Enhancing the information-richness of sheet metal specimens for inverse identification of plastic anisotropy through strain fields

Advanced anisotropic constitutive models involve a large number of model parameters requiring multiple conventional mechanical experiments for accurate calibration. Inverse identification through a single heterogeneous experiment combined with full-field deformation fields enables to reduce the experimental effort. User dependency in design of such information-rich experiment can be eliminated via shape optimization that is driven by an indicator expressing the degree of strain field heterogeneity. This approach is validated by assessing the identification quality of the heterogeneous experiments through the Finite Element Model Updating (FEMU) technique fed by synthetically generated Digital Image Correlation (DIC) data using a reference material model. Positive correlation between the adopted heterogeneity indicator and the inverse identification quality is found for the identification of the Hill 1948 yield criterion. It is shown that identification metrics can be used to accurately predict the identification quality prior to the FEMU process. Moreover, it is shown that the identifiability of the sought parameters can be significantly improved by considering the orientation of the principal material axis in the specimen. The reliability and robustness of the proposed identifiability metrics are validated using a more advanced anisotropic yield function, namely Yld2000-2D. The identification metrics can therefore be used to enhance the design of information-rich experiments. Finally, it is recommended to treat the material orientation as a design variable.

Automated summary

Advanced anisotropic constitutive models involve a large number of model parameters, but inverse identification through a single heterogeneous experiment combined with full-field deformation fields can reduce the experimental effort. The user dependency in designing information-rich experiments can be eliminated through shape optimization, and the heterogeneity indicator can accurately predict the identification quality and improve the identifiability of parameters.