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Optimal Aperture and Digital Speckle Optimization in Digital Image Correlation

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Abstract

Background

Digital image correlation (DIC) method is a non-interference and non-contact full-field optical measurement technology. DIC’s measurement accuracy is largely determined by the image acquisition system and the quality of the speckle pattern.

Objective

Although the optimal speckle pattern has been designed theoretically, the optimization process does not consider the influence of the imaging system. Furthermore, the optimal aperture has not been yield. The objective of this paper is to improve the measurement accuracy through optimization of aperture and speckle pattern.

Methods

In this paper, speckle images with different apertures and different speckle generation parameters were captured, and the corresponding measurement errors were evaluated.

Results

(1) The influence of aperture and the optimization of speckle are independent of each other. The optimal speckle generation parameters (speckle size and density) are determined experimentally, which turn out to be consistent with existing theoretical models. (2) The optimal aperture is 5.6, because excessively large F-number causes the loss of high-frequency information and excessively small F-number leads to significant lens aberrations, both of which will reduce the measurement accuracy.

Conclusions

The optimal aperture and speckle generation parameters (speckle diameter and density) are found and proved to be uncorrelated through actual experiments in practical conditions. These results provide experimental basis for the selection of parameters in actual engineering applications.

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References

  1. Peters WH, Ranson WF (1982) Digital imaging techniques in experimental stress-analysis. Opt Eng 21(3):427–431

    Article  Google Scholar 

  2. Yamaguchi I (1981) A laser-speckle strain-gauge. J Phys E Sci Instrum 14(11):1270–1273

    Article  Google Scholar 

  3. Quan CG, Tay CJ, Sun W, He XY (2008) Determination of three-dimensional displacement using two-dimensional digital image correlation. Appl Optics 47(4):583–593

    Article  Google Scholar 

  4. Huang XF, Liu ZW, Xie HM (2013) Recent progress in residual stress measurement techniques. Acta Mech Solida Sin 26(6):570–583

    Article  Google Scholar 

  5. Yan H, Pan B (2014) Three-dimensional displacement measurement based on the combination of digital holography and digital image correlation. Opt Lett 39(17):5166–5169

    Article  Google Scholar 

  6. Ghorbani R, Matta F, Sutton MA (2015) Full-field deformation measurement and crack mapping on confined masonry walls using digital image correlation. Exp Mech 55(1):227–243

    Article  Google Scholar 

  7. Hu ZX, Xu TG, Luo HY, Gan RZ, Lu HB (2016) Measurement of thickness and profile of a transparent material using fluorescent stereo microscopy. Opt Express 24(26):29823–29830

    Article  Google Scholar 

  8. Shao XX, Eisa MM, Chen ZN, Dong S, He XY (2016) Self-calibration single-lens 3D video extensometer for high-accuracy and real-time strain measurement. Opt Express 24(26):30124–30138

    Article  Google Scholar 

  9. Yan TH, Su Y, Zhang QC (2018) Precise 3D shape measurement of three-dimensional digital image correlation for complex surfaces. Sci China Technol Sc 61(1):68–73

    Article  Google Scholar 

  10. Wang ZY, Wu H, Kang K, Wang SB, Li YH, Hou WJ, Riaz H, Li LA, Li CW (2019) DIC/Moire hybrid method based on regular patterns for deformation measurement. Opt Express 27(13):18435–18444

    Article  Google Scholar 

  11. Gao ZR, Li FJ, Liu Y, Cheng T, Su Y, Fang Z, Yang M, Li Y, Yu J, Zhang QC (2020) Tunnel contour detection during construction based on digital image correlation. Optics and Lasers in Engineering 126

  12. Pan ZW, Huang SH, Su Y, Qiao MX, Zhang QC (2020) Strain field measurements over 3000 degrees C using 3D-Digital image correlation. Optics and Lasers in Engineering 127

  13. Zhang L, Wang T, Jiang Z, Kemao Q, Liu Y, Liu Z, Tang L, Dong S (2015) High accuracy digital image correlation powered by GPU-based parallel computing. Opt Lasers Eng 69:7–12. https://doi.org/10.1016/j.optlaseng.2015.01.012

    Article  Google Scholar 

  14. Huang JW, Zhang LQ, Jiang ZY, Dong SB, Chen W, Liu YP, Liu ZJ, Zhou LC, Tang LQ (2018) Heterogeneous parallel computing accelerated iterative subpixel digital image correlation. Sci China Technol Sci 61(1):74–85. https://doi.org/10.1007/s11431-017-9168-0

    Article  Google Scholar 

  15. Yang J, Huang J, Jiang Z, Dong S, Tang L, Liu Y, Liu Z, Zhou L (2020) SIFT-aided path-independent digital image correlation accelerated by parallel computing. Optics and Lasers in Engineering 127. https://doi.org/10.1016/j.optlaseng.2019.105964

  16. Sun C, Zhou Y, Li Y, Chen J, Miao H (2018) Multiscale segmentation-aided digital image correlation for strain concentration characterization of a turbine blade fir-tree root. Meas Sci Technol 29(4). https://doi.org/10.1088/1361-6501/aa91df

  17. Lin Q, Gong Y, Sun C, Chen J (2020) An indirect experimental measurement method for contact length identification in non-conforming frictionless contact. Exp Mech 60(6):801–813. https://doi.org/10.1007/s11340-020-00600-w

    Article  Google Scholar 

  18. Sun C, Li Y, Chen J (2020) Strain concentration characterization for the multi-tooth contact based on multi-resolution digital image correlation and orthogonal analysis. Int J Mech Sci 167. https://doi.org/10.1016/j.ijmecsci.2019.105238

  19. Lava P, Van Paepegem W, Coppieters S, De Baere I, Wang Y, Debruyne D (2013) Impact of lens distortions on strain measurements obtained with 2D digital image correlation. Opt Lasers Eng 51(5):576–584

    Article  Google Scholar 

  20. Pan B, Yu LP, Wu DF, Tang LQ (2013) Systematic errors in two-dimensional digital image correlation due to lens distortion. Opt Lasers Eng 51(2):140–147

    Article  Google Scholar 

  21. Reu PL, Sweatt W, Miller T, Fleming D (2015) Camera system resolution and its influence on digital image correlation. Exp Mech 55(1):9–25

    Article  Google Scholar 

  22. Yaofeng S, Pang JHL (2007) Study of optimal subset size in digital image correlation of speckle pattern images. Opt Lasers Eng 45(9):967–974

    Article  Google Scholar 

  23. Pan B, Xie HM, Wang ZY, Qian KM, Wang ZY (2008) Study on subset size selection in digital image correlation for speckle patterns. Opt Express 16(10):7037–7048

    Article  Google Scholar 

  24. Hua T, Xie HM, Wang SM, Hu ZX, Chen PW, Zhang QM (2011) Evaluation of the quality of a speckle pattern in the digital image correlation method by mean subset fluctuation. Opt Laser Technol 43(1):9–13

    Article  Google Scholar 

  25. Crammond G, Boyd SW, Dulieu-Barton JM (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378

    Article  Google Scholar 

  26. Liu XY, Li RL, Zhao HW, Cheng TH, Cui GJ, Tan QC, Meng GW (2015) Quality assessment of speckle patterns for digital image correlation by Shannon entropy. Optik 126(23):4206–4211

    Article  Google Scholar 

  27. Lecompte D, Smits A, Bossuyt S, Sol H, Vantomme J, Van Hemelrijck D, Habraken AM (2006) Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44(11):1132–1145

    Article  Google Scholar 

  28. Pan B, Lu ZX, Xie HM (2010) Mean intensity gradient: An effective global parameter for quality assessment of the speckle patterns used in digital image correlation. Opt Lasers Eng 48(4):469–477

    Article  Google Scholar 

  29. Yu H, Guo RX, Xia HT, Yan FT, Zhang YB, He TC (2014) Application of the mean intensity of the second derivative in evaluating the speckle patterns in digital image correlation. Opt Lasers Eng 60:32–37

    Article  Google Scholar 

  30. Su Y, Zhang QC, Xu XH, Gao ZR (2016) Quality assessment of speckle patterns for DIC by consideration of both systematic errors and random errors. Opt Lasers Eng 86:132–142

    Article  Google Scholar 

  31. Lecompte D, Sol H, Vantomme J, Habraken A (2006) Analysis of speckle patterns for deformation measurements by digital image correlation. In: Slangen P (ed) Speckle06: Speckles, from Grains To Flowers, vol 6341. Proceedings of SPIE. https://doi.org/10.1117/12.695276

  32. Schreier H, Orteu J-J, Sutton MA (2009) Image Correlation for Shape. Motion and Deformation Measurements, Basic Concepts, Theory and Applications

    Google Scholar 

  33. Su Y, Zhang QC, Gao ZR (2017) Statistical model for speckle pattern optimization. Opt Express 25(24):30259–30275

    Article  Google Scholar 

  34. Su Y, Gao ZR, Fang Z, Liu Y, Wang YR, Zhang QC, Wu SQ (2019) Theoretical analysis on performance of digital speckle pattern: uniqueness, accuracy, precision, and spatial resolution. Opt Express 27(16):22439–22474

    Article  Google Scholar 

  35. Chen ZN, Shao XX, Xu XY, He XY (2018) Optimized digital speckle patterns for digital image correlation by consideration of both accuracy and efficiency. Appl Optics 57(4):884–893

    Article  Google Scholar 

  36. Mazzoleni P, Matta F, Zappa E, Sutton MA, Cigada A (2015) Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns. Opt Lasers Eng 66:19–33

    Article  Google Scholar 

  37. Bornert M, Bremand F, Doumalin P, Dupre JC, Fazzini M, Grediac M, Hild F, Mistou S, Molimard J, Orteu JJ, Robert L, Surrel Y, Vacher P, Wattrisse B (2009) Assessment of digital image correlation measurement errors: methodology and results. Exp Mech 49(3):353–370

    Article  Google Scholar 

  38. Su Y, Zhang QC, Gao ZR, Xu XH, Wu XP (2015) Fourier-based interpolation bias prediction in digital image correlation. Opt Express 23(15):19242–19260

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11627803, 11702287, 11872354), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB22040502), and the Fundamental Research Funds for the Central Universities (WK2480000004).

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Authors and Affiliations

  1. CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, China

    Y. Wang, Y. Liu, Z. Gao, Y. Su & Q. Zhang

  2. Beijing Institute of Structure and Environment Engineering, Beijing, 100076, China

    Y. Gao

Authors
  1. Y. Wang
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  2. Y. Gao
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  3. Y. Liu
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Correspondence to Y. Su or Q. Zhang.

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The authors declare that they have no potential conflicts of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Wang, Y., Gao, Y., Liu, Y. et al. Optimal Aperture and Digital Speckle Optimization in Digital Image Correlation. Exp Mech 61, 677–684 (2021). https://doi.org/10.1007/s11340-021-00694-w

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