Журнал Российского общества по неразрушающему контролю и технической диагностике
The journal of the Russian society for non-destructive testing and technical diagnostic
 
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22 | 11 | 2024
2022, 07 July

DOI: 10.14489/td.2022.07.pp.050-055

Kadhim M. Kh., Rusinov L. A.
AUTOMATIC DETECTION OF SURFACE DEFECTS OF CERAMIC TILES
(pp. 50-55)

Abstract. Ceramic tiles are one of the most demanded finishing materials, and the areas of their use are constantly expanding. The production of ceramic tiles is quite well automated, but the control of manufactured tiles for defects is usually not automated, that limits its speed and does not guarantee the necessary quality. The algorithm for detecting the main surface defects of ceramic tiles, namely, mechanical (scratches, cracks, etc.), geometric (chips on the corners, gouges or protrusions on the edges), color defects (blobs, spots, etc.) is proposed in the paper. It based on digital image processing of tiles and was implemented using the free library of algorithmic primitives OpenCV. The algorithm does not require the presentation of reference tiles. At the same time, a high speed of classifying monochrome tiles into fresh and defective ones is ensured and allows to organize the automatic control of plain tiles on the conveyor in real time with the probability of correct detections 97 %.

Keywords: сeramic tile, image processing, defect detection, quality control.

M. Kh. Kadhim, L. A. Rusinov (Saint-Petersburg State Institute of Technology (Technical University), Saint Petersburg, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.  

1. Information and technical guide on the best available technologies "Production of ceramic products" IPS 1-2015. (2015). Moscow: Byuro NDT. Available at: https://files.stroyinf.ru/Data2/1/4293757/4293757770.pdf (Accessed: 05.03.2022.) [in Russian language]
2. Ozkan F., Ulutas B. (2016). Use of an Eye-Tracker to Assess Workers in Ceramic Tile Surface Defect Detection. International Conference on Control, Decision and Information Technologies (CoDIT), pp. 088 – 091. DOI 10.1109/CoDIT.2016.7593540.
3. Tiles are ceramic. General specifications. (2019). Ru Standard No. GOST 13996–2019. Moscow: Standartinform. [in Russian language]
4. Czimmermann T., Ciuti G., Milazzo M. et al. (2020). Visual-Based Defect Detection and Classification Approaches for Industrial Applications – A SURVEY. Sensors, Vol. 20, (5). DOI 10.3390/s200514592.
5. Karhe R. R., Nagare N. N. (2017). A Survey on Automatic Defect Detection & Classification Technique from Image: A Special Case Using Ceramic Tiles. International Journal of Advance Engineering and Research Development, Vol. 4, (12), pp. 1027 – 1034.
6. Tsarouhas P. H., Arampatzaki D. (2010). Application of Failure Modes and Effects Analysis (FMEA) of a Ceramic Tiles Manufacturing Plant. St Olympus International Conference On Supply Chains. Katerini. Available at: https://www.researchgate.net/publication/267405062_Application_of_Failure_Modes_and_Effects_Analysis_FMEA_of_a_Ceramic_Tiles_Manufacturing_Plant (Accessed: 01.03.2022.)
7. Hussain Z. (2019). Optimizing Productivity by Eliminating and Managing Rejection Frequency Using 5s and Kaizens Practices: Case Study. Independent Journal of Management & Production (IJM&P), Vol. 10, (6), pp. 1953 – 1970. Available at: http://creativecommons.org/licenses/by/3.0/us/ (Accessed: 01.03.2022.) DOI 10.14807/ijmp.v10i6.943.
8. Shah H. N. M., Kee Y. Sh., Kamis Z. et al. (2019). Automated Quality Inspection on Tile Border Detection Using Vision System. International Journal of Recent Technology and Engineering (IJRTE), Vol. 8, (3), pp. 3737 – 3745. DOI 10.35940/ijrte.C3983.098319.
9. Gruzman I. S., Kirichuk V. S., Kosyh V. P. et al. (2000). Digital image processing in information systems. Novosibirck: Izdatel'stvo NGTU. [in Russian language]
10. Mishra R., Shukla D. (2014). A Survey on Various Defect Detection. International Journal of Engineering Trends and Technology (IJETT), Vol. 10, 13, pp. 643 – 648.
11. Surface inspection. Available at: https://sacmi.com/SacmiCorporate/media/ceramics/Catalogues/CTQF_Processmaster_-Flawmaster_Advancheck-Newcheck_NUOVASIMA_EN-IT-ES.pdf (Accessed: 20.02.2022.)
12. Gonsales R. (2012). Digital image processing. Moscow: Tekhnosfera. [in Russian language]
13. Contour Features. Available at: https://docs.opencv.org/3.4/dd/d49/tutorial_py_contour_features.html (Accessed: 15.03.2022.)
14. CV2 Boundingrect Explained with Examples. Available at: https://www.pythonpool.com/cv2-boundin-grect/ (Accessed: 15.03.2022.)
15. Image Rotation and Translation Using OpenCV. Available at: Image Rotation and Translation Using OpenCV | LearnOpenCV # (Accessed: 15.03.2022.)
16. Adaptive Brightness Contrast Adjustment. Available at: https://www.programmersought.com/article/9935110603/ (Accessed: 01.07.2021.)
17. Dnyandeo S. V., Nipanikar R. S. (2016). A Review of Adaptive Thresholding Techniques for Vehicle Number Plate Recognition. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, (4), pp. 944 – 946.

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