V.V. Avtaev, N.O. Yakovlev, V.A. Soldatov Method for measuring the tip of a fatigue crack by image intensity difference // Proceedings of VIAM. 2026. No. 7. DOI: 10.18577/2307-6046-2026-0-7-215-225. URL: https://viam-works.ru/en/journal/2026/7/17

Method for measuring the tip of a fatigue crack by image intensity difference

V.V. Avtaev, N.O. Yakovlev, V.A. Soldatov
Abstract

The paper presents an automatic measurement of fatigue crack peak based on the analysis of image intensity on the sample surface, which detects local changes in the crack development area and takes into account general and local shifts on the sample surface. The fatigue tests results showed the possibility to determine the crack tip position correctly. The proposed method can be used in non-destructive testing systems and in testing aviation materials to improve the reliability of cyclic cracking assessment.

Keywords
fatigue crack, measurement, intensity, image, FCGR, optical measurements, texture
Reference list
  1. Kablov E.N., Evgenov A.G., Bakradze M.M., Nerush S.V., Krupnina O.A. New generation materials and digital additive technologies for the production of resource parts of FSUE VIAM. Part 1. Materials and synthesis technologies. Elektrometallurgiya, 2022, no. 1, pp. 2–12. DOI: 10.31044/1684-5781-2022-0-1-2-12.
  2. Kablov E.N., Putyrskiy S.V., Yakovlev A.L., Krokhina V.A., Naprienko S.A. Study of fatigue fracture resistance of stampings made of high-strength titanium alloy VT22M, manufactured with final deformation in the (α+β)- and β-regions. Titan, 2021, no. 1 (70), pp. 26–33.
  3. Bolotin V.V. Service Life Forecasting for Machines and Structures. Moscow: Mashinostroenie, 1984, 312 p.
  4. Aviation Handbook. Calculated Values of Characteristics of Aircraft Metal Structural Materials. Moscow: TsAGI, 2012, is. 4: UAC, 118 p.
  5. Sutubalov A.I., Podzhivotov N.Yu., Shershak P.V., Yakovlev N.O. Evaluation of homogeneity of physical and mechanical properties of semi-finished products for aviation purpose. Aviation materials and technologies, 2024, no. 1 (74), pp. 121–135. Available at: http://www.journal.viam.ru (accessed: October 14, 2025). DOI: 10.18577/2713-0193-2024-0-1-121-135.
  6. Si Y., Rouse J.P., Hyde C.J. Potential difference methods for measuring crack growth: A review. International Journal of Fatigue, 2020, no. 136. DOI: 10.1016/j.ijfatigue.2020.105624.
  7. Sun H., Liu Q., Fang L. Research on fatigue crack growth detection of M (T) specimen based on image processing technology. Journal of Failure Analysis and Prevention, 2018, vol. 18, pp. 1010–1016.
  8. Rupil J., Roux S., Hild F., Vincent L. Fatigue microcrack detection with digital image correlation. The Journal of Strain Analysis for Engineering Design, 2011, vol. 46 (6), pp. 492–509.
  9. Lopez-Crespo P., Shterenlikht A., Patterson E.A. et al. The stress intensity of mixed mode cracks determined by digital image correlation. Journal Strain Analysis, 2008, vol. 43, no. 8, pp. 769–780.
  10. Du Y., Diaz F.A., Burguete R.L., Patterson E.A. Evaluation using digital image correlation of stress intensity factors in an aerospace panel. Experimental Mechanics, 2011, vol. 51, pp. 45–57.
  11. Orell O., Jokinen J., Kanerva M. Use of DIC in the characterization of mode II crack propagation in adhesive fatigue testing. International Journal of Adhesion and Adhesives, 2023, vol. 122, pp. 1–10.
  12. Indolia S., Goswami A.K., Mishra S.P., Asopa P. Conceptual understanding of convolutional neural network – a deep learning approach. Procedia Computer Science, 2018, vol. 132, pp. 679–688.
  13. Strohmann T., Starostin-Penner D., Breitbarth E., Requena G. Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks. Fatigue Fracture of Engineering Materials Structures, 2021, vol. 44, pp. 1336–1348.
  14. Ali R., Chuah J.H., Talip M.S. A. et al. Structural crack detection using deep convolutional neural networks. Automation in Construction, 2022, vol. 133. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0926580521004404 (accessed: June 03, 2026). DOI: 10.1016/j.autcon.2021.103989.
  15. Iakovlev N.O., Selivanov A.A., Gulina I.V., Grinevich A.V. Revisiting the durability of hinged-bolt connections. Aviacionnye materialy i tehnologii, 2020, no. 4 (61), pp. 79–85. DOI: 10.18577/2071-9140-2020-0-4-79-85.
  16. Morozova L.V., Levchenko A.A. Research of bimetallic holders of generator rotor after operation. Aviation materials and technologies, 2022, no. 4 (69), pp. 112–122. Available at: http://www.journal.viam.ru (accessed: October 14, 2025). DOI: 10.18577/2713-0193-2022-0-4-112-122.
  17. Matvienko Yu.G. Models and criteria of fracture mechanics. Moscow: Fizmatlit, 2006, 328 p.
  18. Avtaev V.V., Yakovlev N.O. Study of static crack resistance and fracture resistance of thin-sheet aluminum alloy using digital image correlation. Deformatsiya i razrusheniye materialov, 2020, no. 2, pp. 29–35.
  19. Erak A.D., Kiselev A.S. Correlation between fracture toughness parameters and fracture surfaces structure parameters for three-point bending tests. Aviation materials and technologies, 2023, no. 2 (71), pp. 156–166. Available at: http://www.journal.viam.ru (accessed: October 14, 2025). DOI: 10.18577/2713-0193-2023-0-2-156-166.
  20. Potapov A.S. Computer vision systems: a tutorial. St. Petersburg: ITMO University, 2016, 161 p.
  21. Gorbovets M.A., Khodinev I.A., Monin S.A. Tests of structural metallic materials for the fatigue crack growth rate in a corrosive environment (review). Trudy VIAM, 2022, no. 12 (118), pp. 135–144. Available at: http://www.viam-works.ru (accessed: October 14, 2025). DOI: 10.18577/2307-6046-2022-0-12-135-144.