Technical Diagnostics and Non-Destructive Testing, 2024, №01



2024 №01 (04)

DOI of Article 10.37434/tdnk2024.01.05

2024 №01 (06)

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Technical Diagnostics and Non-Destructive Testing #1, 2024, pp. 32-40

Automated visual control systems for surface defects in thin-sheet materials (Review)

A.S. Novodranov, E.V. Shapovalov

E.O. Paton Electric Welding Institute of the NAS of Ukraine 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: office@paton.kiev.ua

Taking into account the speed of development in the production of thin-sheet materials, the use of automated systems for detecting surface defects in such products is becoming more and more relevant. This is due to the fact that an early-detected defect reduces the amount of waste and increases production efficiency by reducing equipment downtime. The purpose of the paper is to review modern automated systems for visual inspection of surface defects on thin-sheet materials, in order to evaluate their effectiveness, advantages, and limitations. The paper examines and analyzes the automated systems for searching for surface defects in various industries, including metal, paper and weaving rolled stock. It is shown that systems of this class are most often performed in a stationary configuration directly on machine tools or rolling machines, but there are systems that, in addition to their stationary implementation, have a portable version that is mounted on a diagnostic cart. The components of the hardware subsystem are considered, namely, the advantages of using intelligent cameras in comparison with linear scanning cameras are outlined. The advantages of using stroboscopic lighting in comparison with conventional LED floodlights have been determined. The software subsystem was considered, and it was also determined that the use of artificial intelligence methods, namely neural networks with machine learning, is a promising vector for development of such systems. 36 Ref., 1 Tabl., 10 Fig.
Keywords: automated visual control, defects of thin-sheet materials, metal thin-sheet materials, automated control systems

Received: 05.02.2024,
Received in revised form: 13.02.2024
Accepted: 21.03.2024

References

1. Goebbels, K., Ferrano, G. (1988) Automation of surface defect detection and evaluation. Proc. SPIE 0849, Automated Inspection and High-Speed Vision Architectures, 22 March 1988. https://doi.org/10.1117/12.942832
2. Malamas, E.N., Petrakis, E.G.M., Zervakis, M. et al. (2003) A survey on industrial vision systems, applications and tools. Image Vis. Comput., 21(2), 171-188. https://doi.org/10.1016/S0262-8856(02)00152-X
3. Norton-Wayne, L., Bradshaw, M., Jewell, A.J. (1992) Machine vision inspection of web textile fabric. BMVC, 1, 217-226. https://doi.org/10.5244/C.6.23
4. Kumar, A. (2008) Computer-vision-based fabric defect detection: A survey. IEEE Trans. Ind. Electron., 55(1), 348-363. https://doi.org/10.1109/TIE.1930.896476
5. Shao, X., Xia, X., Song, J. (2022) Design and research of metal surface defect detection based on machine vision. 11th International Conference of Information and Communication Technology, 406-410. https://doi.org/10.1109/ICTech55460.2022.00087
6. Huotilainen, T., Laster, M., Riikonen, S. (2019) Real-time paper and paperboard quality analysis and classification based on paper formation or surface appearance in web inspection system. Conference PaperCon, May2019, Indianapolis, USA.
7. Bissessur, Y., Martin, E., Morris, A. (1999) Monitoring the performance of the paper making process. Control Eng. Pr., 7(11), 1357-1368. https://doi.org/10.1016/S0967-0661(99)00105-7
8. Xiaoxin Fang, Qiwu Luo, Bingxing Zhou, Congcong Liand, Lu Tian (2020) Research progress of automated visual surface defect detection for industrial metal planar materials. Sensors, 20(8), 5136. https://doi.org/10.3390/s20185136
9. Luo, Q., He, Y. (2016) A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. Robot Comput. Integr. Manuf., 38(C), 16-30. https://doi.org/10.1016/j.rcim.2015.09.008
10. Singh, Swarit & Desai, Kaushal (2022) Automated surface defect detection framework using machine vision and convolutional neural networks. J. of Intelligent Manufacturing, 34(4), 1995-2011. https://doi.org/10.1007/s10845-021-01878-w
11. Francisco G. Bulnes, Ruben Usamentiaga, Daniel F. Garcia, Julio Molleda (2012) Vision-based sensor for early detection of periodical defects in web materials. Sensors, 12(8), 10788-10809. https://doi.org/10.3390/s120810788
12. Telljohann, A. (2007) Chapter 2. Introduction to building a machine vision inspection. In: Handbook of Machine Vision; Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 35-71. https://doi.org/10.1002/9783527610136.ch2
13. Basler, AG [Електронний ресурс]. Basler ace acA1920-48gm. https://www.baslerweb.com/en/shop/aca1920-48gm/
14. Chang, C.A., Su, C.T. (1995) A comparison of statistical regression and neural network methods in modeling measurement errors for computer vision inspection systems. Comput. Ind. Eng., 28(3), 593-603. https://doi.org/10.1016/0360-8352(94)00211-5
15. Brzakovic, D., Vujovic, N. (1996) Designing a defect classification system: A case study. Pattern Recog., 29(8), 1401-1419. https://doi.org/10.1016/0031-3203(95)00166-2
16. Duan, X., Duan, F., Han, F. (2011) Study on surface defect vision detection system for steel plate based on virtual instrument technology. In Proceedings of the 2011 International Conference on Control, Automation and Systems Engineering, Singapore. https://doi.org/10.1109/ICCASE.2011.5997625
17. (2014) Wintriss Engineering [Електронний ресурс]. OPSIS Smart Vision. https://datasheet.datasheetarchive.com/originals/crawler/weco.com/682af94e91931264471d0a90eb777fbd.pdf
18. Wintriss Engineering [Електронний ресурс]. Metals Solution. https://www.weco.com/metals
19. (2020) IMS Messsysteme GmbH [Електронний ресурс]. Optical measuring systems surcon 2D surface inspection for Cold Rolling Mills & Processing Lines. https://www.ims-experts.com/wp-content/uploads/2020/12/TIN-2D-Surface-Inspection-Systems-for-Cold-Rolling-Mills-and-Processing-Lines.pdf
20. IMS Messsysteme GmbH [Електронний ресурс]. Surcon 2D surface inspection system. https://www.ims-experts.com/en/measuring-system/surface-inspection-system-surcon-2d-metal-service-centres/
21. (2019) AMETEK Surface Vision [Електронний ресурс]. Metals surface inspection. https://www.ameteksurfacevision.com/-/media/ameteksurfacevisionv2/documentation/industry-brochures/metals/ametek_surface_vision_metals_ brochure_rev3_en.pdf 22. AMETEK Surface Vision [Електронний ресурс]. Surface Vision. https://www.ameteksurfacevision.com/-/media/AmetekSurfaceVisionV2/Documentation/CaseStudies/AMETEK_Surface_Vision_Arania_Case_Study_Rev2_KO.pdf
23. Strokina, N., Eerola, T., Lensu, L., Kälviäinen, H. (2011) Adaptive classification of dirt particles in papermaking process. Image Analysis - 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden. https://doi.org/10.1007/978-3-642-21227-7_68
24. IBS Austria GmbH [Електронний ресурс]. Camera based trouble shooting. https://www.ibs-ppg.com/en/products/detail/camera-based-trouble-shooting.
25. Papertech [Електронний ресурс]. Web inspection. https://www.papertech.ca/web-inspection/
26. Valmet Corporation [Електронний ресурс]. Valmet IQ Web inspection system. https://valmet.my.salesforce-sites.com/solutionfinderweb/sfc/servlet.shepherd/version/download/068580000026M9GAAU
27. Dr. Schenk GmbH [Електронний ресурс]. MIDA - Superior defect detection and classification. https://www. drschenk.com/news/news-article/mida-superior-defect-detection-and-classification-15/
28. Dr. Schenk GmbH [Електронний ресурс]. Paper inspection. https://www.drschenk.com/products/paper-inspection.html
29. Procemex [Електронний ресурс]. Measurement geometries. https://www.procemex.com/web-inspection/measurement-geometries/#low-angle-web-inspection
30. Procemex [Електронний ресурс]. Web inspection system. https://www.procemex.com/web-inspection/
31. Dr. Schenk GmbH [Електронний ресурс]. Textile inspection. https://www.drschenk.com/products/structured-surfaces/textile-inspection.html
32. COMVIS - Company in vision [Електронний ресурс]. Profiler ™ for Looms. https://comvis.com/inspection-systems/profiler-for-looms/
33. COMVIS - Company in vision [Електронний ресурс]. Profiler™ Fabric Inspection. https://comvis.com/inspection-systems/profiler-automatic-fabric-inspection/
34. Shelton Machines Ltd [Електронний ресурс]. Machine vision solutions. https://www.sheltonvision.co.uk/visual-inspection-systems/machine-vision-solutions/
35. Indian Textile Journal [Електронний ресурс]. Shelton vision: Beyond big data and tailored for textiles. https://indiantextilejournal. com/shelton-vision-beyond-big-data-and-tailored-for-textiles/
36. IndiaMART [Електронний ресурс]. Fully automatic camera based inspection machine, for fabric checking. https://www.indiamart.com/proddetail/fully-automatic-camera-based-inspection-machine-23747914755.html

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