Improved Digital Virtual Twin to validate inverse identification of plastic anisotropy via FEMU

Abstract The sheet metal forming industry increasingly relies on numerical simulations to optimize complex forming processes. Accurate simulations require advanced constitutive models to capture material plasticity, but calibrating these models is resource-intensive. To reduce experimental efforts, this study employs Finite Element Model Updating (FEMU) with data from a non-conventional notched tensile test and a biaxial tensile test. These tests, combined with Digital Image Correlation (DIC), provide full-field deformation measurements for inverse calibration of the Yld2000-2d yield function. To verify the proposed FEMU approach, synthetic DIC data was generated using a Digital Virtual Twin (DVT) of the experiments, incorporating reference anisotropy parameters of the Yld2000-2d yield function from a conventional calibration campaign on a low-carbon steel sheet. The DVT not only enables accurate FEMU verification but also facilitates uncertainty quantification by accounting for all steps in the measurement chain. However, a discrepancy between DVT-generated and experimental data led to differing material model parameter identifications. This inconsistency was traced to inaccuracies in the plasticity model within the DVT. To address this, differential work hardening was introduced into the Yld2000-2d yield function, improving the fidelity of the DVT. The refined DVT improves the reliability of FEMU results deepens the understanding of inverse material model calibration.