Розпізнавання мінералогічних різновидів залізної руди із застосуванням методів безконтактних неруйнівних вимірювань

N.V. Morkun; S.M. Hryshchenko; А.М. Matsui; Т.А. Oliinyk

doi:10.33216/1998-7927-2026-299-1-81-91

Authors

N.V. Morkun Ivan Franko National University of Lviv, Lviv city
S.M. Hryshchenko State Tax University, Irpin city
А.М. Matsui Central Ukrainian National Technical University, Kropyvnytskyi city
Т.А. Oliinyk Kryvyi Rih National University, Kryvyi Rih city

DOI:

https://doi.org/10.33216/1998-7927-2026-299-1-81-91

Keywords:

ore, varieties, electromagnetic conversion, modeling, automation, drilling

Abstract

Eddy current and ultrasonic measurement methods combine the possibility of their simultaneous effective application through electromagnetic conversion of a single probing signal in the process of researching the physical, mechanical, chemical, and mineralogical characteristics of ore materials. The determination and justification of the characteristic features of this conversion of a pulsed electromagnetic signal in a ferromagnetic medium is a key task for recognizing the mineralogical varieties of iron ore in the studied deposit. To solve this problem, an analysis of international experience in the field of non-contact non-destructive testing of material characteristics was performed, using computer modeling and intelligent methods of analysis and classification of measurement results. Based on the research results, it is proposed to use a combined electromagnetic converter for recognizing mineralogical varieties of iron ore by means of non-contact non-destructive measurements. The combined transducer uses an electromagnetic field to generate eddy currents in the environment under study and converts electromagnetic signals into elastic vibrations of ferromagnetic rock. The parameters of the secondary magnetic field formed by eddy currents, the amplitude and frequency of elastic acoustic vibrations depend on the content and distribution structure of the ferromagnetic component in the rock, the physical and mechanical characteristics and the state of the rock mass. The modeled effect of eddy currents on the results of conversion by generating a magnetomotive force that counteracts changes in magnetic flux. In this case, parasitic effects are taken into account using elements that simulate the series resistance of magnetic flux and the parallel permeability of its scattering. The formed probing electromagnetic signal has a periodic pulsed sinusoidal character. A special controlled signal simulates the variable characteristics (magnetic resistance taking into account eddy currents) of the environment under study. Thus, the electromagnetic transducer generates an eddy current signal and elastic vibrations directly in the area where the characteristics of ferromagnetic rocks of the mountain massif are measured. Since there are no intermediate elements for transmitting the probing signal into the medium, there are no measurement errors in its characteristics caused by these factors. The determined parameters of the probing electromagnetic pulse and its spectral characteristics are used to recognize the mineralogical varieties of iron ore in the studied deposit. The application of the results of eddy current conversion in addition to ultrasonic measurements made it possible to increase the recognition quality to 93-94.5%.

References

1. Rodriguez-Sotelo J.C., Quintero J.D., and Herrera-Ruiz G. Magnetic core loss modeling in electrical machines: A comprehensive review. Micromachines. 2022, vol.13(3). 418. https://doi.org/10.3390/mi13030418.

2. Harms J. and Kern T. A. Theory and Modeling of Eddy Current Type Inductive Conductivity Sensors. Engineering Proceedings. 2021, vol. 6(1), pp. 37. https://doi.org/10.3390/I3S2021Dresden-10103

3. Mörée R. Modelling of iron losses in electric al machines: State-of-the-art and future trends. Doctoral Thesis, Uppsala University. 2024.

4. Meng B., Zhuang Z., Ma J. and Zhao S. Study on the effect of pulsed eddy current lift-off characteristics for feeding metallic foreign objects detection in coal mine crushers. Scientific Reports. 2025, vol. 15, 23775. https://doi.org/10.1038/s41598-025-08185-x.

5. Bishop C.M., & Bishop H. Deep Learning: Foundations and Concepts. Springer Nature. 2023, 656 p. ISBN 978-3-031-45468-4 (eBook) https://doi.org/10.1007/978-3-031-45468-4.

6. Shukla K., Di Leoni P.C., Blackshire J., Sparkman D., and Karniadakis G.E. Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks. Journal of Nondestructive Evaluation. 2020, vol. 39, pp.1-20. https://doi.org/10.48550/arXiv.2005.03596.

7. Hellier C. Handbook of Nondestructive Evaluation, 3E. Third edition. New York, N.Y.: McGraw-Hill Education. 2020. ISBN-13 978-1260441437.

8. Thon A., Painchaud-April G., Le Duff A., and Bélanger P. Development of a linear array electromagnetic acoustic transducer for shear horizontal guided wave inspection. NDT & E International. 2023, vol. 136, 102807. https://doi.org/10.1016/j.ndteint.2023.102807.

9. Wang Z., Wang S., Wang Q., Zhao W. and Huang S. Development of a Helical Lamb Wave Electromagnetic Acoustic Transducer for Pipeline Inspection. In IEEE Sensors Journal. 2020, vol. 20(17), pp. 9715-9723, doi: 10.1109/JSEN.2020.2990795.

10. Bao L., Li, Z., Dixon, S., Yang Yu, Y., and Guofu Zhai G. Development of a magnetostrictive Fe3O4-film electromagnetic acoustic transducer. Sensors and Actuators A: Physical. 2023, vol. 361, 16, 114593. https://doi.org/10.1016/j.sna.2023.114593

11. Morkun V., Morkun N., Hryshchenko S., Gaponenko I., Gaponenko A., and Bobrov Ye. Modelling of an electromagnetic acoustic transducer. Mining Journal of Kryvyi Rih National University. 2023. vol. 57(1), pp.88-95.

12. Morkun V., Morkun N., Gaponenko A. and Bobrov Ye. Methods of ultrasonic wave generation in the practice of non-destructive measurements. Journal of Kryvyi Rih National University. 2023, vol. 21(1), pp. 54-62. https://doi.org/10.31721/2306-5451-2023-1-56-54-62.

13. Morkun V., Morkun N., Fischerauer G., Tron V., Haponenko A., and Bobrov Y. Identification of mineralogical ore varieties using ultrasonic measurement results. Mining of Mineral Deposits. 2024. vol. 18(3), pp. 1-8. https://doi.org/10.33271/mining18.03.001.

14. Munir N., Park J., Kim H.-J., Song S.-J., and Kang S.-S. Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder. Ndt & E Internationa. 2020, vol. 111, article number 102218. https://doi.org/10.1016/j.ndteint.2020.102218.

15. Niyirora R., Masengesho W. Ji, E., Munyaneza J., Niyonyungu F., and Nyirandayisabye R. Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: a systematic review. Results in Engineering. 2022, 16, article number 100761. https://doi.org/10.1016/j.rineng.2022.100761.

16. Ghafoor I., Peter W.T., Munir W.T., and Trappey A.J. Non-contact detection of railhead defects and their classification by using convolutional neural network. Optik. (Stuttg). 2022, vol. 253, article number 168607. https://doi.org/10.1016/j.ijleo.2022.168607.

17. Sun F., Fan M., Cao B., Ye B., Lu G., and Li W. Cross-Correlation Inspired Residual Network for Pulsed Eddy Current Imaging and Detecting of Subsurface Defects. IEEE Transactions on Industrial Electronics.2023, vol. 70(12), pp.12860 -12871, DOI: 10.1109/TIE.2023.3239862.

18. Stawicki O., Ahlbrink R., and Schröer K. Shallow internal corrosion sensor technology for heavy pipe wall inspection. 2009. URL: https://www.researchgate.net/publication/23767050.

19. Sophian A., Tian G. and Fan M. Pulsed Eddy Current Non-destructive Testing and Evaluation: A Review. Chinese Journal of Mechanical Engineerin. 2017, 30, pp. 500–514. https://doi.org/10.1007/s10033-017-0122-4.

20. Xie Y., Rodriguez S., Zhang W., Liu Z. and Yin W. Simulation of an Electromagnetic Acoustic Transducer Array by Using Analytical Method and FDTD. Journal of Sensors. 2016, vol. 5451821, 10. http://dx.doi.org/10.1155/2016/5451821.

21. Dodd C. V. and Deeds W. E. Analytical solutions to eddy-current probe-coil problems, Journal of Applied Physics. 1968, vol. 39(6), pp. 2829–2838. https://doi.org/10.1063/1.1656680,2-s2.0-36849096720.

22. Петрищев О.М., Сучков Г.М., Плеснецов С.Ю. Теорія і практика електромагнітно-акустичного контролю. Частина 1. Теоретичні основи розрахунку і проектування електроакустичних перетворювачів електромагнітного типу: монографія: Вид. центр НТУ «ХПІ». 2019, 555 c.

23. Романюк М.І. Теоретичні основи розрахунку ультразвукових трактів пристроїв контролю поверхні металопрокату. Київ. 2015.

24. Сучков Г. М., Хащина С. В., Петрищев О. Н. Розвиток концепцій створення ультразвукових перетворювачів електромагнітного типу. Режим збудження. Частина 1. Технічна діагностика та неруйнівний контроль., №1. № 1. (c. 23-28). Інститут електрозварювання ім. Є.О. Патона НАН України. 2012.

25. Eddy Current. URL: https://se.mathworks.com/help/sps/ref/eddycurrent.html.

26. Simscape Block Libraries. URL: https://se.mathworks.com/help/simscape/ug/introducing-the-simscape-block-libraries.html.