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Identifying material parameters in crystal plasticity by Bayesian optimization

Kuhn, Jannick; Spitz, Jonathan; Sonnweber-Ribic, Petra; Schneider, Matti 1; Böhlke, Thomas ORCID iD icon 1
1 Institut für Technische Mechanik (ITM), Karlsruher Institut für Technologie (KIT)

Abstract:

In this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such material models experimentally, in particular when a large number of cycles is considered, as typical for fatigue applications. Even when appropriate microstructures and microscopic material models are identified, calibrating the individual parameters of the model to some experimental data is necessary for industrial use, and the task is formidable as even a single simulation run is time consuming (although less expensive than a corresponding experiment). For solving this problem, we investigate Gaussian process based Bayesian optimization, which iteratively builds up and improves a surrogate model of the objective function, at the same time accounting for uncertainties encountered during the optimization process. We describe the approach in detail, calibrating the material parameters of a high-strength steel as an application. We demonstrate that the proposed method improves upon comparable approaches based on an evolutionary algorithm and performing derivative-free methods.


Verlagsausgabe §
DOI: 10.5445/IR/1000137088
Veröffentlicht am 31.08.2021
Originalveröffentlichung
DOI: 10.1007/s11081-021-09663-7
Scopus
Zitationen: 23
Web of Science
Zitationen: 26
Dimensions
Zitationen: 34
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 1389-4420, 1573-2924
KITopen-ID: 1000137088
Erschienen in Optimization and Engineering
Verlag Springer
Band 23
Seiten 1489–1523
Vorab online veröffentlicht am 21.08.2021
Nachgewiesen in Dimensions
Web of Science
Scopus
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