Stereo digital image correlation (stereo DIC), a full-field deformation measurement technique, is increasingly being used to resolve strains at µm-length scale by using microscope-like imaging systems. Self calibration of these imaging systems is more cost-effective and convenient than the conventional target-based calibration. Though the use of self-calibrated stereo DIC systems has already been reported, less attention has been paid to improving the accuracy of these systems. In the present work, we improve the accuracy of a self-calibrated stereo DIC system, which is composed of two full-frame DSLR cameras coupled to macro lenses and is used for testing ASTM E8M sub-sized flat dog-bone specimens. First, we collect the images of two of the speckled test specimens that subtend an angle of 12° between them using an f/25 aperture. Our image-collection strategy leads to a convergent imaging configuration with viewpoints that range from −45° to 45° across two perpendicular directions. Next, we process the collected images in a commercial photogrammetric calibration software by using more than nine image points for computing each object point. We validate our findings on a rigid-body motion test and a uniaxial tensile experiment, and we observe an excellent agreement between the stereo-DIC measurements and the ground truth. Using our findings, the reprojection error of self calibration is improved from 0.3 pixel to 0.1 pixel. The error in the stereo-DIC strain measurements is always less than 3.4% with the improvements made to self calibration, whereas it is as large as 7.6% without them.

Stereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high-quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature-based matching techniques to address this gap.Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.

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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.

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