Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks

doi:10.3993/jfbim00345

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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%.

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

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	Articles by authors
	Manal R. Abdel-Hamied
	Sherien ElKateb
	Adel El-Geiheini

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

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