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research-article

Validation of a Laser-Based Powder Bed Fusion thermal model via Uncertainty Propagation and generalized Polynomial Chaos Expansions

[+] Author and Article Information
Gustavo Tapia

Industrial and Systems Engineering Department, Texas A&M University, College Station, TX 77843
gtapia@tamu.edu

Wayne E. King

Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550
weking@llnl.gov

Raymundo Arroyave

Materials Science and Engineering Department, Texas A&M University, College Station, TX 77843
rarroyave@tamu.edu

Luke Johnson

Materials Science and Engineering Department, Texas A&M University, College Station, TX 77843
lukejohnson@tamu.edu

Ibrahim Karaman

Materials Science and Engineering Department, Texas A&M University, College Station, TX 77843
ikaraman@tamu.edu

Alaa Elwany

Industrial and Systems Engineering Department, Texas A&M University, College Station, TX 77843
elwany@tamu.edu

1Corresponding author.

ASME doi:10.1115/1.4041179 History: Received November 08, 2017; Revised July 30, 2018

Abstract

Computational models for simulating physical phenomena during laser-based powder bed fusion ad- ditive manufacturing (L-PBF AM) processes are essential for enhancing our understanding of these processes, 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 is a broad field that focuses on characterizing such uncertain- ties in order to maximize the benefit of these models. Although uncertainty quantification has been a center theme in computational models associated with diverse fields such as computational fluid dynam- ics and macro-economics, it has not yet been fully exploited with computational models for advanced manufacturing. The current study presents one among the first efforts to conduct uncertainty propagation analysis in the context of L-PBF AM. More specifically, we present a generalized Polynomial Chaos Expansions framework to assess the variability in melt pool predictions due to uncertainty in input model parameters. We develop the methodology and then employ it to validate model predictions, both through benchmark- ing them against Monte Carlo methods and against experimental data acquired from an experimental testbed.

Copyright (c) 2018 by ASME
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