Volume 14, Issue 1
Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks

Manal R. Abdel-Hamied, Sherien ElKatebAdel El-Geiheini

Journal of Fiber Bioengineering & Informatics, 14 (2021), pp. 13-20.

Published online: 2021-01

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  • Abstract

Manufacturers aim to achieve the optimal quality, therefore, the evaluation of yarn parameters and the determination of factors that influence yarn quality is of great importance. The yarn coefficient of mass variation (CVm%) reflects the irregularity of the yarn which reflects the yarns' quality. This study investigates the parameters affecting the CVm% that was previously estimated using image processing and artificial neural networks. Yarn images and data were used as inputs into neural networks and CVm% was evaluated. In addition, two statistical methods were used which were: correlation and ANOVA to research the effect of yarn count, twist factor, blend ratio, and cotton type on CVm%. It was found that the yarn count and twist factor were the highest correlated parameters regarding CVm%.

  • Keywords

Yarn coefficient of mass variation Image Processing Artificial Neural Networks ANOVA Correlation

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

manal_ramzy@yahoo.com (Manal R. Abdel-Hamied)

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  • TXT
@Article{JFBI-14-13, author = {Abdel-Hamied , Manal R. and ElKateb , Sherien and El-Geiheini , Adel}, title = {Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2021}, volume = {14}, number = {1}, pages = {13--20}, abstract = {

Manufacturers aim to achieve the optimal quality, therefore, the evaluation of yarn parameters and the determination of factors that influence yarn quality is of great importance. The yarn coefficient of mass variation (CVm%) reflects the irregularity of the yarn which reflects the yarns' quality. This study investigates the parameters affecting the CVm% that was previously estimated using image processing and artificial neural networks. Yarn images and data were used as inputs into neural networks and CVm% was evaluated. In addition, two statistical methods were used which were: correlation and ANOVA to research the effect of yarn count, twist factor, blend ratio, and cotton type on CVm%. It was found that the yarn count and twist factor were the highest correlated parameters regarding CVm%.

}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00345}, url = {http://global-sci.org/intro/article_detail/jfbi/18574.html} }
TY - JOUR T1 - Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks AU - Abdel-Hamied , Manal R. AU - ElKateb , Sherien AU - El-Geiheini , Adel JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 13 EP - 20 PY - 2021 DA - 2021/01 SN - 14 DO - http://doi.org/10.3993/jfbim00345 UR - https://global-sci.org/intro/article_detail/jfbi/18574.html KW - Yarn coefficient of mass variation KW - Image Processing KW - Artificial Neural Networks KW - ANOVA KW - Correlation AB -

Manufacturers aim to achieve the optimal quality, therefore, the evaluation of yarn parameters and the determination of factors that influence yarn quality is of great importance. The yarn coefficient of mass variation (CVm%) reflects the irregularity of the yarn which reflects the yarns' quality. This study investigates the parameters affecting the CVm% that was previously estimated using image processing and artificial neural networks. Yarn images and data were used as inputs into neural networks and CVm% was evaluated. In addition, two statistical methods were used which were: correlation and ANOVA to research the effect of yarn count, twist factor, blend ratio, and cotton type on CVm%. It was found that the yarn count and twist factor were the highest correlated parameters regarding CVm%.

Manal R. Abdel-Hamied, Sherien ElKateb & Adel El-Geiheini. (2021). Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks. Journal of Fiber Bioengineering and Informatics. 14 (1). 13-20. doi:10.3993/jfbim00345
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