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Full-field digital image correlation with Kriging regression
Affiliation:1. Centre for Engineering Dynamics, School of Engineering, University of Liverpool, L69 3GH, United Kingdom;2. Institute for Risk and Uncertainty, University of Liverpool, L69 3GH, United Kingdom;3. School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, United Kingdom;1. Brookhaven National Laboratory - NSLS II, 50 Rutherford Dr., Upton, NY 11973-5000, USA;2. Jiangsu Key Laboratory of Spectral Imaging & Intelligence Sense, Nanjing University of Science and Technology, Nanjing 210094, China;3. College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China;4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore;1. School of Laser Technology and Photonics, Suranaree University of Technology, Thailand;2. Department of Urban Environment Systems, Faculty of Engineering, Chiba University, Japan;1. Poznan University of Technology, Institute of Materials Science and Engineering, Pl. M. Sklodowskiej-Curie 5, 60-965 Poznan, Poland;2. Poznan University of Technology, Institute of Mechanical Technology, Piotrowo Street 3, 60-965 Poznan, Poland
Abstract:A full-field Digital Image Correlation (DIC) method with integrated Kriging regression is presented in this article. The displacement field is formulated as a best linear unbiased model that includes the correlations between all the locations in the Region of Interest (RoI). A global error factor is employed to extend conventional Kriging interpolation to quantify displacement errors of the control points. An updating strategy for the self-adaptive control grid is developed on the basis of the Mean Squared Error (MSE) determined from the Kriging model. Kriging DIC is shown to outperform several other full-field DIC methods when using open-access experimental data. Numerical examples are used to demonstrate the robustness of Kriging DIC to different choices of initial control points and to speckle pattern variability. Finally Kriging DIC is tested on an experimental example.
Keywords:Full-field  Digital image correlation  Kriging regression
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