Inverse Estimation of Material Model Parameters Using Digital Image Correlation and Ensemble-based Four-dimensional Variational Methods

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  • SUEKI Sae
    東京農工大学 大学院 工学府 機械システム工学専攻
  • ISHII Akimitsu
    物質・材料研究機構 若手国際研究センター
  • YAMANAKA Akinori
    東京農工大学 大学院 工学研究院 先端機械システム部門

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Other Title
  • デジタル画像相関法とアンサンブル4次元変分法による材料モデルのパラメータ逆推定

Abstract

<p>The prediction accuracy of the deformation behavior of materials by finite element (FE) simulation depends on the parameters in selected material models. Although the parameters are conventionally identified from standard material tests (e.g., uniaxial tensile and multiaxial material tests) to characterize the deformation behavior, the identification process requires a large number of experiments. We develop a novel inverse methodology for estimating the material model parameters by combining digital image correlation (DIC) measurement and FE simulation coupled with an ensemble-based four-dimensional variational method (En4DVar). En4DVar incorporates the experimental data obtained from a material test into the FE simulation that reproduces the test and inversely estimates the parameters such that the simulation results follow the experimental data, allowing for the reduction of experimental effort. We use the proposed method to estimate the parameters of a strain-hardening law and anisotropic yield function from the results of uniaxial tensile test of a round bar of aluminum alloy. DIC measurement is conducted to obtain experimental data of the three-dimensional displacement and strain field over the surface of the specimen, including the post-necking range. The results demonstrate that En4DVar is a promising method for inversely estimating the parameters and characterizing the deformation behavior of a material from the results of a small number of tests.</p>

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