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In this research to deal and classify the grade of yarn appearance

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In this research, to deal and classify the grade of yarn appearance by using fuzzy logic control system (FLCS) and image processing in matlab, first simple graphical user interface has been required in order to process the image and to get pixel values of the binary image. From this binary image number of black and white pixels, the white color pixel will be taken and formulate some logic rule in fuzzy logic method based on the above given table of the standard image pixels for each series of yarn count (from 8 to 590 Tex).

Conclusion Traditionally, based on the method of ASTM (D-2255), the yarn quality checker sometimes give same grade for which has different yarn surface appearance of the same yarn count. Even in case of blackboard warping, due to the difficult of precisely evaluating the yarn appearance and when sometimes the grade falls between the two consecutive grades, they denotes the grade by +’ sign after the letter based on their perception.

In this paper the yarn appearance classification will be analyzed based on the fuzzy logic system (FLS) after matlab image processing. Generally many methods are available to determine the yarn evenness, many of them are tedious and depend on the operator for this result, while others those less subjective and of high speed, are probability expensive and required high cost for maintenance during damaging time because of their more sensitive and easily damageable parts. Therefore, this research will propose a new and simple method which required cost effective image capture device and computer in addition to the blackboard yarn winding device to evaluate yarn appearance and get accurate grade result by removing the manual testing method limitation.

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Journal review references1. ASTM International (D2255-2), Standard Test Method for Grading Spun Yarns for Appearance, PO Box C700, West Conshohocken, PA 19428-2959, United States, (2002) .2. Member ITEEE, Jayaashree V., And Deepgana.I.Dhap (2013), developing two traditional methods for yarn diameter measurement’ 3. Liang Z. (2012), Content-Sensitive Salient Region Modelling With Applications’, Hong Kong Polytechnic University, Department of Electronic and Information Engineering.4. Kaur M., And Sharma M. (2014), Evaluation of Yarn Quality in Fabric Using Image Processing Techniques’, Journal of Applied Engineering and Technology: Indian, Vol. 4, pp.54-60.5. Niles SN. Dias WPP, Perera TKM, Vinoth W., and Wijenayake EMR. (2017), A Vision-Based Method for Analyzing Yarn evenness’ Journal of Scientific & Technology Research: Volume 6, Issue 02.6. H. Souid, M. Sahnoun, A. Babay and M. Cheikrouhou (2012), A Generalized Model for Predicting Yarn Global Quality Index’, Journal Textile Research Unit of ISET of Ksar-Hellal: Tunisdia, Volume 5 9.7. Junjuan Li, Baoqi Zuo, Chen Wang, Wenxiao Tu (2015), A Direct Measurement Method of Yarn Evenness Based on Machine Vision’, Soochow University, Textile and Clothing Engineering, CHINA Volume 10, Issue 4. 8. Zhang D., And Chang L. (2010), Comparison of Two Different Yarn Evenness Test Methods’, China, College of Textiles, Tianjin Polytechnic University.9. R. Abd El-Khalek, R. El-Bealy, and A. El-Deeb (2014), A Computer-Based System for Evaluation of Slub Yarn Characteristics’, Egypt, Mansoura University, Textile Engineering Department: Volume 2014, Article ID 784516.10. Semndani D., Latifi M., Amani M., Pourdeyhimi B., and Akbar Ma. (2005), Development of Appearance Grading Method of Cotton Yarns for Various Types of Yarns’, Journal of Textile and Apparel: Vol. 9 No. 4.11. Mouekova E., and Jiraskova P. (2012), New Possibility of Objective Evaluation of Yarn Appearance’, Journal of Department of Textile Technologies: Vol. 12, No1.12. Yan Li, FENG J., Gang XU and Ming TAU, Integrated Digital System for Yarn Surface Quality Evaluation using Computer Vision and Artificial Intelligence’, Institute of Textiles and Clothing: Hong Kong Polytechnic University.13. Semnani D., Latifi M., Pourdeyhimi B., and Akbar M. (2006), Grading of Yarn Appearance Using Image Analysis and an Artificial Intelligence Technique’, Textile Research Journal: Vol 76(3).14. Chandran N., And Yovaraj D. (2012), A simple yarn hairiness measurement setup using image processing technique’, journal of fiber and textile research: Indian second reviewed, vol. 37, pp. 33-336. 15. Haleem N., And Wang X. (2013), A comparative study on yarn hairiness results from manual test and two commercial hairiness meters’, Journal of The Textile Institute: Vol. 104, No. 5, 494″501.16. Ghazi A., Khaddam H., and Horani M. (2018), A New Method to Evaluate the Appearance of Cotton Yarn Using IP and FIS Supported with Graphical User Interface’, Journal of Textile Science & Engineering: Damascus University, Syria.17. Shedthi B., Shetty S., and Siddappa M. (2017), Development and Implementation of Graphical User Interface for Image Preprocessing using Matlab’, Journal of Computer Applications: Volume 161 ” No 9.

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In this research to deal and classify the grade of yarn appearance. (2019, Aug 20). Retrieved from

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