Abstract
This study investigates image processing techniques for detecting surface cracks in spring steel components, with a focus on applications like Magnetic Particle Inspection (MPI) in industries such as railways and automotive. The research details a comprehensive methodology that covers data collection, software tools, and image processing methods. Various techniques, including Canny edge detection, Hough Transform, Gabor Filters, and Convolutional Neural Networks (CNNs), are evaluated for their effectiveness in crack detection. The study identifies the most successful methods, providing valuable insights into their performance. The paper also introduces a novel batch processing approach for efficient and automated crack detection across multiple images. The trade-offs between detection accuracy and processing speed are analyzed for the Morphological Top-hat filter and Canny edge filter methods. The Top-hat method, with thresholding after filtering, excelled in crack detection, with no false positives in tested images. The Canny edge filter, while efficient with adjusted parameters, needs further optimization for reducing false positives. In conclusion, the Top-hat method offers an efficient approach for crack detection during MPI. This research offers a foundation for developing advanced automated crack detection system, not only to spring sector but also extends to various industrial processes such as casting and forging tools and products, thereby widening the scope of applicability.
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Bibliography
[1] Gubeljak, N., Predan, J., Senčič, B. & Chapetti, M. (2014). Effect of residual stresses and inclusion size on fatigue resistance of parabolic steel springs. Materials Testing. 56(4), 312-317. DOI:10.3139/120.110567.
[2] Xu, C., Yilong L., Ming Y., Jiabang Y. & Xiang P. (2021). Effects of the ultra-sonic assisted surface rolling process on the fatigue crack initiation position distribution and fatigue life of 51CrV4 spring steel. Materials. 14(10), 2565, 1-19. DOI:10.3390/ma14102565.
[3] Yun, J.P., Choi, Dc., Jeon, Yj. et al., (2014). Defect inspection system for steel wire rods produced by hot rolling process. The International Journal of Advanced Manufacturing Technology. 70, 1625-1634. DOI:10.1007/s00170-013-5397-8.
[4] Perichiyappan, S. & Jagadeesha, T. (2021). Modelling and simulation of primary suspension springs used in Indian railways. Materials Today: Proceedings. 46(17), 8450-8454. DOI: 10.1016/j.matpr.2021.03.478.
[5] Kumar, S., Kumar, V., Nandi, R.K. et al. (2008). Investigation into surface defects arising in hot-rolled SUP 11A grade spring billets. Journal of Failure Analysis and Prevention. 8(6), 492-497. DOI:10.1007/s11668-008-9169-y.
[6] Filipović, M., Eriksson, C. & Överstam, H. (2006). Behaviour of surface defects in wire rod rolling. Steel research international. 77(6), 439-444, DOI:10.1002/srin.200606411.
[7] Matjeke, V.J., Van Der Merwe, J.W., Mukwevho, G. & Phasha, M.J. (2019). Thermal characteristics of spring steels used in railway bogies. SN Applied Sciences. 1, 1548, 1-8. DOI:10.1007/s42452-019-1546-5.
[8] Nagumo, Y., Tanifuji, K. & Imai, J. (2010). A basic study on wheel flange climbing using model wheelset. International Journal of Railway. 3(2), 60-67. DOI:10.1299/kikaic.74.242.
[9] The Rail Safety Inspection Office. (2021). Accident and incident investigation report: Derailment of the regional passenger train No. 21209 between Chvalkov and Vcelnicka operating control points. Retrieved November 7, 2023, from https://www.dicr.cz/files/uploads/Zpravy/MU/DI_Chvalkov_Vcelnicka_210715.pdf.
[10] Maass, M., Deutsch, W.A., Bartholomai, F. (2014). Magnetic Particle Inspection on train components. In 11th European Conference on Non-Destructive Testing, 6-11 October 2014 (pp. 1-9). Prague, Czech Republic.
[11] Deng, J., Singh, A., Zhou, Y., Lu, Y. & Lee, V.C.S. (2022). Review on computer vision-based crack detection and quantification methodologies for civil structures. Construction and Building Materials. 356, 129238. DOI:10.1016/j.conbuildmat.2022.129238.
[12] Mohan, A. & Poobal, S. (2018). Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal. 57(2), 787-798. DOI:10.1016/j.aej.2017.01.020.
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