Araştırma Makalesi
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COMPUTER AIDED CONTROL OF CUTTING ERROR IN TEXTILE PRODUCTS

Yıl 2017, Cilt: 27 Sayı: 3, 300 - 308, 30.09.2017

Öz




At present, the audits about the cutting error of textile products (leather, fabric, etc.) are made by the human by the eye via the
template. Making these audits that necessitate accurate measurement by eye both takes so much time and enhance the risk occurrence
risk. In this article, the image processing based industrial quality control system that determines the cutting errors of textile products
automatically and discriminates between faulty and faultless products is explained. The system minimizes the faults based upon the
human auditing and increases the number of pieces that are controlled by the unit of time. The performed system is composed of Panel
PC, line scan camera, system of conveyor, basket control unit, image processing software and control user interface. The textile pieces
(cuts) to be inspected come into the part by the conveyor where the camera and illumination unit are available, and the image is
captured. This captured image is sent to the Panel PC and controlled whether there is a cutting error via image processing software.
According to the result of the audit, the basket system at the end of the conveyor (conveyor belt) moves back and forth on wheel rail,
and the textile pieces are provided to fall into the required basket. The performed system was tested on the leather pieces that were taken
from a company in the leather sector. Totally it was tried by 150 times for 50 pieces of leather in 5 different templates and these pieces
felt into the required basket correctly by discriminating for faulty/faultiness ones by 149 times (99,33% success ratio). 




Kaynakça

  • 1. Sari-Sarraf, H. and J.S. Goddard Jr, 1999, Vision system for on-loom fabric inspection. Industry Applications, IEEE Transactions on, Vol: 35(6), pp: 1252- 1259.
  • 2. Baykut, A., et al., 2000, Real-time defect inspection of textured surfaces, Real-Time Imaging, Vol: 6(1), pp: 17-27.
  • 3. Kumar, A. and G.K. Pang, 2002, Defect detection in textured materials using Gabor filters, Industry Applications, IEEE Transactions on, Vol: 38(2), pp: 425- 440.
  • 4. Chan, C.-h. and G.K. Pang, 2000, Fabric defect detection by Fourier analysis, Industry Applications, IEEE Transactions on, Vol: 36(5), pp: 1267-1276.
  • 5. Huang, C.-C., S.-C. Liu, and W.-H. Yu, 2000, Woven fabric analysis by image processing Part I: identification of weave patterns, Textile Research Journal, Vol: 70(6), pp: 481-485.
  • 6. Huang, W. and T.K. Ghosh, 2002, Online characterization of fabric compressional behavior, Textile research journal, Vol: 72(2), pp: 103-112.
  • 7. Tsai, D.-M. and T.-Y. Huang, 2003, Automated surface inspection for statistical textures, Image and Vision computing, Vol: 21(4), pp: 307-323.
  • 8. Baştürk, A., Yuğnak, Z., Ketencioğlu, H., Yüksel, M.E., 2006, Fault inspection of textile fabrics using Gabor wavelets and basic component analysis, in Eleco'2006 Electrical - Electronics - Computer Engineering Symposium, ELECO: Bursa.
  • 9. Ala, D.M., 2008, Numerating Woven Fabric Defects With Image Analysis, M.Sc. Thesis, Pamukkale University, Denizli.
  • 10. Arıkan, C.O., 2009, Used computer aided image processing applications in textile technology, Ph.D. Thesis, Ege University, İzmir.
  • 11. Jyothi, G., Sushma, C.H. and Veeresh, D.S.S., 2015, Luminance Based Conversion of Gray Scale Image to RGB Image, International Journal of Computer Science and Information Technology Research, Vol: 3(3), pp: 279-283.
  • 12. Young IT and Van Vliet LJ, 1995, Recursive implementation of the Gaussian filter, Signal processing, Vol: 44(2), pp: 139-151.
  • 13. Otsu, N., 1979, An automatic threshold selection method based on discriminate and least squares criteria, Denshi Tsushin Gakkai Ronbunshi, Vol: 63, pp: 349-356.
  • 14. Umbaugh, R.E., 1999, Handbook of IS management, Auerbach.
  • 15. Gonzalez, R.C. and R.E. Woods, 2002, Digital image processing, Prentice hall Upper Saddle River.
  • 16. Mitra, S.K. and G.L. Sicuranza, 2001, Nonlinear image processing, Academic Press.
  • 17. Hodgson, R., et al., 1985, Properties, implementations and applications of rank filters, Image and Vision Computing, Vol: 3(1), pp: 3-14.
  • 18. Chan, T.F., B.Y. Sandberg, and L.A. Vese, 2000, Active contours without edges for vector-valued images, Journal of Visual Communication and Image Representation, Vol: 11(2), pp: 130-141.
  • 19. Chan, T.F. and L.A. Vese, 2001, Active contours without edges, Image Processing, IEEE Transactions on, Vol: 10(2), pp: 266-277.
  • 20. Chenyang, X. and J.L. Prince, 1998, Snakes, shapes, and gradient vector flow, Image Processing, IEEE Transactions on, Vol: 7(3), pp: 359-369.
  • 21. Osher, S. and J.A. Sethian, 1988, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, Vol: 79(1), pp: 12-49.
  • 22. Sapiro, G., 2006, Geometric partial differential equations and image analysis, New York: Cambridge university press, 386.
  • 23. Chesnaud, C., P. Réfrégier, and W. Boulet, 1999, Statistical region snake-based segmentation adapted to different physical noise models, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol: 21(11), pp: 1145-1157.
  • 24. Li, C., Xu, C., Gui, C. and Fox, M. D., 2005, Level set evolution without re-initialization: a new variational formulation, in Computer Vision and Pattern Recognition, CVPR 2005 IEEE Computer Society Conference on, Vol: 1, pp: 430-436.
  • 25. Suri, J. S., Liu, K., Singh, S., Laxminarayan, S. N., Zeng, X. and Reden, L., 2002, Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review, Information Technology in Biomedicine, IEEE Transactions on, Vol: 6(1), pp: 8-28.
  • 26. Malladi, R., Sethian J. and Vemuri, B.C., 1995, Shape modeling with front propagation: A level set approach, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol: 17(2), pp: 158-175.
  • 27. Osher, S. and Fedkiw, R., 2006, Level set methods and dynamic implicit surfaces, New York: Springer Science & Business Media, Vol: 153, pp: 280.
  • 28. Özmen, N., 2009, Image segmentation and smoothing via partial differential equations, M.Sc. Thesis, Middle East Technical University, Ankara, pp: 102.
  • 29. Tunali, I. and Kilic, E., 2013, Mass segmantation on mammograms using active contours." 21st IEEE Signal Processing and Communications Applications Conference, pp:1-4.
  • 30. Hu, M.K., 1962, Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, Vol: 8(2), pp: 179-187.
  • 31. Conseil, S., Bourennane, S. and Martin, L., 2007, Comparison of Fourier descriptors and Hu moments for hand posture recognition, 15th European IEEE Signal Processing Conference, pp: 1960-1964.

TEKSTİL ÜRÜNLERİ KESİM HATALARININ BİLGİSAYAR DESTEKLİ KONTROLÜ

Yıl 2017, Cilt: 27 Sayı: 3, 300 - 308, 30.09.2017

Öz




Günümüzde tekstil (deri, kumaş vb.) ürünleri kesim hataları ile ilgili denetimler şablon vasıtasıyla insan tarafından gözle
yap
ılmaktadır. Hassas ölçüm gerektiren bu denetimlerin gözle yapılması, hem çok uzun zaman almakta hem de hata oluşma riskini
art
ırmaktadır. Bu makalede tekstil parçalarının kesim hatalarını otomatik olarak tespit eden ve hatalı/hatasız parça ayrımı yapabilen
görüntü i
şleme tabanlı endüstriyel kalite kontrol sistemi anlatılmıştır. Sistem insan denetiminden kaynaklanan hatayı en aza indirmekte
ve birim zamanda kontrol edilen parça say
ısını artırmaktadır. Gerçekleştirilen sistem, Panel PC, çizgi tarama kamerası, yürüyen bant
sistemi, sepet kontrol ünitesi, görüntü i
şleme yazılımı ve kullanıcı kontrol ara yüzünden oluşmaktadır. Denetimi yapılacak kesilmiş
tekstil parçaları yürüyen bant üzerinde kamera ve aydınlatma ünitesinin bulunduğu kısma gelir ve görüntü yakalanır. Yakalanan görüntü
Panel PC’ye gönderilir ve görüntü i
şleme yazılımı vasıtasıyla kesim hatası olup olmadığı denetlenir. Denetim sonucuna göre yürüyen
band
ın sonunda yer alan sepet sistemi, pnömatik olarak ileri/geri hareket ettirilerek parçanın istenen sepete düşmesi sağlanır. 5 farklı
şablona sahip 50 adet deri parçası için yapılan 150 denemeden 149 unda (%99.33 başarı oranı) doğru olarak hatalı/hatasız ayrımı
yapılarak belirlenen sepete otomatik olarak düşürüldüğü görülmüştür. 




Kaynakça

  • 1. Sari-Sarraf, H. and J.S. Goddard Jr, 1999, Vision system for on-loom fabric inspection. Industry Applications, IEEE Transactions on, Vol: 35(6), pp: 1252- 1259.
  • 2. Baykut, A., et al., 2000, Real-time defect inspection of textured surfaces, Real-Time Imaging, Vol: 6(1), pp: 17-27.
  • 3. Kumar, A. and G.K. Pang, 2002, Defect detection in textured materials using Gabor filters, Industry Applications, IEEE Transactions on, Vol: 38(2), pp: 425- 440.
  • 4. Chan, C.-h. and G.K. Pang, 2000, Fabric defect detection by Fourier analysis, Industry Applications, IEEE Transactions on, Vol: 36(5), pp: 1267-1276.
  • 5. Huang, C.-C., S.-C. Liu, and W.-H. Yu, 2000, Woven fabric analysis by image processing Part I: identification of weave patterns, Textile Research Journal, Vol: 70(6), pp: 481-485.
  • 6. Huang, W. and T.K. Ghosh, 2002, Online characterization of fabric compressional behavior, Textile research journal, Vol: 72(2), pp: 103-112.
  • 7. Tsai, D.-M. and T.-Y. Huang, 2003, Automated surface inspection for statistical textures, Image and Vision computing, Vol: 21(4), pp: 307-323.
  • 8. Baştürk, A., Yuğnak, Z., Ketencioğlu, H., Yüksel, M.E., 2006, Fault inspection of textile fabrics using Gabor wavelets and basic component analysis, in Eleco'2006 Electrical - Electronics - Computer Engineering Symposium, ELECO: Bursa.
  • 9. Ala, D.M., 2008, Numerating Woven Fabric Defects With Image Analysis, M.Sc. Thesis, Pamukkale University, Denizli.
  • 10. Arıkan, C.O., 2009, Used computer aided image processing applications in textile technology, Ph.D. Thesis, Ege University, İzmir.
  • 11. Jyothi, G., Sushma, C.H. and Veeresh, D.S.S., 2015, Luminance Based Conversion of Gray Scale Image to RGB Image, International Journal of Computer Science and Information Technology Research, Vol: 3(3), pp: 279-283.
  • 12. Young IT and Van Vliet LJ, 1995, Recursive implementation of the Gaussian filter, Signal processing, Vol: 44(2), pp: 139-151.
  • 13. Otsu, N., 1979, An automatic threshold selection method based on discriminate and least squares criteria, Denshi Tsushin Gakkai Ronbunshi, Vol: 63, pp: 349-356.
  • 14. Umbaugh, R.E., 1999, Handbook of IS management, Auerbach.
  • 15. Gonzalez, R.C. and R.E. Woods, 2002, Digital image processing, Prentice hall Upper Saddle River.
  • 16. Mitra, S.K. and G.L. Sicuranza, 2001, Nonlinear image processing, Academic Press.
  • 17. Hodgson, R., et al., 1985, Properties, implementations and applications of rank filters, Image and Vision Computing, Vol: 3(1), pp: 3-14.
  • 18. Chan, T.F., B.Y. Sandberg, and L.A. Vese, 2000, Active contours without edges for vector-valued images, Journal of Visual Communication and Image Representation, Vol: 11(2), pp: 130-141.
  • 19. Chan, T.F. and L.A. Vese, 2001, Active contours without edges, Image Processing, IEEE Transactions on, Vol: 10(2), pp: 266-277.
  • 20. Chenyang, X. and J.L. Prince, 1998, Snakes, shapes, and gradient vector flow, Image Processing, IEEE Transactions on, Vol: 7(3), pp: 359-369.
  • 21. Osher, S. and J.A. Sethian, 1988, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, Vol: 79(1), pp: 12-49.
  • 22. Sapiro, G., 2006, Geometric partial differential equations and image analysis, New York: Cambridge university press, 386.
  • 23. Chesnaud, C., P. Réfrégier, and W. Boulet, 1999, Statistical region snake-based segmentation adapted to different physical noise models, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol: 21(11), pp: 1145-1157.
  • 24. Li, C., Xu, C., Gui, C. and Fox, M. D., 2005, Level set evolution without re-initialization: a new variational formulation, in Computer Vision and Pattern Recognition, CVPR 2005 IEEE Computer Society Conference on, Vol: 1, pp: 430-436.
  • 25. Suri, J. S., Liu, K., Singh, S., Laxminarayan, S. N., Zeng, X. and Reden, L., 2002, Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review, Information Technology in Biomedicine, IEEE Transactions on, Vol: 6(1), pp: 8-28.
  • 26. Malladi, R., Sethian J. and Vemuri, B.C., 1995, Shape modeling with front propagation: A level set approach, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol: 17(2), pp: 158-175.
  • 27. Osher, S. and Fedkiw, R., 2006, Level set methods and dynamic implicit surfaces, New York: Springer Science & Business Media, Vol: 153, pp: 280.
  • 28. Özmen, N., 2009, Image segmentation and smoothing via partial differential equations, M.Sc. Thesis, Middle East Technical University, Ankara, pp: 102.
  • 29. Tunali, I. and Kilic, E., 2013, Mass segmantation on mammograms using active contours." 21st IEEE Signal Processing and Communications Applications Conference, pp:1-4.
  • 30. Hu, M.K., 1962, Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, Vol: 8(2), pp: 179-187.
  • 31. Conseil, S., Bourennane, S. and Martin, L., 2007, Comparison of Fourier descriptors and Hu moments for hand posture recognition, 15th European IEEE Signal Processing Conference, pp: 1960-1964.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Kerim Kürşat ÇEVİK

Hasan Erdinç KOÇER Bu kişi benim

Yayımlanma Tarihi 30 Eylül 2017
Gönderilme Tarihi 30 Eylül 2017
Kabul Tarihi 12 Nisan 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 27 Sayı: 3

Kaynak Göster

APA ÇEVİK, K. K., & KOÇER, H. E. (2017). COMPUTER AIDED CONTROL OF CUTTING ERROR IN TEXTILE PRODUCTS. Textile and Apparel, 27(3), 300-308.

No part of this journal may be reproduced, stored, transmitted or disseminated in any forms or by any means without prior written permission of the Editorial Board. The views and opinions expressed here in the articles are those of the authors and are not the views of Tekstil ve Konfeksiyon and Textile and Apparel Research-Application Center.