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

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