Image correlation technique for strain measurement of polycrystalline microstructures

An image processing technique is proposed to measure the deformation of polycrystalline materials based on correlating the grains in reference and deformed SEM images. The advantage of this technique compared to the conventional subset-based Digital Image Correlation (DIC) is that it can be applied when speckle patterning is not efficient or when studying boundary-related mechanics is the objective. The technique is based on correlating grains by defining their boundaries rather than just subsets of image pixels. It reveals the anisotropy inherent in the polycrystals since it allows the analysis to specify each grain separately without averaging the results. The technique is applied by detecting the approximate grain boundaries edges and then refining their location with high accuracy. The correlation is performed between points calculated from each grain in the reference and deformed images as a Point Set Registration (PSR) problem. Finally, the displacements and strains are calculated from the resulting transformation matrix. A benchmark problem was developed to discuss the error over a strain range of 0.02 to 0.2 and showed that the resulting strains are reasonably accurate. Also, an in situ experiment was conducted to demonstrate the implementation of the technique using a specimen with fine-grained Zirconia polycrystals. The technique successfully revealed the crack tip plastic zone, and strain mismatch between grains.