Skip to main content
SpringerLink
Log in

Precise 3D shape measurement of three-dimensional digital image correlation for complex surfaces

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Three dimensional-digital image correlation (3D-DIC) is a widely used optical metrology in the experimental mechanics community because of its reliability, practicality, and flexibility. Although the precision of digital image correlation (DIC) has been thoroughly studied theoretically and numerically, verification experiments have seldom been performed, especially for complex surfaces with a small field of view (FOV). In this work, the shape of a 1-yuan coin was measured using 3D-DIC; the shape was complex due to the presence of many fine details, and the FOV was relatively small because the coin diameter was only 25 mm. During the experiment, a novel strategy for speckle production was developed: white paint was simply sprayed onto the surface. Black paint was not used; instead, taking advantage of the reflective nature of the coin surface, polarized light and a Polaroid filter were introduced, and the polarization direction was carefully adjusted, ensuring that the spray pattern was extremely thin and that high-quality speckle images with significant contrast were captured. The three-dimensional coin shape was also successfully determined for comparison using a stylus profiler. The results demonstrate that 3D-DIC provides high precision in shape measurement even for complex surfaces with small FOV. The precision of 3D-DIC can reach 1/7000 of the field of view, corresponding to about 6 μm in this experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Pan B. Recent progress in digital image correlation. Exp Mech, 2010, 51: 1223–1235

    Article  Google Scholar 

  2. Pan B, Qian K, Xie H, et al. Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review. Meas Sci Technol, 2009, 20: 062001

    Article  Google Scholar 

  3. Peters W H, Ranson W F. Digital imaging techniques in experimental stress analysis. Opt Eng, 1982, 21: 427

    Google Scholar 

  4. Gao Y, Cheng T, Su Y, et al. High-efficiency and high-accuracy digital image correlation for three-dimensional measurement. Opt Lasers Eng, 2015, 65: 73–80

    Article  Google Scholar 

  5. Pan B, Li K, Tong W. Fast, robust and accurate digital image correlation calculation without redundant computations. Exp Mech, 2013, 53: 1277–1289

    Article  Google Scholar 

  6. Pan B, Ma L J, Xia Y. A novel technique for measuring 3D deformation of adhesively bonded single lap joint. Sci China-Phys Mech Astron, 2015, 59: 614601–614608

    Article  Google Scholar 

  7. Shao X, Dai X, Chen Z, et al. Real-time 3D digital image correlation method and its application in human pulse monitoring. Appl Opt, 2016, 55: 696–704

    Article  Google Scholar 

  8. Yu L, Pan B. Single-camera stereo-digital image correlation with a four-mirror adapter: Optimized design and validation. Opt Lasers Eng, 2016, 87: 120–128

    Article  Google Scholar 

  9. Yuan Y, Zhan Q, Huang J, et al. Digital image correlation with gray gradient constraints: Application to spatially variant speckle images. Opt Lasers Eng, 2016, 77: 85–91

    Article  Google Scholar 

  10. Zhu R, Xie H, Hu Z, et al. Performances of different subset shapes and control points in subset-based digital image correlation and their applications in boundary deformation measurement. Appl Opt, 2015, 54: 1290–1301

    Article  Google Scholar 

  11. Cai Y, Tian C, Zhang G, et al. Influence of γ¢ precipitates on the critical strain and localized deformation of serrated flow in Ni-based superalloys. J Alloys Compd, 2017, 690: 707–715

    Article  Google Scholar 

  12. Cai Y, Yang S, Fu S, et al. Investigation of Portevin-Le Chatelier band strain and elastic shrinkage in Al-based alloys associated with mg contents. J Mater Sci Tech, 2017, 33: 580–586

    Article  Google Scholar 

  13. Cai Y L, Yang S L, Wang Y H, et al. Characterization of the deformation behaviors associated with the serrated flow of a 5456 Al-based alloy using two orthogonal digital image correlation systems. Mater Sci Eng-A, 2016, 664: 155–164

    Article  Google Scholar 

  14. Cai Y L, Yang S L, Fu S H, et al. The influence of specimen thickness on the lüders effect of a 5456 Al-based alloy: Experimental observations. Metals, 2016, 6: 120

    Article  Google Scholar 

  15. Zhang Q, Jiang Z, Jiang H, et al. On the propagation and pulsation of Portevin-Le Chatelier deformation bands: An experimental study with digital speckle pattern metrology. Int J Plasticity, 2005, 21: 2150–2173

    Article  Google Scholar 

  16. Koohbor B, Ravindran S, Kidane A. Experimental determination of representative volume element (RVE) size in woven composites. Opt Lasers Eng, 2017, 90: 59–71

    Article  Google Scholar 

  17. Xue Y, Cheng T, Xu X, et al. High-accuracy and real-time 3D positioning, tracking system for medical imaging applications based on 3D digital image correlation. Opt Lasers Eng, 2017, 88: 82–90

    Article  Google Scholar 

  18. Luo P F, Chao Y J, Sutton M A, et al. Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision. Exp Mech, 1993, 33: 123–132

    Article  Google Scholar 

  19. Sutton M A, Orteu J J, Schreier H. Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. New York: Springer Science & Business Media, 2009

    Google Scholar 

  20. Reu P L, Sutton M A, Wang Y Q, et al. Uncertainty quantification for digital image correlation. In: Proceedings of the SEM Annual Conference. Albuquerque, NM, 2009

    Google Scholar 

  21. Zappa E, Mazzoleni P, Matinmanesh A. Uncertainty assessment of digital image correlation method in dynamic applications. Opt Lasers Eng, 2014, 56: 140–151

    Article  Google Scholar 

  22. Sebastian C, Patterson E A. Calibration of a digital image correlation system. Exp Tech, 2012, 39: 21–29

    Article  Google Scholar 

  23. Hack E, Lin X, Patterson EA, et al. A reference material for establishing uncertainties in full-field displacement measurements. Meas Sci Tech, 2017, doi: 10.1088/0957-0233/26/7/075004

    Google Scholar 

  24. Wang Y Q, Sutton M A, Ke X D, et al. On error assessment in stereobased deformation measurements. Exp Mech, 2011, 51: 405–422

    Article  Google Scholar 

  25. Hu Z, Xie H, Lu J, et al. Error evaluation technique for three-dimensional digital image correlation. Appl Opt, 2011, 50: 6239–6247

    Article  Google Scholar 

  26. Ke X D, Schreier H W, Sutton M A, et al. Error assessment in stereobased deformation measurements. Exp Mech, 2011, 51: 423–441

    Article  Google Scholar 

  27. Xu X, Su Y, Cai Y, et al. Effects of various shape functions and subset size in local deformation measurements using DIC. Exp Mech, 2015, 55: 1575–1590

    Article  Google Scholar 

  28. Shi Y, Wang Y, Cai M, et al. Study on the aviation oxygen supply system based on a mechanical ventilation model. Chin J Aeronaut, 2017, doi: 10.1016/j.cja.2017.10.008

  29. Xu X, Su Y, Zhang Q. Theoretical estimation of systematic errors in local deformation measurements using digital image correlation. Opt Lasers Eng, 2017, 88: 265–279

    Article  Google Scholar 

  30. Yu L, Pan B. The errors in digital image correlation due to overmatched shape functions. Meas Sci Technol, 2015, 26: 045202

    Article  Google Scholar 

  31. Pan B, Xie H, Wang Z. Equivalence of digital image correlation criteria for pattern matching. Appl Opt, 2010, 49: 5501–5509

    Article  Google Scholar 

  32. Shao X, Dai X, He X. Noise robustness and parallel computation of the inverse compositional Gauss-Newton algorithm in digital image correlation. Opt Lasers Eng, 2015, 71: 9–19

    Article  Google Scholar 

  33. Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Machine Intell, 2000, 22: 1330–1334

    Article  Google Scholar 

  34. Bornert M, Brémand F, Doumalin P, et al. Assessment of digital image correlation measurement errors: Methodology and results. Exp Mech, 2008, 49: 353–370

    Article  Google Scholar 

Download references

Author information

Author notes

  1. These authors contributed equally to this work.

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

    TianHao Yan, Yong Su & QingChuan Zhang

Authors
  1. TianHao Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. QingChuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to QingChuan Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, T., Su, Y. & Zhang, Q. Precise 3D shape measurement of three-dimensional digital image correlation for complex surfaces. Sci. China Technol. Sci. 61, 68–73 (2018). https://doi.org/10.1007/s11431-017-9125-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-017-9125-7

Keywords

Search

Navigation