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Digital Image Correlation Using Stochastic Parallel-Gradient-Descent Algorithm

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Abstract

This article proposes a digital image correlation (DIC) method based on the stochastic parallel gradient descent (SPGD) algorithm. Stochastic parallel perturbations are imposed on deformation parameters to make the correlation coefficients converge to a global extremum; thus, this allows the final measured values of the deformation parameters to be obtained and the DIC measurement to be made. Both simulated and real data processing, including rigid body and strain deformation, show that the proposed method can achieve nearly the same accuracy as the Newton–Raphson (NR) method in most cases and higher accuracy in some cases, such as the simulated experiments of rigid body translation with and without noise. It also has a good noise-robustness. Furthermore, a series of experiments have been designed to evaluate the convergence characteristics of the proposed method, and it has been proved able to process large displacement and have a stable convergence process, good robustness, and a high convergence speed when bilinear interpolation is adopted.

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Acknowledgments

This research was funded by the National Nature Science Foundation of China (Grant Nos. 40901215 and 11002156) and the Foundation of the National University of Defense Technology.

Sincere gratitude also goes to Dr. H. Y. Huang of Singapore-MIT Alliance for Research and Technology as the experimental data provider.

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Authors and Affiliations

  1. College of Optoelectric Science and Engineering, National University of Defense Technology, Changsha, Hunan, 410073, People’s Republic of China

    X. Long, S. Fu & Qifeng Yu

  2. College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha, Hunan, 410073, People’s Republic of China

    X. Yang & Qifeng Yu

  3. Hunan Key Laboratory of Videometrics and Vision Navigation, Changsha, Hunan, 410073, People’s Republic of China

    X. Long, S. Fu, X. Yang & Qifeng Yu

  4. The State Key Laboratory of Experimental Physics & Computational Mathematics, Beijing, 100085, People’s Republic of China

    Z. Qi

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  1. X. Long
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  2. S. Fu
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  3. Z. Qi
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  4. X. Yang
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  5. Qifeng Yu
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Correspondence to S. Fu.

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Long, X., Fu, S., Qi, Z. et al. Digital Image Correlation Using Stochastic Parallel-Gradient-Descent Algorithm. Exp Mech 53, 571–578 (2013). https://doi.org/10.1007/s11340-012-9667-4

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  • DOI: https://doi.org/10.1007/s11340-012-9667-4

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