Inverse estimation of material model parameters using Bayesian data assimilation

Inverse estimation of material model parameters using Bayesian data assimilation

YAMANAKA Akinori, SUDA Michihiko, SUEKI Sae, SASAKI Kengo, FUNAMOTO Ryuki

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Abstract. 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.

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


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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