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Material Parameter Identification Using Bayesian Data Assimilation and Biaxial Tensile Test

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Abstract:

This study proposes a Bayesian data assimilation approach to estimate material model parameters based on deformation fields measured via digital image correlation in a biaxial tensile test using a cruciform specimen. The anisotropy parameters and exponent of the Yld2000-2d yield function for a A5052P-H32 aluminum alloy are identified. The results indicate that the proposed method can estimate parameters with high accuracy—comparable to those identified via conventional multiaxial testing methods—while requiring only a single biaxial test. The proposed method offers an efficient framework for material modeling by minimizing a cost function via Bayesian optimization, enabling parameter identification from a single biaxial tensile test for sheet metal forming applications.

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Periodical:

Key Engineering Materials (Volume 1049)

Pages:

29-35

DOI:

https://doi.org/10.4028/p-Wg5agb

Citation:

Cite this paper

Online since:

April 2026

Authors:

Akinori Yamanaka*, Shun Shimaoka

Keywords:

Bayesian Data Assimilation, Material Modeling, Parameter Identification

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Creative Commons CC BY 4.0

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* - Corresponding Author

References

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