Analytical Study of Factors Affecting Yarn Coefficient of Mass Variation Estimated by Artificial Neural Networks (Journal of Fiber Bioengineering and Informatics, 2023)
Manufacturers aim to achieve the optimal quality, therefore, the evaluation of yarn parameters and thedetermination of factors that influence yarn quality is of great importance. The yarn coefficient of massvariation (CVm%) reflects the irregularity of the yarn which reflects the yarns' quality. This studyinvestigates the parameters affecting the CVm% that was previously estimated using image processingand artificial neural networks. Yarn images and data were used as inputs into neural networks andCVm% was evaluated. In addition, two statistical methods were used which were: correlation andANOVA to research the effect of yarn count, twist factor, blend ratio, and cotton type on CVm%. It wasfound that the yarn count and twist factor were the highest correlated parameters regarding CVm%.