A QUALITY CONTROL SYSTEM PROTOTYPE FOR DETECTING KNOT DEFECTS IN THE WOODEN PANEL MANUFACTURING

Abstract

Product quality has become a necessary goal for all manufacturers in today’s competitive market.  Product defects, not detected, cause financial damages and reputation loss for the manufacturer. These defects can be due to quality of the inputs or misuse of the good quality inputs during the manufacturing process. This is also the case for wooden panel manufacturing where elements are the basic input. It is possible to reduce the loss of the manufacturer by using a method that minimizes the human error in the inspection of the elements.  In this study, we, first, identified the quality control problems of the wooden panel manufacturers and basic steps in the automated element quality control. We then developed a prototype for the detection of knots, the most common defects in wooden panels.  This prototype, with 80.0% true positive (with knot defect) and 82.0% true negative (without knot defect) rates, performs close to accuracy rates of a quality control inspector. The element image library created during the development of the system made publicly available for use in similar studies. This prototype is expected to be developed to detect other wood defects and to be applied in the wooden panel manufacturing.

Keywords

• Image processing,Defect detection,Wooden panel quality control

References

1. Masif Panel Nedir. Available: http://www.bilenor.com.tr/default.aspx?pid=62953&nid=60387
2. Dilek, T., Erdinler, E.S. and Öztürk, E., “Masif panel ve sektörel gelişiminin incelenmesi”. İkinci Ulusal Mobilya Kongresi, Denizli, Türkiye, 11-13 Nisan 2013
3. Ade, F., Lins, N. and Unser, M., “Comparison of various filter sets for defect detection in textiles”. Seventh International Conference on Pattern Recognition, Montreal, Canada, July 30 - August 2 1984.
4. Baykut, A., Atalay, A., Ercil, A. and Guler, M., “Real-time defect inspection of textured surfaces”. Real-Time Imaging, 6(1), 17–27, 2000.
5. Conci, A. and Proenca, C.B., “A system for real-time fabric inspection and industrial decision”. Fourteenth International Conference on Software Engineering and Knowledge Engineering, Ischia, Italy, 15-19 July 2002.
6. Kumar, A., “Neural network based detection of local textile defects”. Pattern Recognition, 36(7), 1645-1659, 2003.
7. Costa, C.E. and Petrou, M., “Automatic registration of ceramic tiles for the purpose of fault detection”. Machine Vision and Applications, 11(5), 225–230, 2000.
8. Shire, A.N., Khanapurkar, M.M. and Mundewadikar, R.S., “Plain ceramic tiles surface defect detection using image processing”. Fourth International Conference on Emerging Trends in Engineering and Technology, Port Louis, Mauritius, 18-20 November 2011.

9. Boukouvalas, C. and Petrou, M., “Perceptual correction for colour grading of random textures”. Machine Vision and Applications, 12(3), 129–136, 2000.
10. Pernkopf, F., “Detection of surface defects on raw steel blocks using Bayesian network classifiers”. Pattern Analysis and Applications, 7(3), 333–342, 2004.
11. Wiltschi, K., Pinz, A. and Lindeberg, T., “An automatic assessment scheme for steel quality inspection”. Machine Vision and Applications, 12(3), 113–128, 2000.
12. Peng, X., Chen, Y., Yu, W., Zhou, Z. and Sun G., “An online defects inspection method for float glass fabrication based on machine vision”. International Journal of Advanced Manufacturing Technology, 39(11-12), 1180-1189, 2008.
13. Mar, N.S.S., Fookes, C. and Yarlagadda, P.K.D.V., “Design of automatic vision-based inspection system for solder joint segmentation”. Journal of Achievements in Materials and Manufacturing Engineering, 34(2), 145-151, 2009.
14. Li, Y., Li, Y.F., Wang, Q.L., Xu, D. and Tan, M., “Measurement and defect detection of the weld bead based on online vision inspection”. IEEE Transactions on Instrumentation and Measurement, 59(7), 1841–1849, 2010.
15. Kamal, I.A. and Al-Alaoui, M.A. “Online machine vision inspection system for detecting coating defects in metal lids”. International MultiConference of Engineers and Computer Scientists, Hong Kong, China, 19-21 March 2008.
16. Laurowski, M., Klein, P.H., Weyrich, M., Scharf, P. and Stark, S., “Use-appropriate design of automated optical inspection systems for rotationally symmetric parts”. 56th International Scientific Colloquium, Ilmenau, Germany, 12 – 16 September 2011.
17. Nacy, S.M. and Abbood, W.T., “Automated surface defect detection using area scan camera”. Innovative Systems Design and Engineering, 4(8), 1-10, 2013.
18. Xie, X., “A review of recent advances in surface defect detection using texture analysis techniques”. Electronic Letters on Computer Vision and Image Analysis, 7(3), 1-22, 2008.
19. Jabo, S., Machine Vision for Wood Defect Detection and Classification. MSc Thesis, Chalmers University of Technology, Göteborg, Sweden, 2011.
20. Kline, D.E., Hou, Y.J., Conners, R.W., Schmoldt, D.L. and Araman P.A., “Lumber scanning system for surface defect detection”. American Society of Agricultural Engineers International Winter Meeting, Nashville, Tennessee, USA, 15-18 December 1992.
21. Mohan, S. and Venkatachalapathy, K., “Wood knot classification using bagging”. International Journal of Computer Applications, 51(18), 50-53, 2012.
22. Hu, C., Min, X., Yun, H., Wang, T. and Zhang, S., “Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters”. Annals of Forest Science, 68 (6), 1077-1083, 2011.
23. WEINIG scanner systems. Available: https://www.weinig.com/en/solid-wood/scanner-systems.html
24. GreCon SUPERSCAN SPM/L. Available: https://www.fagus-grecon.com/en/solutions/measuring-technology/superscan-ml/
25. ColourBrain® Panel. Available: http://www.baumerinspection.com/en/products/surface-inspection/colourbrainr-panel.html
26. TS 11970 EN 13990 standardları. Available: https://intweb.tse.org.tr/Standard/Standard/Standard.aspx?081118051115108051104119110104055047105102120088111043113104073088108051048054053122098066110072
27. Record, S.J., The Mechanical Properties of Wood. 1st ed. New York, USA, J. Wiley & sons, 1914.
28. Visual Inspection of Lumber. Available: http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html
29. OpenCV (Open Source Computer Vision Library). Available: http://opencv.org
30. Lowe, D.G., “Distinctive image features from scale-invariant Keypoints”. International Journal of Computer Vision, 60(2), 91–110, 2004.
31. Stier, J. and Beilschmidt, M., “Generating train side views from video sequences for microphone array pass-by measurements”. Fourth Berlin Beamforming Conference, 22-23 February 2012.
32. Jianhua, Y., Var, A.A., X’ao, J., Fu, W., Chen, J., Yan, L. and Wang S., “Image processing and identification of lumber surface knots”. Acta Technica, 62(1A), 99–108, 2017.
33. Haralick, R., Shanmugan, K. and Dinstein, I., “Textural features for image classification”. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621, 1973.
34. Conners, R., McMillan, C., Lin, K. and Vasquez-Espinosa, R., “Identifying and locating surface defectsin wood: Part of an automated timber processing system”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(6), 573–583, 1983.
35. Otsu, N., “An automatic threshold selection method based on discriminate and least squares criteria (in Japanese)”. Denshi Tsushin Gakkai Ronbunshi, 63, 349–356, 1979.
36. Gabor, D., “Theory of communication”. Journal of the Institution of Electrical Engineers, 93, 429-441, 1946.
37. Daugman, J.G., “Uncertainty relation for resolution in space, spatial-frequency, and orientation optimized by two-dimensional visual cortical filters”. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 2(7), 1160-1169, 1985.
38. Chacon, M.I. and Alonso, G.R., “Wood defects classification using a SOM/FFP approach with minimum dimension feature vector”. Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1 2006.