Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
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AbstractDigital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.
SponsorsThis work is supported by the National Natural Science Foundation of China (Grant nos. 11272032, 11322220, 91216301 and 11427802), the Program for New Century Excellent Talents in University (Grant no. NCET-12-0023), the Science Fund of State Key Laboratory of Automotive Safety and Energy (Grant no. KF14032) and Beijing Nova Program (xx2014B034). We also thank King Abdullah University of Science and Technology (KAUST) for its support.