Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks
Year: 2021
Author: Manal R. Abdel-Hamied, Sherien ElKateb, Adel El-Geiheini
Journal of Fiber Bioengineering and Informatics, Vol. 14 (2021), Iss. 1 : pp. 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%.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbim00345
Journal of Fiber Bioengineering and Informatics, Vol. 14 (2021), Iss. 1 : pp. 13–20
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8
Keywords: Yarn coefficient of mass variation
Author Details
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