Prospects for the development of magnetic particle inspection of aircraft parts

Lednev I.S., Khodakova E.A.
UDC
620.1
DOI
10.18577/2307-6046-2023-0-9-132-144
Article PDF (Russian) (790.27 KB)
How to cite
Lednev I.S., Khodakova E.A. Prospects for the development of magnetic particle inspection of aircraft parts // Proceedings of VIAM. 2023. No. 9. DOI: 10.18577/2307-6046-2023-0-9-132-144. URL: https://test.viam.ru/en/journal/2023/9/12
Keywords
magnetic non-destructive testing, magnetic particle inspection, discontinuity, aviation production, monitoring equipment, magnetic indicator
Abstract

The article discusses the main directions of development of methods of non-destructive testing, and in particular magnetic powder non-destructive testing of aircraft parts. The possibilities of magnetic powder control and the reasons for its development are given. with their help, discontinuities, depending on the purpose of control. The development of control automation and the use of neural networks, as well as mathematical modeling of processes in IPC, are identified as the main promising areas for the development of non-destructive magnetic particle control. The experience of the work carried out in these areas is given.

Reference list
  1. Onishchenko G.G., Kablov E.N., Ivanov V.V. Scientific and technological development of Russia in the context of achieving national goals: problems and solutions. Innovatsii, 2020, no. 6 (260), pp. 3–16.
  2. Kablov E.N. Materials of the new generation – the basis of innovation, technological leadership and national security of Russia. Intellekt i tekhnologii, 2016, no. 2 (14), pp. 16–21.
  3. Kablov E.N., Shevchenko Yu.N., Grinevich A.V., Kochanov D.I. Problems of certification of aviation materials at the present stage. 75 years. Aviation materials. Moscow: VIAM, 2007, pp. 388–396.
  4. Chertishchev V.Yu., Ospennikova O.G., Boichuk A.S., Dikov I.A., Generalov A.S. Determination of the size and depth of defects in multilayer PCM honeycomb structures based on the mechanical impedance value. Aviaсionnye materialy i tehnologii, 2020, no. 3 (60), pp. 72–94. DOI: 10.18577/2071-9140-2020-0-3-72-94.
  5. Kosarina E.I., Krupnina O.A., Demidov A.A., Mikhaylova N.A. Digital optical pattern and its dependence on the radiation image at non-destructive testing by method of digital radiography. Aviacionnye materialy i tehnologii, 2019, no. 1 (54), pp. 37–42. DOI: 10.18577/2071-9140-2019-0-1-37-42.
  6. Skorobogatko D.S., Golovkov A.N., Kudinov I.I., Kulichkova S.I. Revisiting the ecotoxicity and efficiency of different classes of industrial nonionic surfaces used for cleaning metal surfaces in the process of capillary control of details of the aviation technology (review). Aviation materials and technologies, 2021, no. 4 (65), paper no. 11. Available at: http://www.journal.viam.ru (accessed: June 07, 2023). DOI: 10.18577/2713-0193-2021-0-4-98-106.
  7. Krasnov I.S., Lozhkova D.S., Dalin M.A. Evaluation of deficiency of titanium alloy forgings for probabilistic calculation of gas turbine engine disks fracture risk. Aviation materials and technologies, 2021, no. 2 (63), paper no. 12. Available at: https: //journal.viam.ru (accessed: June 07, 2023). DOI: 10.18577/2713-0193-2021-0-2-115-122.
  8. State Standard R 56512–2015. The control is non-destructive. Magnetic particle method. Typical technological processes. Moscow: Standartinform, 2016, 56 p.
  9. State Standard R ISO 9934-1–2011. The control is non-destructive. Magnetic particle method. Part 1. Basic requirements. Moscow: Standartinform, 2019, 16 p.
  10. Lozhkova D.S., Krasnov I.S., Dalin M.A. Evaluation of defectiveness of blanks of GTE disks from titanium alloys. Kontrol. Diagnostika, 2016, no. 7, pp. 61–67. DOI: 10.14489/td.2016.07.pp.061-067.
  11. Ekobori T. Physics and mechanics of destruction and strength of solid bodies. Moscow: Metallurgiya, 1971, 264 p.
  12. Methodological materials for the implementation of the requirements for the main parts of the engine, the resource of which is set in cycles: Aviation Regulations-33.70-1. Moscow: Aviaizdat, 2012, 31 p.
  13. Kablov E.N., Ospennikova O.G., Kudinov I.I., Golovkov A.N., Generalov A.S., Knyazev A.V. Estimation of the probability of detecting operational defects in aircraft parts made of heat-resistant alloys using domestic and foreign flaw detection liquids. Defektoskopiya, 2021, no. 1, pp. 64–71.
  14. SDANK-01-2020. Rules for attestation and basic requirements for non-destructive testing laboratories. Available at: https://ntcexpert.ru/documents/sdank-01-2020.pdf (accessed: June 13, 2023).
  15. ISO 9934-1. Non-destructive testing. Magnetic particle testing. Part 1: General principles. Geneva: ISO, 2001, 14 p.
  16. DIN EN 1369-2013. Founding – Magnetic particle testing. Deutsches Institut für Normung e.V., 2013, 26 р.
  17. ASTM E1444/E1444M. Standard Practice for Magnetic Particle Testing for Aerospace. ASTM International (ASTM), 2022, 16 р.
  18. ASTM E709-21. Standard Guide for Magnetic Particle Testing. ASTM International (ASTM), 2021, 48 р.
  19. Mariani S., Rendu Q., Urbani M., Sbarufatti C. Causal dilated convolutional neural networks for automatic inspection of ultrasonic signals in non-destructive evaluation and structural health monitoring. Mechanical Systems and Signal Processing. Elsevier, 2021, 21 р. DOI: 10.1016/j.ymssp.2021.107748.
  20. Rawat W., Wang Z. Deep Convolutional Neural Networks for Image Classification: a Comprehensive Review. Neural Computation, 2017, vol. 29, no. 9, pp. 2352–2449. DOI: 10.1162/neco_a_00990.
  21. Tout K., Meguenani A., Urban J.P. et al. Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks. The International Journal of Advanced Manufacturing Technology, 2021, vol. 112, рр. 3307–3326. DOI: 10.1007/s00170-020-06467-4.
  22. Link R., Riess N. NDT 4.0-significance and implications to NDT–automated magnetic particle testing as an example. 12th European Conference on Non-Destructive Testing (ECNDT 2018), 2018, vol. 15, р. 6.
  23. Antonio S., Fulginei F., Faba A. et al. Vector Hysteresis Processes for Innovative Fe-Si Magnetic Powder Cores: Experiments and Neural Network Modeling. Magnetochemistry, 2021, vol. 7 no. 2, p. 18. DOI: 10.3390/magnetochemistry7020018.
  24. Ferguson M., Ak R., Lee Y.-T.T., Law K. H. Automatic localization of casting defects with convolutional neural networks. 2017 International conference on big data, 2017, pp. 1726–1735.
  25. Chen Y., Feng B., Kang Y. et al. A novel thermography-based dry magnetic particle testing method. IEEE Transactions on Instrumentation and Measurement, 2022, vol. 71, pp. 1–9. DOI: 10.1109/TIM.2022.3165742.
  26. Wong B.S., Low Y.G., Wang X. et al. 3D finite element simulation of magnetic particle inspection. 2010 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology, 2010, pp. 50–55. DOI: 10.1109/STUDENT.2010.5687008.
  27. Zolfaghari A., Zolfaghari A., Kolahan F. Reliability and sensitivity of magnetic particle nondestructive testing in detecting the surface cracks of welded components. Nondestructive Testing and Evaluation, 2018, vol. 33, no. 3, pp. 290–300. DOI: 10.1080/10589759.2018.1428322.
  28. Apostol E.S., Nedelcu A., Daniel D.V. et al. Mathematical modeling of eddy current non-destructive testing. 10th International Symposium on Advanced Topics in Electrical Engineering, 2017, pp. 469–474. DOI: 10.1109/ATEE.2017.7905088.
  29. Tout K., Meguenani A., Urban J.-P., Cudel C. Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks. The International Journal of Advanced Manufacturing Technology, 2021, vol. 112, pp. 3307–3326. DOI: 10.1007/s00170-020-06467-4.
  30. Ueda A., Lu H., Kamiya T. Deep-Learning Based Segmentation Algorithm for Defect Detection in Magnetic Particle Testing Images. Proceedings of International Conference on Artificial Life and Robotics, 2021, vol. 26, pp. 235–238. DOI: 10.5954/ICAROB.2021.GS3-1.
  31. Yang Y., Yang Y., Li L. et al. Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features. Applied Sciences, 2022, vol. 12 (3), p. 1293. DOI: 10.3390/app12031293.
  32. Staněk P., Škvor Z. Automated Magnetic Field Evaluation for Magnetic Particle Inspection by Impulse. Journal of Nondestructive Evaluation, 2019, vol. 38, art. 75. DOI: 10.1007/s10921-019-0615-4.
  33. Bermúdez A., Gómez D., Piñeiro M. et al. Numerical Simulation of Magnetization and Demagnetization Processes. IEEE Transactions on Magnetics, 2017, vol. 53, no. 12, pp. 1‒6. DOI: 10.1109/TMAG.2017.2743069.
  34. Mao B., Lan T., Deng W. Simulation of Magnetic Field Penetration of Cylindrical Cavity with Wound Solenoid. Advances in Computer Science Research, 2016, vol. 58. Available at: https://www.atlantis-press.com/article/25868834.pdf (accessed: June 07, 2023).