In the classic subset-based digital image correlation (DIC) technique, the displacement measurement errors due to the shape function have been a key factor. Generally, the first-order shape function is widely employed. However, in the practical experiments, the order of the deformation of the specimen is higher than the selected shape function. So the errors due to the under-matched shape functions are presented. Although the systematic errors due to under-matched displacement mapping functions and the random errors due to matched or overmatched displacement mapping function have been examined, the root-mean-square errors (RMSE) due to under-matched shape function are still not investigated. In the paper, the root-mean-square errors of the measured displacements due to under-matched shape function are studied experimentally by using simulated speckle patterns.
A modified digital image correlation (DIC) method is presented to balance the influence of subset size choice, improve the calculation efficiency, and enhance the measurement accuracy. The relations between pixels and the central pixel are investigated. A relatively large subset size is selected in the method and all the pixels in a subset are treated nonuniformly. Weight is assigned to each pixel dependent on the significance of the pixel to track a subset from deformed images, and weights are computed by Butterworth function based on the distances between the pixels and the central pixel in the subset. Compared with classical DIC, the proposed method is a compromise between a small and a large subset. The choice of subset size for classical DIC is displaced by choice of parameters of Butterworth function in the presented method. The influence of subset size is reduced, and the calculation efficiency is improved with enhanced measurement accuracy by the modified method. Computer-simulated speckle patterns and real experiments are applied to verify the proposed method.
Digital image correlation (DIC) is widely applied in optical measurement field. In this work, the classical DIC algorithm is modified to improve the speed and enhance the measurement accuracy. A Butterworth function is installed on the traditional sum-of-squared differences correlation criterion. And inverse compositional Gauss-Newton is carried out. The computer generated speckle patterns are used to demonstrate the presented algorithm. The results declare the proposed method can improve the speed with enhanced measurement accuracy.
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