Notice This is not the latest version of this item. The latest version can be found at: https://fordatis.fraunhofer.de/handle/fordatis/292.2
31-Oct-2022 Tabular Data https://fordatis.fraunhofer.de/handle/fordatis/292
http://dx.doi.org/10.24406/fordatis/225

Data for "Machine learning-based sampling of virtual experiments within the full stress state to identify parameters of anisotropic yield models"

Wessel, Alexander (Fraunhofer Institute for Mechanics of Materials IWM; Chair of Metal Forming and Casting, Technical University of Munich); Morand, Lukas (Fraunhofer Institute for Mechanics of Materials IWM); Butz, Alexander (Fraunhofer Institute for Mechanics of Materials IWM); Helm, Dirk (Fraunhofer Institute for Mechanics of Materials IWM); Volk, Wolfram (Chair of Metal Forming and Casting, Technical University of Munich)

IWM Fraunhofer-Institut für Werkstoffmechanik
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Abstract

Data for "Machine learning-based sampling of virtual experiments within the full stress state to identify parameters of anisotropic yield models" by A. Wessel, L. Morand, A. Butz, D. Helm and W. Volk.
Classification
620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
531 Klassische Mechanik, Festkörpermechanik
672 Eisen, Stahl, andere Eisenlegierungen
Keywords
yield condition
anisotropic material,
crystal plasticity
machine learning
adaptive sampling
Funder
Deutsche Forschungsgemeinschaft DFG
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Version Item Date Summary
2 fordatis/292.2 2024-05-13 10:29:56.092 Nachreichung von Daten zur finalen Veröffentlichung.
1 fordatis/292 2022-11-17 12:44:59.0
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