Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation |
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Authors: | Jia-qing ZhaoPan Zeng Li-ping LeiYuan Ma |
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Affiliation: | Key Laboratory for Advanced Materials Processing Technology of MOE, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China |
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Abstract: | Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement. |
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Keywords: | NR, Newton-Raphson method qN, quasi-Newton method ROI, region of interest POI, point of interest RMS, root mean square PIA, population-based intelligent algorithm GA, genetic algorithm DE, differential evolution PSO, particle swarm optimization T, Q-stage evolutionary strategy C, parameter control strategy E, space expanding strategy DE-T, DE combined with T strategy (GA-T and PSO-T have similar meanings) DE-TC, DE combined with T and C strategies DE-TE, DE combined with T and E strategies DE-TCE, DE combined with T, C and E strategies DE-TCE& qN, the correlation algorithm which iterates with qN from the initial guess by DE-TCE |
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