Determination of effect of input yarn tension on weight (GSM) of single jersey knitted fabrics using ANOVA model

Neway Seboka Debele¹

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.11 , pp.35-41, Nov-2021

Online published on Nov 30, 2021

Copyright © Neway Seboka Debele . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In Knitting technology, input yarn tension is one of the basic process controlling parameters which affects the fabric aerial weight (GSM) and hence the quality of the fabric. This study tries to develop a model showing the relation between input yarn tensions (g) with knit fabric aerial weight (GSM). For that, experimentation is done using 100 % Cotton yarn of 34’s used for making of single jersey plain knitted fabric. Keeping all machine and yarn parameters constant, five different levels of treatments of input yarn tension, which are selected randomly has been set by altering the position of tension pulley. The experimentation follows a single factor ANOVA with 5 levels of input yarn tension and 10 replicates of observations. Using significance level of ? = 0.05, the finding shows input yarn tension significantly affects the knitted fabric aerial weight (GSM). For comparing pairs of treatment means that significantly differs, a multiple comparison method is used. Other than treatment means between the first and second level of treatments and the fourth and fifth treatment levels, the rest pairs of treatment means are significantly different. The linear regression model is computed as Y= 95.644+9.9675 X1. From the experiment, a correlation coefficient (R2) of 84.4% is computed. In this study, plot for predicted versus residual value and plot for observed versus predicted value is also made, which shows the model is adequate and fitting.

Key-Words / Index Term :
Input yarn tension, GSM, 100 % Cotton yarn, ANOVA, linear regression model

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