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Online Sewing Defect Monitoring for SNLS Machine by Image Processing Technique


Affiliations
1 Department. of Textile Technology, Department. of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore-641049, India
     

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Apparels are subjected to visual examination to detect sewing defects after making of the garments which results in higher rejection, time, cost etc. Sewing defects must be detected early i.e during sewing itself and accurately to overcome above quality issues. Apparels are mostly sewn with lock stitch in straight and curve directions, with different colours and stitches per inch. The paper discuss the on line detection of sewing defects occurring during the sewing process. Common defects such as skipped stitch, missed stitch, or loose stitch occurring in lockstitch are detected and marked. Using image processing methods, the proposed work follows the stitch path by capturing digital images of stich lines in lock stitch sewing machine and processed through PYTHON software to detect the sewing defects and subsequently stop the machine during sewing.

Keywords

Sewing, Defect, Online, Image Processing, SNLS.
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  • H. Carvalho, A. Rocha, J. L. Monteiro, L. F. Silva et al.), “An innovative device for bobbin thread consumption measurement on industrial sewing machines”, IEEE International Conference on industrial Technology, 2004,pp 1034-1039

  • W. D. Samuel and V. Poojitha, “A simple system for the online detection of SKIP/LOOP stitches in single needle lockstitch sewing machines,” AUTEX Research Journal, 2010.vol. 10, no. 3, pp. 69–72.

  • T. G. Clapp, L. H. Olson, K. J. Titus, L. Dorrity et al. , “The online inspection of sewn seams”, National Textile Centre Annual Report, November, 1996.pp 173-182.

  • Michael K. Ng, Henry Y. T. Ngan, Xiaoming Yuan, and Wenxing Zhang, “Patterned Fabric Inspection and Visualization by the Method of Image Decomposition”, IEEE transactions on auto’ science and Engineering, 2014.vol. 11, no. 3, pp. 943 – 947.

  • H. G. Bu, J. Wang, and X. B. Huang, “Fabric defect detection based on multiple fractal features and support vector data description,” Eng. Appl. Artificial Intelligence, 2009.vol. 22, no. 2, pp. 224–235,

  • R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital Image Processing using MATLAB”, 2005.ISBN 81-297-0515-X,.

  • U. Farooq, T. King, P. H. Gaskell, and N. Kapur, “Machine vision using image data feedback for fault detection in complex deformable webs,” Trans. Inst. Meas. Control, 2004.vol. 26, no. 2, pp. 119–137.

  • PejmanNiksaz,” Automatic Traffic Estimation Using Image Processing”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 4, December, 2012, pp.167-174.

  • Raluca Brad, LaviniaBarac, and Remus Brad, “Defect Detection Techniques for Airbag Production Sewing Stages,” Journal of Textiles, vol. 2014, Article ID 738504, 7 pages, 2014. doi:10.1155/2014/738504.

  • C.H. Chan, Hugh Liu, Thomas Kwan and G. Pang, “Automation Technology For Fabric Inspection System,” Proc. Conf. Applications of Automation Science and Technology, City University of Hong Kong, 1998.pp. 24-26, Nov.

  • H. Y. T. Ngan and G. K. H. Pang, “Novel method for patterned fabric inspection using Bollinger bands,” Opt. Eng., 2006.vol. 45, no. 8,

  • Z. D. Tsai and M. H. Perng, “Defect detection in periodic patterns using a multi-band-pass filter,” Mach. Vision Appl., 2013.vol. 24, pp. 551–565,

  • C. Kwak, J.A. Ventura, K. Tofang-Sazi, Automated defect inspection and classification of leather fabric, Intelligent Data Analyzation 5 .2001. 355–370.

  • A. Abouelela, H.M. Abbas, H. Eldeeb, A.A. Wahdan, S.M. Nassar, Automated vision system for localizing structural defects in textile fabrics, Pattern Recognition Lett. 26 .2005. 1435–1443.

  • K. Yin and W. Yu, “Image processing for the use of garment production detection system,” in Proceedings of the 1st International Congress on Image and Signal Processing (CISP '08), 2008.vol. 3, pp. 349–352.

  • B. Karunamoorthy. and D. Somasundaraseswari ,”Automated patterned fabric fault detection using image processing” International Journal of Advanced Research in Computer Engineering and Technology,2015.Vol.4, pp.63-69.

  • K. L. Mak and W. Li, “Objective evaluation of seam pucker on textiles by using self-organizing map,” IAENG International Journal of Computer Science, 2008.vol. 35, no. 1, pp. 47–54.

  • C. L. P. Hui, N. S. Fun, and C. Ip, “Review of application of artificial neural networks in textiles and clothing Industries over last decades,” in Artificial Neural Networks Industrial and Control Engineering Applications, K. Suzuki, Ed., INTECH, 2011.


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  • Online Sewing Defect Monitoring for SNLS Machine by Image Processing Technique

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Authors

V. Ramesh Babu
Department. of Textile Technology, Department. of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore-641049, India
B. Karunamoorthy
Department. of Textile Technology, Department. of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore-641049, India

Abstract


Apparels are subjected to visual examination to detect sewing defects after making of the garments which results in higher rejection, time, cost etc. Sewing defects must be detected early i.e during sewing itself and accurately to overcome above quality issues. Apparels are mostly sewn with lock stitch in straight and curve directions, with different colours and stitches per inch. The paper discuss the on line detection of sewing defects occurring during the sewing process. Common defects such as skipped stitch, missed stitch, or loose stitch occurring in lockstitch are detected and marked. Using image processing methods, the proposed work follows the stitch path by capturing digital images of stich lines in lock stitch sewing machine and processed through PYTHON software to detect the sewing defects and subsequently stop the machine during sewing.

Keywords


Sewing, Defect, Online, Image Processing, SNLS.

References