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.

先进的各向异性本构模型涉及大量模型参数，需要多次常规机械实验才能进行准确校准。通过单个异质实验与全场变形场相结合的逆识别能够减少实验工作。通过由表示应变场异质性程度的指标驱动的形状优化，可以消除用户在设计此类信息丰富的实验时的依赖性。这种方法通过使用参考材料模型综合生成的数字图像相关 (DIC) 数据提供的有限元模型更新 (FEMU) 技术评估异质实验的识别质量来验证。对于Hill 1948屈服准则的识别，发现所采用的异质性指标与逆识别质量之间存在正相关。结果表明，识别指标可用于在 FEMU 过程之前准确预测识别质量。此外，结果表明，通过考虑试样中主材料轴的方向，可以显着提高所寻求参数的可识别性。使用更先进的各向异性屈服函数，即 Yld2000-2D，验证了所提出的可识别性指标的可靠性和稳健性。因此，识别指标可用于增强信息丰富的实验设计。最后，建议将材料方向视为设计变量。