A New Production Recording Module Recommended for Online Analysis of Denim Fabrics Produced with Weaving Looms
Abstract
In the Textile and Clothing sector, while producing fabrics there is need for computer-based examination systems on weaving loom machines in order to do the fabric analysis and online defect detection process. Online recording systems are divided into two categories: moving and stationary. It has high costs due to the ability of stationary systems to record high resolution images. The developing of moving equipment offers low cost, but it can record depending on the type of fabric. An economic recording module is proposed compared to fabric limitation of this kind, high resolution recording. This module drives the camera on a linear rail mechanism and can adjust its movement according to the fabric production speed.
References
- Association, H. K., & Council, H. K. (2001). Textile Handbook. Hong Kong: Hong Kong Cotton Spinners Association. Berkay, B., & Hüseyin, Z. Ö. (2019). Dokuma kumaş hatalarının sistematik sınıflandırılması üzerine bir çalışma. Tekstil ve Mühendis, 26-114, 156-167.
- BMSvision. (2019). Cyclops. https://www.bmsvision.com/products/camera-inspection.
- Castilho, H. P., Gonçalves, P. J., & Serafim, A. L. (2007). Intelligent Real-Time Fabric Defect Detection. Image Analysis and Recognition, (s. 1297-1307). Berlin, Heidelberg.
- Goyal, A. (2018). Automation in fabric inspection. The Textile Institute Book Series (s. 75-107). Bhiwani, India: Woodhead Publishing.
- Hanbay, K. (2016). Yuvarlak örgü makineleri için görüntü işleme tabanlı kumaş hatası tespit sistemi. Phd thesis.
- Hanbay, K., Alpaslan, N., Talu, M. F., Hanbay, D., Karci, A., & Kocamaz, A. F. (2015). Continuous rotation invariant features for gradient-based texture classificatio. Comput. Vis. Image Underst., 132, 87-101.
- Kumar, A. (2008). Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics, 55(1), 348-363.
- Mak, K., Peng, P., & Lau, H. (2005). A real-time computer vision system for detecting defects in textile fabrics. IEEE International Conference on Industrial Technology. Hong Kong, China.
- Ngan, H. Y., Grantham, K. P., & Yung, N. H. (2011). Automated fabric defect detection—A review. Image and Vision Computing, 29(7), 442-458.
- Niles, S. N., Fernando, S., & Lanerolle, W. D. (2015). A System for Analysis, Categorisation and Grading of Fabric Defects using Computer Vision. Research Journal of Textile and Apparel, 19(1).
- Rasheed, A., Zafar, B., Rasheed, A., Ali, N., Sajid, M., Dar, . . . Tariq, M. (2020). Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review. Mathematical Problems in Engineering (Hindawi), 2020(8189403), 24.
- Sari-Sarraf, H., & Goddard, J. (1999). Vision system for on-loom fabric inspection. IEEE Transactions on Industry Applications , 35(6), 1252 - 1259.
- Schneider, D., & Aach, T. (2012). Vision-based in-line fabric defect detection using yarn-specific shape features. Image Processing: Machine Vision Applications V. Burlingame, California, United States.
- Şeker, A., Peker, K. A., Yüksek, A. G., & Delibaş, E. (2016). Derin Ögrenme ile Kumas Hatası Tespiti. 24th Signal Processing and Communication Application Conference.
Dokuma Tezgahında Üretilen Denim Kumaşlarının Online Analizleri için Önerilen Yeni Bir Üretim Kayıt Modülü
Abstract
Tekstil ve Hazır Giyim sektöründe dokuma tezgahlarında üretilen kumaşların bilgisayarla incelenmesi, otomatik hata tespiti ve kumaş analiz yazılımlarının geliştirilmesi için üretim anında online kayıt yapabilen sistemlere ihtiyaç duyulmaktadır. Online kayıt sistemleri hareketsiz ve hareketli olmak üzere ikiye ayrılmaktadır. Hareketsiz sistemlerin yüksek çözünürlükte görüntü kaydı yapabilmelerine karşılık yüksek maliyetlere sahip olduğu görülmektedir. Gelişmekte olan hareketli sistemler düşük maliyet sunmakta, ancak kumaş türüne bağlı kayıt yapabilmektedir. Bu çalışmada kumaş türü sınırlaması olmaksızın yüksek çözünürlükte kayıt yapabilen ekonomik bir kayıt modülü önerilmektedir. Bu modül, doğrusal raylı bir mekanizma üzerinde kamerayı hareketlendirmekte ve kumaş üretim hızına göre kamera hareket hızını ayarlayabilmektedir.
References
- Association, H. K., & Council, H. K. (2001). Textile Handbook. Hong Kong: Hong Kong Cotton Spinners Association. Berkay, B., & Hüseyin, Z. Ö. (2019). Dokuma kumaş hatalarının sistematik sınıflandırılması üzerine bir çalışma. Tekstil ve Mühendis, 26-114, 156-167.
- BMSvision. (2019). Cyclops. https://www.bmsvision.com/products/camera-inspection.
- Castilho, H. P., Gonçalves, P. J., & Serafim, A. L. (2007). Intelligent Real-Time Fabric Defect Detection. Image Analysis and Recognition, (s. 1297-1307). Berlin, Heidelberg.
- Goyal, A. (2018). Automation in fabric inspection. The Textile Institute Book Series (s. 75-107). Bhiwani, India: Woodhead Publishing.
- Hanbay, K. (2016). Yuvarlak örgü makineleri için görüntü işleme tabanlı kumaş hatası tespit sistemi. Phd thesis.
- Hanbay, K., Alpaslan, N., Talu, M. F., Hanbay, D., Karci, A., & Kocamaz, A. F. (2015). Continuous rotation invariant features for gradient-based texture classificatio. Comput. Vis. Image Underst., 132, 87-101.
- Kumar, A. (2008). Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics, 55(1), 348-363.
- Mak, K., Peng, P., & Lau, H. (2005). A real-time computer vision system for detecting defects in textile fabrics. IEEE International Conference on Industrial Technology. Hong Kong, China.
- Ngan, H. Y., Grantham, K. P., & Yung, N. H. (2011). Automated fabric defect detection—A review. Image and Vision Computing, 29(7), 442-458.
- Niles, S. N., Fernando, S., & Lanerolle, W. D. (2015). A System for Analysis, Categorisation and Grading of Fabric Defects using Computer Vision. Research Journal of Textile and Apparel, 19(1).
- Rasheed, A., Zafar, B., Rasheed, A., Ali, N., Sajid, M., Dar, . . . Tariq, M. (2020). Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review. Mathematical Problems in Engineering (Hindawi), 2020(8189403), 24.
- Sari-Sarraf, H., & Goddard, J. (1999). Vision system for on-loom fabric inspection. IEEE Transactions on Industry Applications , 35(6), 1252 - 1259.
- Schneider, D., & Aach, T. (2012). Vision-based in-line fabric defect detection using yarn-specific shape features. Image Processing: Machine Vision Applications V. Burlingame, California, United States.
- Şeker, A., Peker, K. A., Yüksek, A. G., & Delibaş, E. (2016). Derin Ögrenme ile Kumas Hatası Tespiti. 24th Signal Processing and Communication Application Conference.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | July 13, 2023 |
| Publication Date | July 14, 2023 |
| Submission Date | August 12, 2021 |
| Published in Issue | Year 2023 Volume: 15 Issue: 2 |
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