Research on a New Power Window Weighted Digital Image Correlation for Accurate Measurement

Abstract

Background

Digital Image Correlation (DIC) is a widely employed full-field measurement technique in the realm of experimental mechanics. Nevertheless, mitigating measurement errors, particularly in fields with large strain gradients, remains a challenge.

Objective

The Gaussian window is employed to weight the correlation criterion in order to enhance measurement accuracy, and this method is called Gaussian window weighted DIC (GW-DIC). However, the optimization of the weighted correlation criterion does not guarantee that the displacement vector iterates to its optimal solution as the Gaussian window parameter changes during the iteration.

Methods

A new power window and the power window weighted DIC (PW-DIC) are proposed. The parameters of this power window keep constant during the iteration, and can be selected by given self-adaptive strategy for accuracy or preset according to the presumed deformation of the region of interest (ROI) for efficiency.

Results

The calculation example of synthetic images with imposed homogeneous deformation indicates that, the proposed power window is more effective than the Gaussian window when weighting the correlation criterion. For multi-directional deformation fields, both the displacement and strain accuracy of PW-DIC with self-adaptive parameters are at least 18% superior to those of conventional DIC. The tensile experimental dataset indicates that PW-DIC is more accurate and stable than GW-DIC.

Conclusions

PW-DIC with self-adaptive parameters is better suited for strain measurement in fields with large strain gradients. The weighted correlation criterion with preset parameters can potentially serve as a substitute for conventional correlation criterion.