A methodology is proposed to optimize a specimen shape in a biaxial testing machine for the identification of constitutive laws based on full-field measurements. Within the framework of finite element model updating and integrated digital image correlation, the covariance matrix of the identified material parameters due to acquisition noise is computed, and its minimization is the basis of the proposed shape optimization. Two models are investigated: first, a linear elastic law, and second, an elastoplastic law with linear kinematic hardening. Two optimal fillet radii sets are assessed for the two investigated laws based on the minimization of the identification uncertainty.
The identification of the parameters of several constitutive laws is performed with the Integrated Digital Image Correlation (IDIC) technique in a biaxial experiment for a cruciform specimen made of stainless steel. The sought material parameters are assessed with the contribution of both reaction forces (from load sensors) and displacement fields (measured via digital image correlation). For each constitutive law a global residual quantifying the model error is assessed.
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