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Inverse estimation of material model parameters using Bayesian data assimilation
YAMANAKA Akinori, SUDA Michihiko, SUEKI Sae, SASAKI Kengo, FUNAMOTO Ryuki
download PDFAbstract. This study proposes a new method for the inverse estimation of the parameters included in material models from full-field measurement data that are obtained using the digital image correlation method. This approach is based on data assimilation according to the Bayes’ theorem (Bayesian data assimilation). In this study, we demonstrate the assimilation of experimental data obtained from uniaxial tensile, forming, and fracture tests of aluminum alloys into elastoplastic finite element and phase-field crack propagation simulations. The proposed method allows the simultaneous estimation of multiple material model parameters. The Bayesian data assimilation is a promising methodology for estimating the parameters of different material models and constructing digital twins of material deformation.
Keywords
Bayesian Data Assimilation, Digital Image Correlation, Material Model
Published online 4/24/2024, 6 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: YAMANAKA Akinori, SUDA Michihiko, SUEKI Sae, SASAKI Kengo, FUNAMOTO Ryuki, Inverse estimation of material model parameters using Bayesian data assimilation, Materials Research Proceedings, Vol. 41, pp 1190-1195, 2024
DOI: https://doi.org/10.21741/9781644903131-132
The article was published as article 132 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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