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Digital Material Surface Image Analysis for Evaluating Geometric Fatigue Crack Parameters

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Strength of Materials Aims and scope

A procedure for acquiring and analyzing digital fatigue crack propagation images is advanced, which provides automatic crack size detection and the deviation from the axis perpendicular to the loading direction. The procedure is based on comparing the original image with the subsequent ones and eliminating all elements present, only getting the changes in the fatigue crack extension. A slight shift of the images concerning each other was fixed with their cross-correlation. Choosing an optimum image segmentation level permits rejecting the elements that appear due to the digital matrix noise. The four-dimensional pixel assembly was used to get the geometric elements. As a result, a set of geometric figures characterizing the fatigue crack propagation was obtained. For analyzing the length and direction of crack components, the ellipses were applied whose second moments coincide with those of the components. The total change in the crack propagation direction and length is determined from the parameters of the vector connecting the centers of mass of the first and last components. The advanced procedure can be employed in crack resistance tests.

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References

  1. ASTM Code, E647-95a, Standard Test Method for Measurement of Fatigue Crack Growth Rates, American Society for Testing and Materials, Philadelphia (1995).

  2. A. Mohan and S. Poobal, “Crack detection using image processing: A critical review and analysis,” Alexandria Eng J, 57, No. 2, 787–798 (2018). https://doi.org/10.1016/j.aej.2017.01.020

    Article  Google Scholar 

  3. Y. Zhang, “The design of glass crack detection system based on image pre-processing technology,” in: Proc. of Information Technology and Artificial Intelligence Conference (2014), pp. 39–42.

  4. R. S. Adhikari, O. Moselhi, and A. Bagchi, “Image-based retrieval of concrete crack properties for bridge inspection,” Automat Constr, 39, 180–194 (2014).

    Article  Google Scholar 

  5. S. Y. Alam, A. Loukili, F. Grondin, and E. Rozière, “Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete,” Eng Fract Mech, 143, 17–31 (2015).

    Article  Google Scholar 

  6. I. Shivprakash and K. Sinha Sunil, “A robust approach for automatic detection and segmentation of cracks in underground pipeline images,” Image Vis Comput, 23, No. 10, 931–933 (2005).

  7. M. Rodríguez-Martín, S. Lagüela, D. González-Aguilera, and J. Martínez, “Thermographic test for the geometric characterization of cracks in welding using IR image rectification,” Automat Constr, 61, 58–65 (2016).

    Article  Google Scholar 

  8. W. S. M. Brooks, D. A. Lamb, and S. J. C. Irvine, “IR reflectance imaging for crystalline Si solar cell crack detection,” IEEE J Photovolt, 5, No. 5, 1271–1275 (2015).

    Article  Google Scholar 

  9. M. Vidal, M. Ostra, N. Imaz, et al., “Analysis of SEM digital images to quantify crack network pattern area in chromium electrodeposits,” Surf Coat Technol, 285, 289–297 (2016).

    Article  CAS  Google Scholar 

  10. G. Deng and T. Nakanishi, “Practical methods for crack length measurement and fatigue crack initiation detection using ion-sputtered film and crack growth characteristics in glass and ceramics,” in: C. Sikalidis (Ed.), Advances in Ceramics – Characterization, Raw Materials, Processing, Properties, Degradation and Healing, InTech (2011). https://doi.org/10.5772/22594

  11. O. M. Herasymchuk and O. V. Kononuchenko, "Peculiarities of short fatigue crack growth from a blind hole in specimens made of steel 45. Part 1. Experimental results,” Strength Mater, 53, No. 2, 213–221 (2021). https://doi.org/10.1007/s11223-021-00277-z

  12. R. Draelos, Convolution vs. Cross-Correlation. https://glassboxmedicine.com/2019/07/26/convolution-vscross-correlation/

Download references

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Authors and Affiliations

  1. Pisarenko Institute of Problems of Strength, National Academy of Sciences of Ukraine, Kyiv, Ukraine

    A. V. Byalonovych

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  1. A. V. Byalonovych
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Correspondence to A. V. Byalonovych.

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Translated from Problemy Mitsnosti, No. 5, pp. 103 – 111, September – October, 2023.

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Byalonovych, A.V. Digital Material Surface Image Analysis for Evaluating Geometric Fatigue Crack Parameters. Strength Mater 55, 960–966 (2023). https://doi.org/10.1007/s11223-023-00587-4

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  • DOI: https://doi.org/10.1007/s11223-023-00587-4

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