Computation of strains from stereo digital image correlation using principal component analysis

Abstract

Stereo digital image correlation (stereo DIC), a full-field deformation measurement technique, is widely used in the mechanics community due to its accuracy, versatility, and ability to measure 3D displacements over specimen surfaces. There are several strategies reported in the literature to compute strains from DIC displacements. Among them, the one based on principal component analysis (PCA) offers a way to effectively demarcate noise from the data by identifying the number of dominant singular vectors that are further differentiated by fitting an appropriate polynomial function. Though this approach has been used to differentiate 2D-DIC displacements, its implementation in the stereo-DIC workflow has not been reported yet. Moreover, its two important parameters, namely, the number of dominant singular vectors and the order of polynomial function, are still selected based on a heuristic where the user assesses the shape of the singular vectors and values to choose these parameters. In the present work, we address these gaps by presenting our novel PCA-based strain computation approach to stereo DIC, and automate the selection of parameters in our method using existing routines such as Stein’s unbiased risk estimator (SURE) and Bayesian information criterion (BIC). We verify the accuracy of the proposed approach on synthetic and DIC Challenge datasets and validate it on data from two experiments: uniaxial tension test and hydraulic inflation test. We observe an excellent agreement between the computed strains and the ground truth in all the cases. The main benefits of the proposed method are two-fold: (a) it leads to convergence in virtual strain gauge study and (b) it is non-parametric because its key parameters are chosen adaptively based on the data at hand.