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Experimental Mechanics

, Volume 59, Issue 7, pp 1021–1032 | Cite as

Application of Digital Image Correlation (DIC) to the Measurement of Strain Concentration of a PVA Dual-Crosslink Hydrogel Under Large Deformation

  • M. Liu
  • J. Guo
  • C.-Y. Hui
  • A. T. ZehnderEmail author
Article

Abstract

Hydrogels are a class of soft, highly deformable materials formed by swelling a network of polymer chains in water. With mechanical properties that mimic biological materials, hydrogels are often proposed for load bearing biomedical or other applications in which their deformation and failure properties will be important. To study the failure of such materials a means for the measurement of deformation fields beyond simple uniaxial tension tests is required. As a non-contact, full-field deformation measurement method, Digital Image Correlation (DIC) is a good candidate for such studies. The application of DIC to hydrogels is studied here with the goal of establishing the accuracy of DIC when applied to hydrogels in the presence of large strains and large strain gradients. Experimental details such as how to form a durable speckle pattern on a material that is 90% water are discussed. DIC is used to measure the strain field in tension loaded samples containing a central hole, a circular edge notch and a sharp crack. Using a nonlinear, large deformation constitutive model, these experiments are modeled using the finite element method (FEM). Excellent agreement between FEM and DIC results for all three geometries shows that the DIC measurements are accurate up to strains of over 10, even in the presence of very high strain gradients near a crack tip. The method is then applied to verify a theoretical prediction that the deformation field in a cracked sample under relaxation loading, i.e. constant applied boundary displacement, is stationary in time even as the stress relaxes by a factor of three.

Keywords

Digital image correlation Hydrogel Large deformation Finite element simulation Viscoelastic Crack tip field 

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. CMMI -1537087.

References

  1. 1.
    Kwon HJ, Yasuda K, Gong JP, Ohmiya Y (2014) Polyelectrolyte hydrogels for replacement and regeneration of biological tissues. Macromol Res 22(3):227–235CrossRefGoogle Scholar
  2. 2.
    Qiu Y, Park K (2001) Environment-sensitive hydrogels for drug delivery. Adv Drug Deliv Rev 53(3):321–339CrossRefGoogle Scholar
  3. 3.
    Gong JP, Katsuyama Y, Kurokawa T, Osada Y (2003) Double-Network Hydrogels with Extremely High Mechanical Strength. Adv Mater 15(14):1155–1158CrossRefGoogle Scholar
  4. 4.
    Webber RE, Creton C, Brown HR, Gong JP (2007) Large Strain Hysteresis and Mullins Effect of Tough Double-Network Hydrogels. Macromolecules 40(8):2919–2927CrossRefGoogle Scholar
  5. 5.
    Henderson KJ, Zhou TC, Otim KJ, Shull KR (2010) Ionically Cross-Linked Triblock Copolymer Hydrogels with High Strength. Macromolecules 43(14):6193–6201CrossRefGoogle Scholar
  6. 6.
    Sun TL et al (2013) Physical hydrogels composed of polyampholytes demonstrate high toughness and viscoelasticity. Nat Mater 12(10):932–937CrossRefGoogle Scholar
  7. 7.
    Mayumi K, Marcellan A, Ducouret G, Creton C, Narita T (2013) Stress–Strain Relationship of Highly Stretchable Dual Cross-Link Gels: Separability of Strain and Time Effect. ACS Macro Lett 2(12):1065–1068CrossRefGoogle Scholar
  8. 8.
    Long R, Mayumi K, Creton C, Narita T, Hui C-Y (2014) Time Dependent Behavior of a Dual Cross-Link Self-Healing Gel: Theory and Experiments. Macromolecules 47(20):7243–7250CrossRefGoogle Scholar
  9. 9.
    Guo J, Long R, Mayumi K, Hui C-Y (2016) Mechanics of a Dual Cross-Link Gel with Dynamic Bonds: Steady State Kinetics and Large Deformation Effects. Macromolecules 49(9):3497–3507CrossRefGoogle Scholar
  10. 10.
    Long R, Mayumi K, Creton C, Narita T, Hui C-Y (2015) Rheology of a dual crosslink self-healing gel: Theory and measurement using parallel-plate torsional rheometry. J Rheol 59(3):643–665CrossRefGoogle Scholar
  11. 11.
    Guo J et al. (2018) Fracture mechanics of a self-healing hydrogel with covalent and physical crosslinks: A numerical study, Journal of the Mechanics and Physics of SolidsGoogle Scholar
  12. 12.
    Liu M, Guo J, Hui C-Y, Creton C, Narita T, Zehnder A (2018) Time-temperature equivalence in a PVA dual cross-link self-healing hydrogel. J Rheol 62(4):991–1000CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Khoo S-W, Karuppanan S, Tan C-S (2016) A Review of Surface Deformation and Strain Measurement Using Two-Dimensional Digital Image Correlation, Metrology and Measurement Systems, vol. 23, no. 3Google Scholar
  15. 15.
    Schreier H, Orteu J-J, Sutton MA (2009) Image Correlation for Shape, Motion and Deformation Measurements. Springer US, BostonCrossRefGoogle Scholar
  16. 16.
    Kwon HJ, Rogalsky AD, Kovalchick C, Ravichandran G (2010) Application of digital image correlation method to biogel. Polym Eng Sci 50(8):1585–1593CrossRefGoogle Scholar
  17. 17.
    Sasson A, Patchornik S, Eliasy R, Robinson D, Haj-Ali R (2012) Hyperelastic mechanical behavior of chitosan hydrogels for nucleus pulposus replacement—Experimental testing and constitutive modeling. J Mech Behav Biomed Mater 8:143–153CrossRefGoogle Scholar
  18. 18.
    Leibinger A et al (2016) Soft Tissue Phantoms for Realistic Needle Insertion: A Comparative Study. Ann Biomed Eng 44(8):2442–2452CrossRefGoogle Scholar
  19. 19.
    Hong Y, Sarntinoranont M, Subhash G, Canchi S, King MA (2016) Localized Tissue Surrogate Deformation due to Controlled Single Bubble Cavitation. Exp Mech 56(1):97–109CrossRefGoogle Scholar
  20. 20.
    Christensen K, Davis B, Jin Y, Huang Y (2018) Effects of printing-induced interfaces on localized strain within 3D printed hydrogel structures. Mater Sci Eng C 89:65–74CrossRefGoogle Scholar
  21. 21.
    Wyss CS, Karami P, Bourban P-E, Pioletti DP (2018) Cyclic loading of a cellulose/hydrogel composite increases its fracture strength. Extreme Mech Lett 24:66–74CrossRefGoogle Scholar
  22. 22.
    Haddadi H, Belhabib S (2008) Use of rigid-body motion for the investigation and estimation of the measurement errors related to digital image correlation technique. Opt Lasers Eng 46(2):185–196CrossRefGoogle Scholar
  23. 23.
    Pan B, Yu L, Wu D, Tang L (2013) Systematic errors in two-dimensional digital image correlation due to lens distortion. Opt Lasers Eng 51(2):140–147CrossRefGoogle Scholar
  24. 24.
    Sutton MA, Yan JH, Tiwari V, Schreier HW, Orteu JJ (2008) The effect of out-of-plane motion on 2D and 3D digital image correlation measurements. Opt Lasers Eng 46(10):746–757CrossRefGoogle Scholar
  25. 25.
    Jerabek M, Major Z, Lang RW (2010) Strain determination of polymeric materials using digital image correlation. Polym Test 29(3):407–416CrossRefGoogle Scholar
  26. 26.
    Hoult NA, Andy Take W, Lee C, Dutton M (2013) Experimental accuracy of two dimensional strain measurements using Digital Image Correlation. Eng Struct 46:718–726CrossRefGoogle Scholar
  27. 27.
    Goh CP, Ismail H, Yen KS, Ratnam MM (2017) Single-step scanner-based digital image correlation (SB-DIC) method for large deformation mapping in rubber. Opt Lasers Eng 88:167–177CrossRefGoogle Scholar
  28. 28.
    Moerman KM, Holt CA, Evans SL, Simms CK (2009) Digital image correlation and finite element modelling as a method to determine mechanical properties of human soft tissue in vivo. J Biomech 42(8):1150–1153CrossRefGoogle Scholar
  29. 29.
    Horst CR, Brodland B, Jones LW, Brodland GW (2012) Measuring the Modulus of Silicone Hydrogel Contact Lenses. Optom Vis Sci 89(10):1468–1476CrossRefGoogle Scholar
  30. 30.
    Dicker MP, Bond IP, Rossiter JM, Faul CF, Weaver PM (2015) Modelling and Analysis of pH Responsive Hydrogels for the Development of Biomimetic Photo-Actuating Structures, MRS Proceedings, vol. 1718Google Scholar
  31. 31.
    Subhash G, Liu Q, Moore DF, Ifju PG, Haile MA (2011) Concentration Dependence of Tensile Behavior in Agarose Gel Using Digital Image Correlation. Exp Mech 51(2):255–262CrossRefGoogle Scholar
  32. 32.
    Mac Donald K, Ravichandran G (2019) An Experimental Method to Induce and Measure Crack Propagation in Brittle Polymers with Heterogeneities, in Fracture, Fatigue, Failure and Damage Evolution, Volume 6, pp. 21–23Google Scholar
  33. 33.
    Alshehri AM, Wilson OC, Dahal B, Philip J, Luo X, Raub CB (2017) Magnetic nanoparticle-loaded alginate beads for local micro-actuation of in vitro tissue constructs. Colloids Surf B: Biointerfaces 159:945–955CrossRefGoogle Scholar
  34. 34.
    Skulborstad AJ, Wang Y, Davidson JD, Swartz SM, Goulbourne NC (2013) Polarized Image Correlation for Large Deformation Fiber Kinematics. Exp Mech 53(8):1405–1413CrossRefGoogle Scholar
  35. 35.
    A. Hijazi, A. Friedl, and C. J. Kähler, “Influence of camera’s optical axis non-perpendicularity on measurement accuracy of two-dimensional digital image correlation,” vol. 5, no. 4, p. 10, 2011Google Scholar
  36. 36.
    Blaber J, Adair B, Antoniou A (2015) Ncorr: Open-Source 2D Digital Image Correlation Matlab Software. Exp Mech 55(6):1105–1122CrossRefGoogle Scholar
  37. 37.
    Pan B (2009) Reliability-guided digital image correlation for image deformation measurement. Appl Opt 48(8):1535CrossRefGoogle Scholar
  38. 38.
    Pan B, Dafang W, Yong X (2012) Incremental calculation for large deformation measurement using reliability-guided digital image correlation. Opt Lasers Eng 50(4):586–592CrossRefGoogle Scholar
  39. 39.
    Dong YL, Pan B (2017) A Review of Speckle Pattern Fabrication and Assessment for Digital Image Correlation. Exp Mech 57(8):1161–1181CrossRefGoogle Scholar
  40. 40.
    Crammond G, Boyd SW, Dulieu-Barton JM (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378CrossRefGoogle Scholar
  41. 41.
    Hua T, Xie H, Wang S, Hu Z, Chen P, Zhang Q (2011) Evaluation of the quality of a speckle pattern in the digital image correlation method by mean subset fluctuation. Opt Laser Technol 43(1):9–13CrossRefGoogle Scholar
  42. 42.
    Lecompte D et al (2006) Quality assessment of speckle patterns for digital image correlation. Opt Lasers Eng 44(11):1132–1145CrossRefGoogle Scholar
  43. 43.
    Park J, Yoon S, Kwon T-H, Park K (2017) Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology. Opt Lasers Eng 91:62–72CrossRefGoogle Scholar
  44. 44.
    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
  45. 45.
    Guo J, Hui CY, Liu M, Zehnder AT (2019) The stress field near the tip of a plane stress crack in a gel consisting of chemical and physical cross-links, Submitted to Proceedings of the Royal Society AGoogle Scholar
  46. 46.
    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):7037CrossRefGoogle Scholar
  47. 47.
    Yaofeng S, Pang JHL (2007) Study of optimal subset size in digital image correlation of speckle pattern images. Opt Lasers Eng 45(9):967–974CrossRefGoogle Scholar

Copyright information

© Society for Experimental Mechanics 2019

Authors and Affiliations

  1. 1.Sibley School of Mechanical and Aerospace Engineering, Field of Theoretical & Applied MechanicsCornell UniversityIthacaUSA

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