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 »
- Authors:
-
- Texas A & M Univ., College Station, TX (United States)
- 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. https://doi.org/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. https://doi.org/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 = {Fri Oct 05 00:00:00 EDT 2018},
month = {Fri Oct 05 00:00:00 EDT 2018}
}
Web of Science
Figures / Tables:
Works referenced in this record:
An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis
journal, April 2010
- Blatman, Géraud; Sudret, Bruno
- Probabilistic Engineering Mechanics, Vol. 25, Issue 2
Variance Components and Generalized Sobol' Indices
journal, January 2013
- Owen, Art B.
- SIAM/ASA Journal on Uncertainty Quantification, Vol. 1, Issue 1
Mesoscale modelling of selective laser melting: Thermal fluid dynamics and microstructural evolution
journal, January 2017
- Panwisawas, Chinnapat; Qiu, Chunlei; Anderson, Magnus J.
- Computational Materials Science, Vol. 126
Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
journal, January 2009
- Najm, Habib N.
- Annual Review of Fluid Mechanics, Vol. 41, Issue 1
Polynomial Chaos-Based Analysis of Probabilistic Uncertainty in Hypersonic Flight Dynamics
journal, January 2010
- Prabhakar, Avinash; Fisher, James; Bhattacharya, Raktim
- Journal of Guidance, Control, and Dynamics, Vol. 33, Issue 1
Uncertainty propagation in CFD using polynomial chaos decomposition
journal, September 2006
- Knio, O. M.; Le Maître, O. P.
- Fluid Dynamics Research, Vol. 38, Issue 9
ALGORITHM 659: implementing Sobol's quasirandom sequence generator
journal, March 1988
- Bratley, Paul; Fox, Bennett L.
- ACM Transactions on Mathematical Software, Vol. 14, Issue 1
Uncertainty quantification and validation of 3D lattice scaffolds for computer-aided biomedical applications
journal, July 2017
- Gorguluarslan, Recep M.; Choi, Seung-Kyum; Saldana, Christopher J.
- Journal of the Mechanical Behavior of Biomedical Materials, Vol. 71
Thermal behavior and densification mechanism during selective laser melting of copper matrix composites: Simulation and experiments
journal, March 2014
- Dai, Donghua; Gu, Dongdong
- Materials & Design, Vol. 55
Modeling metal deposition in heat transfer analyses of additive manufacturing processes
journal, September 2014
- Michaleris, Panagiotis
- Finite Elements in Analysis and Design, Vol. 86
The Homogeneous Chaos
journal, October 1938
- Wiener, Norbert
- American Journal of Mathematics, Vol. 60, Issue 4
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
journal, January 2002
- Xiu, Dongbin; Karniadakis, George Em
- SIAM Journal on Scientific Computing, Vol. 24, Issue 2
Integration of Design for Manufacturing Methods With Topology Optimization in Additive Manufacturing
journal, January 2017
- Ranjan, Rajit; Samant, Rutuja; Anand, Sam
- Journal of Manufacturing Science and Engineering, Vol. 139, Issue 6
Model Validation via Uncertainty Propagation and Data Transformations
journal, July 2004
- Chen, Wei; Baghdasaryan, Lusine; Buranathiti, Thaweepat
- AIAA Journal, Vol. 42, Issue 7
Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies
journal, May 2016
- Aboutaleb, Amir M.; Bian, Linkan; Elwany, Alaa
- IISE Transactions, Vol. 49, Issue 1
Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory
journal, November 2014
- King, W.; Anderson, A. T.; Ferencz, R. M.
- Materials Science and Technology, Vol. 31, Issue 8
Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones
journal, April 2016
- Khairallah, Saad A.; Anderson, Andrew T.; Rubenchik, Alexander
- Acta Materialia, Vol. 108
Global sensitivity analysis using polynomial chaos expansions
journal, July 2008
- Sudret, Bruno
- Reliability Engineering & System Safety, Vol. 93, Issue 7
In situ absorptivity measurements of metallic powders during laser powder-bed fusion additive manufacturing
journal, December 2017
- Trapp, Johannes; Rubenchik, Alexander M.; Guss, Gabe
- Applied Materials Today, Vol. 9
Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W
journal, May 2014
- Kamath, Chandrika; El-dasher, Bassem; Gallegos, Gilbert F.
- The International Journal of Advanced Manufacturing Technology, Vol. 74, Issue 1-4
Finite Element Simulation of Selective Laser Melting process considering Optical Penetration Depth of laser in powder bed
journal, January 2016
- Foroozmehr, Ali; Badrossamay, Mohsen; Foroozmehr, Ehsan
- Materials & Design, Vol. 89
Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models
journal, October 2016
- Tapia, G.; Elwany, A. H.; Sang, H.
- Additive Manufacturing, Vol. 12
Simulation of Laser Beam Melting of Steel Powders using the Three-Dimensional Volume of Fluid Method
journal, January 2013
- Gürtler, F. -J.; Karg, M.; Leitz, K. -H.
- Physics Procedia, Vol. 41
Solutions for modelling moving heat sources in a semi-infinite medium and applications to laser material processing
journal, November 2007
- Van Elsen, M.; Baelmans, M.; Mercelis, P.
- International Journal of Heat and Mass Transfer, Vol. 50, Issue 23-24
Numerical Modeling of Metal-Based Additive Manufacturing Using Level Set Methods
journal, April 2017
- Ye, Qian; Chen, Shikui
- Journal of Manufacturing Science and Engineering, Vol. 139, Issue 7
Investigations on Temperature Fields during Laser Beam Melting by Means of Process Monitoring and Multiscale Process Modelling
journal, January 2014
- Schilp, J.; Seidel, C.; Krauss, H.
- Advances in Mechanical Engineering, Vol. 6
Laser Transformation Hardening
journal, February 2002
- Ion, J. C.
- Surface Engineering, Vol. 18, Issue 1
Multiscale Modeling of Powder Bed–Based Additive Manufacturing
journal, July 2016
- Markl, Matthias; Körner, Carolin
- Annual Review of Materials Research, Vol. 46, Issue 1
In-Process Monitoring of Selective Laser Melting: Spatial Detection of Defects Via Image Data Analysis
journal, November 2016
- Grasso, Marco; Laguzza, Vittorio; Semeraro, Quirico
- Journal of Manufacturing Science and Engineering, Vol. 139, Issue 5
A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing
journal, October 2009
- Roberts, I. A.; Wang, C. J.; Esterlein, R.
- International Journal of Machine Tools and Manufacture, Vol. 49, Issue 12-13
Predicting Microstructure From Thermal History During Additive Manufacturing for Ti-6Al-4V
journal, June 2016
- Irwin, Jeff; Reutzel, Edward W.; Michaleris, Pan
- Journal of Manufacturing Science and Engineering, Vol. 138, Issue 11
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
journal, October 2014
- Tapia, Gustavo; Elwany, Alaa
- Journal of Manufacturing Science and Engineering, Vol. 136, Issue 6
Data mining and statistical inference in selective laser melting
journal, January 2016
- Kamath, Chandrika
- The International Journal of Advanced Manufacturing Technology, Vol. 86, Issue 5-8
Finite element analysis of single layer forming on metallic powder bed in rapid prototyping by selective laser processing
journal, January 2002
- Matsumoto, M.; Shiomi, M.; Osakada, K.
- International Journal of Machine Tools and Manufacture, Vol. 42, Issue 1
Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges
journal, December 2015
- King, W. E.; Anderson, A. T.; Ferencz, R. M.
- Applied Physics Reviews, Vol. 2, Issue 4
An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling
journal, October 2014
- Pal, Deepankar; Patil, Nachiket; Zeng, Kai
- Journal of Manufacturing Science and Engineering, Vol. 136, Issue 6
Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel
journal, September 2017
- Tapia, Gustavo; Khairallah, Saad; Matthews, Manyalibo
- The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12
Numerical and experimental analysis of heat distribution in the laser powder bed fusion of Ti-6Al-4V
journal, June 2018
- Karayagiz, Kubra; Elwany, Alaa; Tapia, Gustavo
- IISE Transactions, Vol. 51, Issue 2
3D FE simulation for temperature evolution in the selective laser sintering process
journal, February 2004
- Kolossov, S.; Boillat, E.; Glardon, R.
- International Journal of Machine Tools and Manufacture, Vol. 44, Issue 2-3
Bayesian calibration of computer models
journal, August 2001
- Kennedy, Marc C.; O'Hagan, Anthony
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3
Regularization and variable selection via the elastic net
journal, April 2005
- Zou, Hui; Hastie, Trevor
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, Issue 2
Works referencing / citing this record:
Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling
journal, June 2019
- Wang, Zhuo; Liu, Pengwei; Ji, Yanzhou
- JOM, Vol. 71, Issue 8
Uncertainty analysis of microsegregation during laser powder bed fusion
journal, February 2019
- Ghosh, Supriyo; Mahmoudi, Mohamad; Johnson, Luke
- Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 3
Uncertainty quantification of grain morphology in laser direct metal deposition
journal, April 2019
- Nath, Paromita; Hu, Zhen; Mahadevan, Sankaran
- Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 4
A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations
journal, February 2019
- Moges, Tesfaye; Ameta, Gaurav; Witherell, Paul
- Journal of Manufacturing Science and Engineering, Vol. 141, Issue 4
Chaotic Signatures Exhibited by Plasmonic Effects in Au Nanoparticles with Cells
journal, October 2019
- Martines-Arano, Hilario; García-Pérez, Blanca Estela; Vidales-Hurtado, Mónica Araceli
- Sensors, Vol. 19, Issue 21
Uncertainty Analysis of Microsegregation during Laser Powder Bed Fusion
text, January 2019
- Ghosh, Supriyo; Mahmoudi, Mohamad; Johnson, Luke
- arXiv
Figures / Tables found in this record: