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Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications

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Abstract

The aim of this paper is to present a method to perform evolutionary multi-objective optimization of CPU intensive sheet metal forming applications using kriging surrogates. Two main ingredients are employed to achieve this goal. First of all, given a learning dataset, the kriging surrogate is designed to minimize the leave-one-out error. Secondly, during the optimization, new data points are added to the learning set to update the surrogate locally (by well chosen points on the current Pareto front) and globally (by maximum kriging variance points over the entire design landscape). The ability of the method to capture Pareto fronts with accuracy is demonstrated on the well-known ZDT test functions. The method is then tested on a real-life problem, the simultaneous minimization of springback and failure for a three-dimensional CPU intensive high strength steel stamping industrial use case.

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Notes

  1. An attempt to use a parallel version of LS-DYNA was made on the INRIA cluster but it was too complicated to manage that is why a serial version was used

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Acknowledgments

This research was conducted as part of the OASIS project, supported by OSEO within the contract FUI no. F1012003Z. The authors also acknowledge the support of Labex MS2T.The authors also acknowledge the support of Labex MS2T.

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Authors and Affiliations

  1. Universite de Lorraine, LEM3 UMR 7239, Ile du Saulcy, F-57045, Metz Cedex 01, France

    Mohamed Hamdaoui

  2. INRIA, Projet OPALE 2004, Route des Lucioles, BP 93 06902, Sophia-Antipolis, France

    Fatima-Zahra Oujebbour & Abderrahmane Habbal

  3. Laboratoire Roberval UMR 7337 UTC-CNRS, BP 20529, 60205, Compiegne cedex, France

    Piotr Breitkopf & Pierre Villon

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  1. Mohamed Hamdaoui
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  2. Fatima-Zahra Oujebbour
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  4. Piotr Breitkopf
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Correspondence to Mohamed Hamdaoui.

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Hamdaoui, M., Oujebbour, FZ., Habbal, A. et al. Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications. Int J Mater Form 8, 469–480 (2015). https://doi.org/10.1007/s12289-014-1190-y

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