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Self-Adaptive Digital Volume Correlation for Unknown Deformation Fields

  • B. Wang
  • B. PanEmail author
Article
  • 140 Downloads

Abstract

Digital volume correlation (DVC) has evolved into a powerful tool for quantifying full-field internal deformation. In existing subvolume-based local DVC, subvolume size and shape function are two key user-defined parameters closely related to the DVC measurement errors. In routine implementation, the user must define fixed subvolume size and shape function according to prior experience and intuition, which cannot ensure accurate measurements, particularly for unknown complex heterogeneous deformation fields. Self-adaptive selection of optimal subvolume size and the best shape function is therefore highly desirable to realize full-automatic and quality DVC measurements. In this work, we first establish theoretical error models that relate total displacement errors to subvolume sizes and shape functions. By minimizing the V-shaped models of theoretically predicted total errors, optimal subvolume size and the best shape function can be identified as inputs for self-adaptive DVC analysis at each calculation point. The accuracy advantage of the presented self-adaptive DVC approach over classic one using fixed subvolume size and shape function is demonstrated through numerically simulated three-point bending tests.

Keywords

Digital volume correlation Optimal subvolume size Best shape function Adaptive optimization 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) (11872009, 11632010), the National Key Research and Development Program of China (2018YFB0703500), the Aeronautical Science Foundation of China (ASFC) (2016ZD51034), and State Key Laboratory of Traction Power of Southwest Jiaotong University (Grant No. TPL1607).

References

  1. 1.
    Bay BK, Smith TS, Fyhrie DP et al (1999) Digital volume correlation: three-dimensional strain mapping using X-ray tomography. Exp Mech 39(3):217–226CrossRefGoogle Scholar
  2. 2.
    Pan B, Qian K, Xie H, Asundi A (2009) Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Meas Sci Technol 20(6):062001CrossRefGoogle Scholar
  3. 3.
    Pan B (2018) Digital image correlation for surface deformation measurements: historical developments, recent advances and future goals. Meas Sci Technol 29(8):082001CrossRefGoogle Scholar
  4. 4.
    Bay BK (2008) Methods and applications of digital volume correlation. J Strain Anal Eng Des 43(8):745–760CrossRefGoogle Scholar
  5. 5.
    Fedele R, Ciani A, Fiori F (2014) X-ray microtomography under loading and 3D-volume digital image correlation. A review. Fund Inform 135(1–2):171–197MathSciNetzbMATHGoogle Scholar
  6. 6.
    Roberts BC, Perilli E, Reynolds KJ (2014) Application of the digital volume correlation technique for the measurement of displacement and strain fields in bone: a literature review. J Biomech 47(5):923–934CrossRefGoogle Scholar
  7. 7.
    Buljac A, Jailin C, Mendoza A, Neggers J, Taillandier-Thomas T, Bouterf A et al (2018) Digital volume correlation: review of Progress and challenges. Exp Mech 58(5):661–708CrossRefGoogle Scholar
  8. 8.
    Borsdorf A, Raupach R, Flohr T, Hornegger J (2008) Wavelet based noise reduction in CT-images using correlation analysis. IEEE Trans Med Imaging 27(12):1685–1703CrossRefGoogle Scholar
  9. 9.
    Momose A, Takeda T, Itai Y, Hirano K (1996) Phase-contrast X-ray computed tomography for observing biological soft tissues. Nat Med 2(4):473–475CrossRefGoogle Scholar
  10. 10.
    Boas FE, Fleischmann D (2012) CT artifacts: causes and reduction techniques. Imaging Med 4(2):229–240CrossRefGoogle Scholar
  11. 11.
    Verhulp E, van Rietbergen B, Huiskes R (2004) A three-dimensional digital image correlation technique for strain measurements in microstructures. J Biomech 37(9):1313–1320CrossRefGoogle Scholar
  12. 12.
    Jandejsek I, Jiroušek O, Vavřík D (2011) Precise strain measurement in complex materials using digital volumetric correlation and time lapse micro-CT data. Procedia Eng 10:1730–1735CrossRefGoogle Scholar
  13. 13.
    Leclerc H, Périé JN, Roux S, Hild F (2011) Voxel-scale digital volume correlation. Exp Mech 51(4):479–490CrossRefGoogle Scholar
  14. 14.
    Pan B, Wu D, Wang Z (2012) Internal displacement and strain measurement using digital volume correlation: a least-squares framework. Meas Sci Technol 23(4):045002CrossRefGoogle Scholar
  15. 15.
    Pan B, Wang B, Wu D, Lubineau G (2014) An efficient and accurate 3D displacements tracking strategy for digital volume correlation. Opt Lasers Eng 58:126–135CrossRefGoogle Scholar
  16. 16.
    Pan B, Wang B (2017) A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images. Meas Sci Technol 28(10):105007CrossRefGoogle Scholar
  17. 17.
    Wang B, Pan B (2018) Incremental digital volume correlation method with nearest subvolume offset: an accurate and simple approach for large deformation measurement. Adv Eng Softw 116:80–88CrossRefGoogle Scholar
  18. 18.
    Gomes Perini LA, Passieux JC, Périé JN (2014) A multigrid PGD-based algorithm for volumetric displacement fields measurements. Strain 50(4):355–367CrossRefGoogle Scholar
  19. 19.
    Schreier HW, Sutton MA (2002) Systematic errors in digital image correlation due to undermatched subset shape functions. Exp Mech 42(3):303–310CrossRefGoogle Scholar
  20. 20.
    Lu H, Cary PD (2000) Deformation measurements by digital image correlation: implementation of a second-order displacement gradient. Exp Mech 40(4):393–400CrossRefGoogle Scholar
  21. 21.
    Yu L, Pan B (2015) The errors in digital image correlation due to overmatched shape functions. Meas Sci Technol 26(4):045202CrossRefGoogle Scholar
  22. 22.
    Wang B, Pan B (2015) Random errors in digital image correlation due to matched or overmatched shape functions. Exp Mech 55(9):1717–1727CrossRefGoogle Scholar
  23. 23.
    Xu X, Su Y, Zhang Q (2017) Theoretical estimation of systematic errors in local deformation measurements using digital image correlation. Opt Lasers Eng 88:265–279CrossRefGoogle Scholar
  24. 24.
    Pan B, Xie H, Wang Z, Qian K, Wang Z (2008) Study on subset size selection in digital image correlation for speckle patterns. Opt Express 16(10):7037–7048CrossRefGoogle Scholar
  25. 25.
    Wang YQ, Sutton MA, Bruck HA, Schreier HW (2009) Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements. Strain 45(2):160–178CrossRefGoogle Scholar
  26. 26.
    Wang Y, Lava P, Reu P, Debruyne D (2016) Theoretical analysis on the measurement errors of local 2D DIC: part I temporal and spatial uncertainty quantification of displacement measurements. Strain 52(2):110–128CrossRefGoogle Scholar
  27. 27.
    Yuan Y, Huang J, Peng X, Xiong C, Fang J, Yuan F (2014) Accurate displacement measurement via a self-adaptive digital image correlation method based on a weighted ZNSSD criterion. Opt Lasers Eng 52:75–85CrossRefGoogle Scholar
  28. 28.
    Gates M, Gonzalez J, Lambros J, Heath MT (2015) Subset refinement for digital volume correlation: numerical and experimental applications. Exp Mech 55(1):245–259CrossRefGoogle Scholar
  29. 29.
    Wittevrongel L, Lava P, Lomov SV, Debruyne D (2015) A self adaptive global digital image correlation algorithm. Exp Mech 55(2):361–378CrossRefGoogle Scholar
  30. 30.
    Pan B, Wang B (2016) Digital image correlation with enhanced accuracy and efficiency: a comparison of two subpixel registration algorithms. Exp Mech 8(56):1395–1409CrossRefGoogle Scholar
  31. 31.
    Pan B (2013) Bias error reduction of digital image correlation using Gaussian pre-filtering. Opt Lasers Eng 51(10):1161–1167CrossRefGoogle Scholar
  32. 32.
    Pan B, Lu Z, Xie H (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):469–477CrossRefGoogle Scholar
  33. 33.
    Pan B, Asundi A, Xie H, Gao J (2009) Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements. Opt Lasers Eng 47(7):865–874CrossRefGoogle Scholar
  34. 34.
    Tai SC, Yang SM (2008) A fast method for image noise estimation using Laplacian operator and adaptive edge detection. In Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on. IEEE, pp 1077–1081Google Scholar
  35. 35.
    Murphy MJ, Wei Z, Fatyga M, Williamson J, Anscher M, Wallace T, Weiss E (2008) How does CT image noise affect 3D deformable image registration for image-guided radiotherapy planning? Med Phys 35(3):1145–1153CrossRefGoogle Scholar
  36. 36.
    Fu J, Pierron F, Ruiz PD (2013) Elastic stiffness characterization using three-dimensional full-field deformation obtained with optical coherence tomography and digital volume correlation. J Biomed Opt 18(12):121512CrossRefGoogle Scholar
  37. 37.
    Valle V, Bokam P, Germaneau A, Hedan S (2018) New development of digital volume correlation for the study of fractured materials. Exp Mech:1–15.  https://doi.org/10.1007/s11340-018-0415-2
  38. 38.
    Bar-Kochba E, Toyjanova J, Andrews E, Kim KS, Franck C (2015) A fast iterative digital volume correlation algorithm for large deformations. Exp Mech 55(1):261–274CrossRefGoogle Scholar

Copyright information

© Society for Experimental Mechanics 2018

Authors and Affiliations

  1. 1.Institute of Solid MechanicsBeihang UniversityBeijingChina

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