Identification of Anisotropic Yield Functions Using FEMU and an Information-Rich Tensile Specimen

Abstract. To fully exploit the predictive accuracy of advanced anisotropic yield functions, a large number of classical mechanical tests is required for calibration purposes. The Finite Element Model Updating (FEMU) technique enables to simultaneously extract multiple anisotropic parameters when fed with heterogeneous strain fields obtained from a single information-rich experiment. This inverse approach has the potential to mitigate the experimental calibration effort by resorting to a single, yet more complex experiment augmented with Digital Image Correlation. In this paper, the sought anisotropic parameters of two selected yield functions are inversely identify for a low carbon steel sheet based on a previously designed information-rich tensile specimen. The experimentally acquired strain field data is used to inversely identify the Hill48 yield criterion and the Yld2000-2d yield functions. The results are compared with conventional calibration methods for both anisotropic yield functions. The inverse identification is then thoroughly studied using virtual experiments enabling to disentangle the effect of the material model error and the strain reconstruction error (DIC), respectively. It is shown that the material model error dominates the inverse identification of the Hill48 yield criterion. The reduced material model error for the Yld2000-2d yield function enables obtaining inversely identified anisotropic parameters that are closer to the reference ones. The paper clearly shows the importance of the predictive accuracy of the selected anisotropic yield function in the inverse identification process.