Uncertainty analysis in composite material properties characterization using digital image correlation and finite element model updating

Abstract This work presents an uncertainty analysis on composite material constitutive parameters, which are extracted using digital image correlation (DIC) and finite-element-model-updating (FEMU). The uncertainty is induced by the measurement system noise in the DIC technique and the approximation error in the displacements and strains smoothing algorithm. The covariance matrix of the extracted material constitutive parameters has been given explicitly. Six material constitutive parameters were identified from a customized short-shear experiment simultaneously using an estimated optimal reconstruction mesh size as an illustration. Sensitivity of measurement noise and reconstruction parameter on extracted material properties has been investigated. The effects of region of interest (ROI) and DIC image number on uncertainties of extracted material properties have been addressed. It is suggested that there exist an appropriate ROI and the number of images, from which reliable material parameters can be identified, but much more data used in identification process always lead to smaller standard deviation and COV. It is observed that the material constants used to characterize the in-plane shear stress-strain behavior show strong robustness to the measurement noise. However, the identified longitudinal Young’s modulus is more sensitive to the measurement noise. Another key finding is that the reconstruction parameter in the global finite-element based approximation approach is critical for reliable material properties identification. Its value has to stay close to optimum for guaranteeing reliable identification of material properties.