Various sources of uncertainty can arise in metal forming processes, or their numerical simulation, or both, such as uncertainty in material behavior, process conditions, and geometry. Methods from the domain of uncertainty quantification can help assess the impact of such uncertainty on metal forming processes and their numerical simulation, and they can thus help improve robustness and predictive accuracy. In this paper, we compare stochastic methods and interval methods, two classes of methods receiving broad attention in the domain of uncertainty quantification, through their application to a numerical simulation of a sheet metal forming process.
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@article{CRMECA_2018__346_8_634_0,
author = {Maarten Arnst and Jean-Philippe Ponthot and Romain Boman},
title = {Comparison of stochastic and interval methods for uncertainty quantification of metal forming processes},
journal = {Comptes Rendus. M\'ecanique},
pages = {634--646},
publisher = {Elsevier},
volume = {346},
number = {8},
year = {2018},
doi = {10.1016/j.crme.2018.06.007},
language = {en},
}
TY - JOUR AU - Maarten Arnst AU - Jean-Philippe Ponthot AU - Romain Boman TI - Comparison of stochastic and interval methods for uncertainty quantification of metal forming processes JO - Comptes Rendus. Mécanique PY - 2018 SP - 634 EP - 646 VL - 346 IS - 8 PB - Elsevier DO - 10.1016/j.crme.2018.06.007 LA - en ID - CRMECA_2018__346_8_634_0 ER -
%0 Journal Article %A Maarten Arnst %A Jean-Philippe Ponthot %A Romain Boman %T Comparison of stochastic and interval methods for uncertainty quantification of metal forming processes %J Comptes Rendus. Mécanique %D 2018 %P 634-646 %V 346 %N 8 %I Elsevier %R 10.1016/j.crme.2018.06.007 %G en %F CRMECA_2018__346_8_634_0
Maarten Arnst; Jean-Philippe Ponthot; Romain Boman. Comparison of stochastic and interval methods for uncertainty quantification of metal forming processes. Comptes Rendus. Mécanique, Volume 346 (2018) no. 8, pp. 634-646. doi : 10.1016/j.crme.2018.06.007. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2018.06.007/
[1] Convex Models of Uncertainty in Applied Mechanics, Elsevier, Amsterdam, The Netherlands, 1990
[2] Stochastic Finite Elements: A Spectral Approach, Dover Publications, 2003
[3] A survey of non-probabilistic uncertainty treatment in finite element analysis, Comput. Methods Appl. Mech. Eng., Volume 194 (2005), pp. 1527-1555
[4] Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics, Springer, 2010
[5] Numerical Methods for Stochastic Computations: A Spectral Method Approach, Princeton University Press, 2010
[6] Non-probabilistic finite element analysis for parametric uncertainty treatment in applied mechanics: recent advances, Finite Elem. Anal. Des., Volume 47 (2011), pp. 4-16 | DOI
[7] Stochastic Systems: Uncertainty Quantification and Propagation, Springer-Verlag, 2012
[8] Uncertainty Quantification: Theory, Implementation, and Applications, SIAM, 2013
[9] An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics, Int. J. Uncertain. Quantificat., Volume 4 (2014), pp. 387-421
[10] Introduction to Uncertainty Quantification, Springer, 2015
[11] Handbook of Uncertainty Quantification, Springer, 2017
[12] Uncertainty Quantification, Springer, 2017
[13] Itô-SDE MCMC method for Bayesian characterization of errors associated with data limitations in stochastic expansion methods for uncertainty quantification, J. Comput. Phys., Volume 349 (2017), pp. 59-79
[14] Real Analysis and Probability, Cambridge University Press, Cambridge, United Kingdom, 2002
[15] Statistical Inference, 2002 (Duxbury, Pacific Grove, California)
[16] Global Sensitivity Analysis: The Primer, Wiley, West Sussex, United Kingdom, 2008
[17] Monte Carlo Statistical Methods, Springer, New York, 2010
[18] Gaussian Processes for Machine Learning, The MIT Press, 2006
[19] Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Syst. Saf., Volume 93 (2008), pp. 964-979 | DOI
[20] Polynomial chaos expansion for sensitivity analysis, Reliab. Eng. Syst. Saf., Volume 94 (2009), pp. 1161-1172 | DOI
[21] Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simul., Volume 55 (2001), pp. 271-280 | DOI
[22] Traitement unifié de la mécanique des milieux continus solides en grandes deformations par la méthode des éléments finis, University of Liège, Liège, Belgium, 1995 (Ph.D. thesis)
[23] http://metafor.ltas.ulg.ac.be/ (Metafor, website)
[24] Numerical simulation of springback using enhanced assumed strain elements, Finite Elem. Anal. Des., Volume 153–154 (2004), pp. 314-318 | DOI
[25] Statistical inference with bivariate gamma distributions, Biometrika, Volume 56 (1969), pp. 627-634 http://www.jstor.org/stable/2334670
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