Performances of different subset shapes and control points in subset-based digital image correlation and their applications in boundary deformation measurement

Abstract

Digital image correlation (DIC) is an effective and popular tool for displacement and strain measurements. In the standard subset-based algorithms, the center point of a subset is considered by default as the control point for calculation, and it is difficult to obtain the deformation information at the boundary. Proper selection of the subset shape and the location of control points are vital to the displacement calculation at the boundary. In this paper, registration accuracies of several typical types of subset shapes and different locations of control points are investigated. The results illustrate that different choices of subset shapes can greatly affect the registration accuracy, while different choices for the locations of control points have little impact on it. Based on these results, the noncentral algorithm is developed for the whole-field deformation measurement.