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  4. A new machine learning based method for sampling virtual experiments and its effect on the parameter identification for anisotropic yield models
 
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2021
Journal Article
Title

A new machine learning based method for sampling virtual experiments and its effect on the parameter identification for anisotropic yield models

Abstract
A new method for sampling virtual experiments on the initial yield surface is introduced for the plane stress state. The method is based on a machine learning technique called active learning, which can be used to adaptively sample a parameter space with respect to a certain criterion. For the evaluation of this new method, it is compared against a random sampling approach taken from literature and the effect of both methods on three different anisotropic yield models, namely Yld89, Yld2000-2d and Yld2004-18p (in-plane), is analysed. The results demonstrate that the active learning based sampling approach has advantages over the random sampling approach in terms of reliability and sample efficiency. Moreover, it is found that the effect of the sampling method on the resulting yield surface depends on the anisotropic yield model considered.
Author(s)
Wessel, Alexander
Fraunhofer-Institut für Werkstoffmechanik IWM  
Morand, Lukas  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Butz, Alexander  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Helm, Dirk  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Volk, Wolfram
Technical University of Munich
Journal
IOP conference series. Materials science and engineering  
Project(s)
Verbesserte Blechumformsimulation durch 3D-Werkstoffmodelle und erweiterte Schalenformulierungen  
Verbesserte Blechumformsimulation durch 3D-Werkstoffmodelle und erweiterte Schalenformulierungen - Teil 2
Maßgeschneiderte Werkstoffeigenschaften durch Mikrostrukturoptimierung: Maschinelle Lernverfahren zur Modellierung und Inversion von Struktur-Eigenschafts-Beziehungen und deren Anwendung auf Blechwerkstoffe  
Funder
Bundesministerium für Wirtschaft und Energie -BMWI-  
Bundesministerium für Wirtschaft und Energie BMWI
Deutsche Forschungsgemeinschaft -DFG-, Bonn  
Conference
International Deep Drawing Research Group (IDDRG Conference) 2021  
Open Access
DOI
10.1088/1757-899X/1157/1/012026
Additional full text version
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Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • machine learning

  • sampling

  • anisotropic yield models

  • crystal plasticity model

  • active learning

  • adaptive sampling

  • virtual experiments

  • sheet metal forming simulation

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