Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 12 Sayı: 2, 510 - 519, 30.06.2020
https://doi.org/10.29137/umagd.681653

Öz

Kaynakça

  • Adelson, E. H. and Wang, J. Y. A., (1992). “Single Lens Stereo with a Plenoptic Camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2. pp. 99–106, 1992.
  • Adem, K., and Orhan, U. “Imaging processing-based quality control of transversal seams in Tetra Brik Aseptic cartons,” 2013 21st Signal Process. Commun. Appl. Conf., pp. 1–4, 2013.
  • Cabral, J. D. D., and Araújo, S. A., (2012). “Computer Vision System for Automatic Quality Inspection of Glass Products Used for Food Packaging,” Int. Conf. Ind. Eng. Oper. Manag., pp. 1–10, 2012.
  • Chao, S. M., Tsai, D. M., Tseng, Y. H. and Jhang, Y. R., (2006). “Defect detection in low-contrast glass substrates using anisotropic diffusion,” Proc. - Int. Conf. Pattern Recognit., vol. 1, pp. 654–657, 2006.
  • Chen, X., Liu, N., You, B., and Xiao, B., (2016). “A novel method for surface defect inspection of optic cable with short-wave infrared illuminance,” Infrared Phys. Technol., vol. 77, pp. 456–463, 2016.
  • Elbehiery, H., Hefnawy, A., and Elewa, M., (2005). “Surface defects detection for ceramic tiles using image processing and morphological techniques,” Proc. - WEC’05 3rd World Enformatika Conf., vol. 5, no. 5, pp. 158–162, 2005.
  • Hocenski, Z., Vasili, S., and Hocenski, V., (2006). “Improved canny edge detector in ceramic tiles defect detection,” IECON Proc. (Industrial Electron. Conf., pp. 3328–3331, 2006.
  • Keser, T. Hocenski, Z., and Hocenski V., (2010). “Intelligent machine vision system for automated quality control in ceramic tiles industry,” Strojarstvo, vol. 52, no. 2, pp. 105–114, 2010.
  • Kumar, N., and Kaur N., (2013). “Detection of Defects in Glass Using Edge Detection with Adaptive Histogram Equalization,” Int. J. Innov. Res. Comput. Commun. Eng., vol.1, Issue 6, pp. 1321–1327, 2013.
  • Liu, Y. H. and Chen, Y. J. (2011). “Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description,” Int. J. Mol. Sci., vol. 12, no. 9, pp. 5762–5781, 2011.
  • Nishu and S. Agrawal (2011). “Glass Defect Detection Techniques using Digital Image Processing–A Review,” Spec. issues IP Multimed. Commun., vol. 1, pp. 65–67, 2011.
  • Öztürk, Ş. and Akdemir, B., (2018). “Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces,” Neural Comput. Appl., vol. 29, no. 8, pp. 107–116, 2018.
  • Öztürk, Ş. and Akdemir, B., (2015). “Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production,” Procedia - Soc. Behav. Sci., vol. 195, pp. 2675–2682, 2015.
  • Qu, T., Zou, L., Zhang, Q. X., Chen, and C. Fan, (2016). “Defect detection on the fabric with complex texture via dual-scale over-complete dictionary,” J. Text. Inst., vol. 107, no. 6, pp. 743–756, 2016.
  • Rosli, N. S., Fauadi, M. H. F. M., Awang, N. F., and Noor, A. Z. M. (2018). “Vision-based defects detection for glass production based on improved image processing method,” J. Adv. Manuf. Technol., vol. 12, no. Special issue1, pp. 203–212, 2018.
  • Singh, T., Lal Dua R., Agrawal, S. and Acharya, A. (2013). “Detection of Defects in Glass Sheet using C. S. C based Segmentation Method,” Int. J. Comput. Appl., vol. 68, no. 14, pp. 29–32, 2013.
  • Spinola, C., Canero,G. J. G., Moreno-Aranda, J. M. Bonelo, and M. Martin-Vazquez, (2011). “Real-time image processing for edge inspection and defect detection in stainless steel production lines,” 2011 IEEE Int. Conf. Imaging Syst. Tech. IST 2011 - Proc., no. May, pp. 170–175, 2011.
  • Zhao, J. Q. J. Kong, Zhao, X., J. Liu, and Y. Liu, (2011). “A method for detection and classification of glass defects in low resolution images,” Proc. - 6th Int. Conf. Image Graph. ICIG 2011, pp. 642–647, 2011.

A New Prototype That Performs Real-Time Error Detection in Glass Products

Yıl 2020, Cilt: 12 Sayı: 2, 510 - 519, 30.06.2020
https://doi.org/10.29137/umagd.681653

Öz

Due to their economical, ergonomic and processing power capabilities, unique designs and software development applications based on embedded systems are becoming more common every day in detecting errors in product output in quality control processes. In this study, an automated control system based on embedded system was performed to detect errors on the surfaces of products purchased from a glass factory that performed quality control manually by eye. A prototype consisting of the conveyor band and micro drive and camera embedded system was designed for the realization of this system. The embedded system has an open source software that works with morphological image processing techniques and makes boundary determination by gaussian method. The success rate of the system was found by classifying it with Support Vector Machine, Quadratics Discriminant and Medium Tree classifiers. The application of the system has been tested in a glass factory, and as a result of the test process, the system has achieved a high success rate of defect detection in glass products.

Kaynakça

  • Adelson, E. H. and Wang, J. Y. A., (1992). “Single Lens Stereo with a Plenoptic Camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2. pp. 99–106, 1992.
  • Adem, K., and Orhan, U. “Imaging processing-based quality control of transversal seams in Tetra Brik Aseptic cartons,” 2013 21st Signal Process. Commun. Appl. Conf., pp. 1–4, 2013.
  • Cabral, J. D. D., and Araújo, S. A., (2012). “Computer Vision System for Automatic Quality Inspection of Glass Products Used for Food Packaging,” Int. Conf. Ind. Eng. Oper. Manag., pp. 1–10, 2012.
  • Chao, S. M., Tsai, D. M., Tseng, Y. H. and Jhang, Y. R., (2006). “Defect detection in low-contrast glass substrates using anisotropic diffusion,” Proc. - Int. Conf. Pattern Recognit., vol. 1, pp. 654–657, 2006.
  • Chen, X., Liu, N., You, B., and Xiao, B., (2016). “A novel method for surface defect inspection of optic cable with short-wave infrared illuminance,” Infrared Phys. Technol., vol. 77, pp. 456–463, 2016.
  • Elbehiery, H., Hefnawy, A., and Elewa, M., (2005). “Surface defects detection for ceramic tiles using image processing and morphological techniques,” Proc. - WEC’05 3rd World Enformatika Conf., vol. 5, no. 5, pp. 158–162, 2005.
  • Hocenski, Z., Vasili, S., and Hocenski, V., (2006). “Improved canny edge detector in ceramic tiles defect detection,” IECON Proc. (Industrial Electron. Conf., pp. 3328–3331, 2006.
  • Keser, T. Hocenski, Z., and Hocenski V., (2010). “Intelligent machine vision system for automated quality control in ceramic tiles industry,” Strojarstvo, vol. 52, no. 2, pp. 105–114, 2010.
  • Kumar, N., and Kaur N., (2013). “Detection of Defects in Glass Using Edge Detection with Adaptive Histogram Equalization,” Int. J. Innov. Res. Comput. Commun. Eng., vol.1, Issue 6, pp. 1321–1327, 2013.
  • Liu, Y. H. and Chen, Y. J. (2011). “Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description,” Int. J. Mol. Sci., vol. 12, no. 9, pp. 5762–5781, 2011.
  • Nishu and S. Agrawal (2011). “Glass Defect Detection Techniques using Digital Image Processing–A Review,” Spec. issues IP Multimed. Commun., vol. 1, pp. 65–67, 2011.
  • Öztürk, Ş. and Akdemir, B., (2018). “Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces,” Neural Comput. Appl., vol. 29, no. 8, pp. 107–116, 2018.
  • Öztürk, Ş. and Akdemir, B., (2015). “Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production,” Procedia - Soc. Behav. Sci., vol. 195, pp. 2675–2682, 2015.
  • Qu, T., Zou, L., Zhang, Q. X., Chen, and C. Fan, (2016). “Defect detection on the fabric with complex texture via dual-scale over-complete dictionary,” J. Text. Inst., vol. 107, no. 6, pp. 743–756, 2016.
  • Rosli, N. S., Fauadi, M. H. F. M., Awang, N. F., and Noor, A. Z. M. (2018). “Vision-based defects detection for glass production based on improved image processing method,” J. Adv. Manuf. Technol., vol. 12, no. Special issue1, pp. 203–212, 2018.
  • Singh, T., Lal Dua R., Agrawal, S. and Acharya, A. (2013). “Detection of Defects in Glass Sheet using C. S. C based Segmentation Method,” Int. J. Comput. Appl., vol. 68, no. 14, pp. 29–32, 2013.
  • Spinola, C., Canero,G. J. G., Moreno-Aranda, J. M. Bonelo, and M. Martin-Vazquez, (2011). “Real-time image processing for edge inspection and defect detection in stainless steel production lines,” 2011 IEEE Int. Conf. Imaging Syst. Tech. IST 2011 - Proc., no. May, pp. 170–175, 2011.
  • Zhao, J. Q. J. Kong, Zhao, X., J. Liu, and Y. Liu, (2011). “A method for detection and classification of glass defects in low resolution images,” Proc. - 6th Int. Conf. Image Graph. ICIG 2011, pp. 642–647, 2011.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Çetin Cem Bükücü 0000-0001-8079-2260

Levent Gökrem 0000-0003-2101-5378

Yayımlanma Tarihi 30 Haziran 2020
Gönderilme Tarihi 29 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 12 Sayı: 2

Kaynak Göster

APA Bükücü, Ç. C., & Gökrem, L. (2020). A New Prototype That Performs Real-Time Error Detection in Glass Products. International Journal of Engineering Research and Development, 12(2), 510-519. https://doi.org/10.29137/umagd.681653
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.