raymundo, Karaman, Ibrahim, and Elwany, Alaa. Fri . "Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions". United States. doi:10.1115/1.4041179. https://www.osti.gov/servlets/purl/1527295.
@article{osti_1527295,
title = {Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions},
author = {Tapia, Gustavo and King, Wayne and Johnson, Luke and Arroyave, Raymundo and Karaman, Ibrahim and Elwany, Alaa},
abstractNote = {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.},
doi = {10.1115/1.4041179},
journal = {Journal of Manufacturing Science and Engineering},
number = 12,
volume = 140,
place = {United States},
year = {2018},
month = {10}
}

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