Journal of Textiles and Polymers

Open Access and Free of Charge

Online Control System Design for Selvedge Waste Length in Rapier Weaving Loom

Document Type : Original Article

Authors

Textile Engineering Department, Yazd University, Yazd, Iran.

Abstract

Controlling the selvedge waste length in shuttle-less weaving loom has great importance in the cost of production. In this study, an online control system is designed and implemented in a weaving loom. First, a high-speed camera records selvedge formation in several successive cycles with different weft yarn tensions. Then the length of weft yarn waste is measured by three methods based on image processing, namely Kalman filter, K-means method and background subtraction. The performances of three methods in terms of accuracy and processing time are evaluated and compared with each other. The results show that Kalman filter method has higher accuracy and it requires lower processing time. In addition, it shows that the results obtained from two other methods are very close to the actual result. There is an inverse relationship between weft yarn tension and selvedge waste length. By increasing the yarn tension, waste length is decreased. Therefore, based on the online measurement of selvedge waste length, the waste length can be measured in each cycle and adjusted by changing the weft yarn tension. So the proposed system has satisfactory performance in online control of weft yarn waste.

Keywords

References
[1] J.L. Dorrity and G. Vachtsevanos, “On-line defect detection for weaving system”, In: Proceedings of IEEE Annual Textile Fiber and Film Industry Technical Conference, Atlanta, USA, 1996.
[2] M.W. Suh, W. Jasper, and A. Cherkassky, “3-D electronic imaging of fabric qualities by on-line yarn data”, Ann. Rep. Text. Ctr., PA, 2003.
[3] C. Cho, B.M. Chung, and M.J. Park, “Development of real-time vision-based fabric inspection system”, IEEE T. Ind. Electron., vol. 52, no. 4, pp. 1073-1079, 2005.
[4] K.L. Mak and P. Peng, “An automated inspection system for textile fabrics based on Gabor filters”, Robot. Cim.Int. Manuf., vol. 24, no. 3, pp. 359–369, 2008.
[5] D. Schneider, T. Holtermann, F. Neumann, A. Heh, T. Aach, and T. Gries, “A vision based system for high precision online fabric defect detection”, In: Proceedings of 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, Singapore, 2012.
[6] A.S. Malek, “Online fabric inspection by image processing technology” Ph.D. Thesis, Mech. Eng., University of Haute Alsace, Mulhouse, Sud Alsace, France, 2012.
[7] M.A. Aziz, A.S. Haggag, and M.S. Sayed, “Fabric defect detection algorithm using morphological processing and DCT”, In: Proceedings of 1st International Conference on Communications, Signal Processing, and their Applications, Sharjah, United Arab Emirates, 2013.
[8] A.A. Zefrehyee, M.A. Tehran, H. Norsaty, and M. Abhajani, “On-line loom weft density control system design”, J. Eng. Fiber. Fabr., vol. 10, no. 4, pp. 28-36, 2015.
[9] G. Vladimir, B. Dmitriy, T.N. Win, and I. Evgen, “The vision system in the weaving loom industry”, In: Proceedings of IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, Petersburg, Russia, 2017.
[10] R. Akdeniz, H.Z. Ozek, and G. Durusoy, “A study of selvedge waste length in rapier weaving by image analysis techniqueˮ, Text. Konfeksiyon, vol. 27, no. 1, pp. 22-26, 2017.
[11] R.E. Kalman, “A new approach to linear filtering and prediction problems”, J. Basic Eng.-T. Asme, vol. 82, no. 1, pp. 35–45, 1996.
[12] J. Scott, M. Pusateri, and D. Cornish, “Kalman filter based video background estimation”, IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 2009.
[13] N. Dehghan, P. Payvandy, and M.A. Tavanaei, “Nano fiber images thresholding based on imperial competitive algorithm”, Int. J. Comput. Appl. T., vol. 99, no. 6, pp. 37-47, 2014.
[14] J.B. MacQueen, “Some methods for classification and analysis of multivariate bservations”, In: Proceedings of Fifth Berkeley Symposium on Mathematics: Statistics and Probability, Berkeley: University of California Press, 1967.
[15] N. Otsu, “A threshold selection method from graylevel histogram”, IEEE T. Syst. Man. Cyb., vol. 9, no. 1, pp. 62-66, 1975.
[16] B.D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision”, In: Proceedings of International Joint Conference on Artificial Intelligence, San Francisco, USA, 1981.
[17] B.D. Wang and P. Dudek, “A fast self-tuning background subtraction algorithm”, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 2014.
Journal of Textiles and Polymers
Volume 7, Issue 2
June 2019
Pages 73-80
Files
Share
How to cite
Statistics