@Article{JFBI-14-1,
author = {R., Manal, Abdel-Hamied and Sherien, ElKateb 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 = {<p>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&#39; 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%.</p>},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00345},
url = {https://global-sci.com/article/86399/analytical-study-of-factors-affecting-yarn-coefficient-of-mass-variation-estimated-by-artificial-neural-networks}
}