Dimensional Characteristics of Knitted Fabrics Made from 100% Cotton and Cotton/Elastane Yarns

The dimensional characteristics such as loop length (l), wales per centimeter (wpc), courses per centimeter (cpc), stitch density (s), tightness factor (K), loop shape factor (R) and take-up rate (T) of single jersey, 1x1rib, 1x1 interlock, single pique, and two-thread fleece knitted fabrics made from 100% cotton and cotton/elastane yarns (5% elastane yarn content) are investigated in this research. Dimensional properties of knitted fabrics are an important property and determine the materials consumption during production, productions parameter, and applications of different knitted structures. The sample fabrics have been conditioned for 24 hours at 20±1°C temperature and 65±2% relative humidity. The specimens used for sampling are determined as per the test standards described in the paper for each yarn type, property, and structure. As observed in the result, the properties are related to each other. It is found that the loop length, wpc, cpc, stitch density, tightness factor, loop shape factor and take-up rate of single jersey, 1x1rib, 1x1interlock, single pique, and two-thread fleece knitted fabrics made from 100% cotton and cotton/elastane yarns are significantly influenced by the presence of an elastane yarn. The loop length of single jersey, 1x1rib, and interlock knitted fabrics made from elastane yarns reduced while the single pique and fleece increased. Similarly, other dimensional properties are significantly influenced by the yarn types used during knitting.

Characterization and statistical modelling of thermal resistance of cotton/polyester blended double layer interlock knitted fabrics

The aim of this study was to analyse and model the effect of knitting parameters on the thermal resistance of cotton/polyester double layer interlock knitted fabrics. Fabric samples of areal densities ranging from 310-495 g/m2 were knitted using yarns of three different cotton/polyester blends, each of two different linear densities by systematically varying knitting loop lengths for achieving different cover factors. It was found that by changing the polyester content in the inner and outer fabric layer from 40 to 65% in the double layer knitted fabric has statistically significant effect on the fabric thermal resistance. Fabric thermal resistance increased with increase in relative specific heat of outer fabric layer, yarn linear density, loop length, and fabric thickness while decrease in fabric areal density. It was concluded that response surface regression modelling could be successfully used for the prediction of thermal resistance of double layer interlock knitted fabrics. The model was validated by unseen data set and it was found that the actual and predicted values were in good agreement with each other with less than 10% absolute error. Sensitivity analysis was also performed to find out the relative contribution of each input parameter on the air permeability of the double layer interlock knitted fabrics.

Part II. Predicting the Pilling Tendency of the Cotton Interlock Knitted Fabrics by Artificial Neural Network

Artificial neural network (ANN) is a mathematical model inspired by biological neural networks and it processes information using a connectionist approach to computation. The aim of the second part of the study is to determine models for estimating the pilling propensity of the interlock knitted fabrics produced from yarns of different yarn counts (Ne 20, Ne 30, Ne 40) and yarn twist coefficients (αe=3.2, αe=3.6, αe=4.0) spun by using seven different cotton types harvested from different regions. The fabrics were manufactured in three different tightness factors, including dense, medium, and loose, by changing the yarn length utilized in each course of the fabrics. The models for pilling degree, total pill number, total weighted pill number, average pill area, and average pill height of the fabrics evaluated by PillGrade Objective Pilling Grading System, were derived by using a neural network method. In order to define the effective properties on pilling formation, sensitivity analysis was carried out. All models indicated relatively good estimation power. Fabric cover factor and short fiber content were found as the most significant parameters influencing the pilling propensity feature of the interlock knitted fabrics.

Part 1. Predicting the Pilling Tendency of the Cotton Interlock Knitted Fabrics by Regression Analysis

This study, it was aimed to determine the equations and models for estimating the pilling propensity of interlock knitted fabrics. Seven different cotton blends supplied from different spinning mills, yarns in 3 different yarn counts (Ne 20, Ne 30 and Ne 40) and in 3 different twist coefficients (αe=3.2, 3.6 and 4.0) were produced. Interlock knitted fabrics were manufactured in three different fabric tightness values from each of the produced yarns. The pilling tendencies of the fabrics were tested according to EN ISO 12945–2 standard by a Martindale pilling and abrasion device. The PillGrade Objective Pilling Grading System, based on the image analysis principle, was used for evaluating the pilling propensity of the fabrics. By using this system, the pilling degree of the fabrics, total pill number, total weighted pill number, average pill area, and average pill height of the fabrics were measured. Fiber features determined by an AFIS PRO 2 instrument with the samples taken from the cotton roving were used as independent variables for the regression analysis. Moreover, yarn unevenness, yarn twist, yarn count, yarn hairiness, and fabric cover factor values were included in the equations as independent variables; and by considering each of the pilling features measured by PillGrade as a dependent variable, multivariate linear regression equations were determined and the availability of the equations was investigated in detail statistical analyses.