Identification of deformations of cylindrical specimens by optical method using the technique of digital image correlation

Cover Page

Cite item

Full Text

Abstract

A provision of location tolerances and their retention in the postoperative period is one of the main hard-hitting process tasks when producing long-length low-rigidity shaft-type parts. Mixed treatment – tensile straightening or thermal-power treatment is one of the technological methods intended to provide this group of geometrical indicators, including axle linearity. The efficiency improvement of this technology is impossible without knowing the features of the formation of plastic deformations distribution along the length of long-length blank parts. The paper considers the application of an optical method for controlling deformation on the surface using the method of digital image correlation at axial deformation of cylindrical parts. The work describes an experimental device for optic control of deformations when loading a specimen using digital cameras. The authors studied the influence of various modes of paint deposition to a sample (deposition rate, distance, deposition mode – continuous or pulsed) on the features of a produced speckle in the form of random distribution of mixed-size paint spots over the specimen surface; obtained histograms of the intensity distribution of various speckles. The authors carried out the experiments to identify deformations based on the technology of the local gradient digital image correlation method for the specimens of polymer tubes with different speckle types. The study identified the distribution of the deformation over the length of samples within the deformable area selected for analysis with the specified degree of smoothing provided by choice of correlation kernel size and the choice of its displacement step for fixing deformation processes with a precise error. The authors obtained distributions of axial deformations along the length of specimens and errors of deformations determination depending on a speckle nature. The study specifies necessary speckle parameters ensuring minimal error for long-length samples up to 200 mm in length and appropriate technology for paint depositing. It is a speckle with a wide range of spot sizes rarefied with their locations and the Gaussian filter image smoothing before the analysis.

About the authors

Dmitry A. Rastorguev

Togliatti State University, Togliatti

Author for correspondence.
Email: rast_73@mail.ru
ORCID iD: 0000-0001-6298-1068

PhD (Engineering), assistant professor of Chair “Equipment and Technologies of Machine Building Production”

Russian Federation

Kirill O. Semenov

Togliatti State University, Togliatti

Email: semen-tgu@yandex.ru
ORCID iD: 0000-0002-0397-4009

postgraduate student of Chair “Equipment and Technologies of Machine Building Production”

Russian Federation

References

  1. Sciammarella C.A. A Review: Optical Methods That Evaluate Displacement. Conference Proceedings of the Society for Experimental Mechanics Series, 2019, vol. 12, pp. 23–52. doi: 10.1007/978-3-319-97481-1_3.
  2. Sutton M.A., Orteu J.-J., Schreier H. Image correlation for shape, motion and deformation measurements: basic concepts, theory and applications. USA, Springer Science Publ., 2009. 321 p. doi: 10.1007/978-0-387-78747-3.
  3. Miikki K., Karakoc A., Rafiee M., Lee D.W., Vapaavuori J., Tersteegen J., Lemetti L., Jouni P. An open-source camera system for experimental measurements. SoftwareX, 2021, vol. 14, article number 100688. doi: 10.1016/j.softx.2021.100688.
  4. Blaber J., Adair B., Antoniou A. Ncorr: Open-Source 2D Digital Image Correlation Matlab Software. Experimental Mechanics, 2015, vol. 55, no. 6, pp. 1105–1122. doi: 10.1007/s11340-015-0009-1.
  5. Turner D.Z. An overview of the gradient-based local DIC formulation for motion estimation in DICe. Sandia Report. 2016. doi: 10.2172/1561808.
  6. Belloni V., Ravanelli R., Nascetti A., Rita M.Di., Mattei D., Crespi M. Py2DIC: A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics. Sensors (Switzerland), 2019, vol. 19, no. 18, article number 3832. doi: 10.3390/s19183832.
  7. Golasiński K., Pieczyska E.A., Maj M., Staszczak M., Świec P., Furuta T., Kuramoto S. Investigation of strain rate sensitivity of Gum Metal under tension using digital image correlation. Archives of Civil and Mechanical Engineering, 2020, vol. 20, no. 2, article number 53. doi: 10.1007/s43452-020-00055-9.
  8. Sutton M.A., Matta F., Rizos D., Ghorbani R., Rajan S., Mollenhauer D.H., Schreier H.W., Lasprilla A.O. Recent Progress in Digital Image Correlation: Background and Developments since the 2013 W M Murray Lecture. Experimental Mechanics, 2017, vol. 57, no. 1, pp. 1–30. doi: 10.1007/s11340-016-0233-3.
  9. Oberg M.B.A.M., de Oliveira D.F., Goulart J.N.V., Anflor C.T.M. A novel to perform a thermoelastic analysis using digital image correlation and the boundary element method. International Journal of Mechanical and Materials Engineering, 2020, vol. 15, no. 1, article number 1. doi: 10.1186/s40712-019-0115-4.
  10. Rastorguev D.A., Semenov K.O., Dema R.R., Amirov R.N., Romanenko E.F., Latypov O.R., Matveev P.A. Process support for uniformity of plastic deformation during thermal force treatment. Tekhnologiya metallov, 2021, no. 8, pp. 24–32.
  11. Panin S.V., Lyubutin P.S. Verification of a method of deformation estimation at the mesolevel on the basis of constructing displacement vector fields on the surface. Fizicheskaya mezomekhanika, 2005, vol. 8, no. 2, pp. 69–80.
  12. Panin S.V., Titkov V.V., Lyubutin P.S. Automatic determination of subset size in the problem of estimation of material strain by digital image correlation method. Vychislitelnye tekhnologii, 2015, vol. 20, no. 2, pp. 65–78.
  13. Joseph S.H. Markings for Image-Based Deformation Measurement on a Torsion Test Machine. Strain, 2009, vol. 45, no. 2, pp. 139–148. doi: 10.1111/j.1475-1305.2008.00425.x.
  14. Dong Y.L., Pan B. A Review of Speckle Pattern Fabrication and Assessment for Digital Image Correlation. Experimental Mechanics, 2017, vol. 57, no. 8, pp. 1161–1181. doi: 10.1007/s11340-017-0283-1.
  15. Bomarito G.F., Hochhalter J.D., Ruggles T.J. Cannon A.H. Increasing accuracy and precision of digital image correlation through pattern optimization. Optics and Lasers in Engineering, 2017, vol. 91, pp. 73–85. doi: 10.1016/j.optlaseng.2016.11.005.
  16. Kreopalova G.V., Lazareva N.L., Puryaev D.T. Opticheskie izmereniya [Optical measurements]. Moscow, Mashinostroenie Publ., 1987. 264 p.
  17. Nadezhdin K.D., Sharnin L.M., Kirpichnikov A.P. Visual methods of identifying deformations and stresses on the surfaces of tested structures. Vestnik Tekhnologicheskogo universiteta, 2016, vol. 19, no. 12, pp. 143–146.
  18. Lyubutin P.S., Panin S.V. Mesoscale measurement of strains by analyzing optical images of the surface of loaded solids. Journal of Applied Mechanics and Technical Physics, 2006, vol. 47, no. 6, pp. 905–910.
  19. Schreier H.W., Braasch J.R., Sutton M.A. Systematic errors in digital image correlation caused by intensity interpolation. Optical Engineering, 2000, vol. 39, no. 11, pp. 2915–2921. doi: 10.1117/1.1314593.
  20. G'sell C., Hiver J.M., Dahoun A. Experimental characterization of deformation damage in solid polymers under tension, and its interrelation with necking. International Journal of Solids and Structures, 2002, vol. 39, no. 13-14, pp. 3857–3872. doi: 10.1016/S0020-7683(02)00184-1.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c)



This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies