DEFECTOMETRY OF HIDDEN CRACKS IN METALS BY THEIR MAGNETO-OPTICAL IMAGES
DOI:
https://doi.org/10.15407/dopovidi2026.04.067Keywords:
non-destructive testing of products, magnetic field, fatigue crack, magneto-optical image, image processingAbstract
A new method for the acquisition and processing of magneto-optical images of hidden crack-like defects in metal products using a crystalline magneto-optical film is proposed. A device for capturing magneto-optical images of ferromagnetic materials based on the Faraday effect has been developed. The use of oblique illumination in the device’s design, along with an additional compensating prism placed in front of the analyzer, has made it possible to increase the inspection area, enhance sensitivity to the magnetic field scattered by defects, and ensure high contrast in the magneto-optical image of the entire inspected area. Experimental testing of this device on steel specimens with fatigue cracks demonstrated its effectiveness. To improve the reliability of defect detection, an algorithm for processing magneto-optical images was proposed; this algorithm is designed to segment and locate cracks by taking into account the characteristic features of the image. An assessment of crack width was performed based on an analysis of the normalized intensity distribution of a magneto-optical image along cross-sections of the crack. Theoretical calculations were performed to determine the distribution of the scattered magnetic field above the surface of a magnetized object at defect locations. Using a dipole model and a non-uniform distribution of magnetic charges along the edges of the defect, the profiles of the vertical component of the magnetic field scattered by the defect were calculated. The topography of the defect field as a function of its geometric parameters was obtained. The results obtained are in good agreement with experimental data. The results of the study indicate the effectiveness of the proposed device and methods for processing magneto-optical images for the non-destructive testing of steel structural elements and products.
Downloads
References
Gerken, M., Sievers, S. & Schumacher, H. W. (2020). Inhomogeneous field calibration of a magneto-optical indicator film device. Meas. Sci. Technol., 31, No. 7, 075009. https://doi.org/10.1088/1361-6501/ab816e
Dorosinskiy, L. & Sievers, S. (2023). Magneto-optical indicator films: fabrication, principles of operation, calibration, and applications. Sensors, 23, No. 8, 4048. https://doi.org/10.3390/s23084048
Agalidi, Y., Kozhukhar, P., Levyi, S. & Turbin, D. (2015). Enhanced magneto-optical imaging of internal stresses in the removed surface layer. Nondestructive Testing and Evaluation, 30, No. 4, pp. 347-355. https://doi.org/10.1080/10589759.2015.1044527
Li, Y., Gao, X., Zhang, Y., You, D., Zhang, N., Wang, C. & Wang, C. (2019). Detection model of invisible weld defects by magneto-optical imaging at rotating magnetic field directions. Opt. Laser Technol., 121, 105772. https://doi.org/10.1016/j.optlastec.2019.105772
Neu, V., Pedrini, G., Soldatov, I., Reichelt, S. & Schäfer, R. (2025). Lensless magneto-optical imaging. Sci. Rep., 15, No. 1, 28277. https://doi.org/10.1038/s41598-025-10005-1
Maksymenko, O. P., Voronyak, T. I., Stasyshyn, I. V. & Syvorotka, I. I. (2024). Evaluation of the characteristics of ferrite garnet films for magneto-optical control of materials. Information Extraction and Processing, Iss. 52, pp. 61-67 (in Ukrainian). https://doi.org/10.15407/vidbir2024.52.061
Stasyshyn, I. & Maksymenko, O. (2025, September). Visualization of hidden cracks in ferromagnetic materials by magnetoptical method. International Young Scientists Conference on Materials science and surface engineering (pp. 276-279). Lviv. https://doi.org/10.15407/msse2025.01.276
Stasyshyn, I. V., Maksymenko, O. P., Voronyak, T. I., Ivasenko, I. B., Berehulyak, O. R. & Stetsko, I. H. (2025). Application of magneto-optical method for non-destructive testing of riveted joints. Information Extraction and Processing, Iss. 53, pp. 58-64 (in Ukrainian). https://doi.org/10.15407/vidbir2025.53.058
Maksymenko, O. P. & Suriadova, O. D. (2021). Application of magneto-optical method for detection of material structure changes. Information Extraction and Processing, Iss. 49, pp. 32-36 (in Ukrainian). https://doi.org/10.15407/vidbir2021.49.032
Tian, F., Zhao, Y., Che, X., Zhao, Y. & Xin, D. (2019). Concrete crack identification and image mosaic based on image processing. Appl. Sci., 9, No. 22, 4826. https://doi.org/10.3390/app9224826
Prasetyo, A., Purnama, I. K. E., Yuniarno, E. M. & Suprobo, P. (2025). Improving crack detection precision of concrete structures using U-Net architecture and novel DBCE loss function. IEEE Access, 13, pp. 20903-20922. https://doi.org/10.1109/access.2025.3534803
Laxman, K. C., Tabassum, N., Ai, L., Cole, C. & Ziehl, P. (2023). Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning. Constr. Build. Mater., 370, 130709. https://doi.org/10.1016/j.conbuildmat.2023.130709
Kasahara, K., Wang, S., Ishibashi, T. & Manago, T. (2019). Magneto-optical images of submicron-size Bi-substituted YIG patterns prepared by electron-beam irradiated metal-organic decomposition. Jpn. J. Appl. Phys., 58, No. 6, 060906. https://doi.org/10.7567/1347-4065/ab1fc7
Hashimoto, R., Itaya, T., Uchida, H., Funaki, Y. & Fukuchi, S. (2022). Properties of magnetic garnet films for flexible magneto-optical indicators fabricated by spin-coating method. Materials, 15, No. 3, 1241. https://doi.org/10.3390/ma15031241
Feng, B., Wu, J., Tu, H., Tang, J. & Kang, Y. (2022). A review of magnetic flux leakage nondestructive testing. Materials, 15, No. 20, 7362. https://doi.org/10.3390/ma15207362
Li, H., Chen, Z., Zhang, D. & Sun, H. (2016). Reconstruction of magnetic charge on breaking flaw based on two-layers algorithm. Int. J. Appl. Electromagn. Mech., 52, pp. 1133-1139. https://doi.org/10.3233/JAE-162148
Trevino, D. A. G., Dutta, S. M., Ghorbel, F. H. & Karkoub, M. (2015). An improved dipole model of 3-D magnetic flux leakage. IEEE Trans. Magn., 52, No. 12, pp. 1-7. https://doi.org/10.1109/tmag.2015.2475429
Förster, F. (1986). New findings in the field of non-destructive magnetic leakage field inspection. NDT Int., 19, No. 1, pp. 3-14. https://doi.org/10.1016/0308-9126(86)90134-3
Liu, Q., Ye, G., Gao, X., Zhang, Y. & Gao, P. P. (2023). Magneto-optical imaging nondestructive testing of welding defects based on image fusion. NDT & E Int., 138, 102887. https://doi.org/10.1016/j.ndteint.2023.102887
He, J., Gao, X., Yang, H., Gao, P. & Zhang, Y. (2024). Analysis of image formation laws and enhancement methods for weld seam defects based on infrared and magneto-optical sensor technology. J. Nondestruct. Eval., 43, No. 4, 101. https://doi.org/10.1007/s10921-024-01118-0
Fujita, T., Sakaguchi, H., Zhang, J., Nonaka, H., Sumi, S., Awano, H. & Ishibashi, T. (2022). Magneto-optical diffractive deep neural network. Opt. Express, 30, No. 20, 36889. https://doi.org/10.1364/oe.470513
Luo, J., Ying, K. & Bai, J. (2005). Savitzky—Golay smoothing and differentiation filter for even number data. Signal Process., 85, No. 7, pp. 1429-1434. https://doi.org/10.1016/j.sigpro.2005.02.002
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Reports of the National Academy of Sciences of Ukraine

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

