PSI - Issue 13

Francisco Barros et al. / Procedia Structural Integrity 13 (2018) 1993–1998 Author name / Structural Integrity Procedia 00 (2018) 000–000

1998

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The results relating to the 3D DIC computations of the displacements of the tensile specimen for both camera configurations are presented graphically in Fig. 4. In Fig. 4a, it can be seen that the displacements measured through DIC, after adjusting for the new calibration parameters, are consistent with 5 mm vertical displacement imposed on the top grip. For comparison, the point cloud obtained if the new calibration values had not been taken into account is also shown in Fig. 4a, and is clearly inaccurate. The displacement magnitude field in Fig. 4b also matches the expected values. The mean error in the deformed 3D coordinates of the subset centres between the original calibration and the repositioned calibration was 0.037 mm and its distribution in the region of interest can be seen in Fig. 4c. It is two orders of magnitude smaller than the measured displacement. The difference in computed 3D coordinates is mostly in the out of-plane direction, which is to be expected, as 3D DIC is known to have lower accuracy in the depth direction [7]. 4. Conclusions and future work A stereo camera rig recalibration method based on feature detection in fixed objects around the region of interest has proven capable of providing credible results in 3D digital image correlation. Changing the positions and orientations of the cameras and refocusing them during a DIC test while keeping the load constant resulted in very small alterations after compensating for the new calibration parameters. The concept should, in the future, be able to be applied to long-term field measurements, where cameras need to be removed and returned regularly for practical reasons, where references for calibration would come from either natural features or prepared fixed targets instead of speckle patterns. Acknowledgements The authors gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000022 - SciTech - Science and Technology for Competitive and Sustainable Industries, co-financed by Programa Operacional Regional do Norte (NORTE2020) through Fundo Europeu de Desenvolvimento Regional (FEDER). Pedro J. Sousa gratefully acknowledges the FCT (Fundação para a Ciência e a Tecnologia) for the funding of the PhD scholarship SFRH/BD/129398/2017. Dr. Moreira acknowledges POPH - QREN-Tipologia 4.2 - Promotion of scientific employment funded by the ESF and MCTES. References [1] F. Kahl, B. Triggs and K. Åström, “Critical Motions for Auto-Calibration When Some Intrinsic Parameters Can Vary,” Journal of Mathematical Imaging and Vision, vol. 13, no. 2, p. 131–146, 2000. [2] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2003. [3] M. Malesa and M. Kujawinska, “Deformation measurements by digital image correlation with automatic merging of data distributed in time,” Applied Optics, vol. 52, no. 19, pp. 4681-4692, 2013. [4] H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008. [5] T. O. H. Charrett, K. Kotowski and R. P. Tatam, “Speckle tracking approaches in speckle sensing,” Proc. SPIE 10231, Optical Sensors 2017, 102310L, 2017. [6] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000. [7] M. A. Sutton, J. J. Orteu and H. W. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications, Springer, 2009.