Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Abstract

The physical characteristics of a fiber determine its processing behavior, production efficiency and finally yarn and fabric quality. Therefore, predicting the quality characteristics of yarns, such as the tensile properties from the raw material properties, was the main purpose of many studies in the last century. In addition to raw material processing conditions, preparation stages, machine parameters and the spinning method also have considerable effects on the yarn properties. Generally two approaches were used in these studies for predicting yarn quality from fiber and yarn characteristics:• an empirical and statistical approach; and • a theoretical or analytical approach. 1The empirical and statistical approach to establishing a relationship between fiber and yarn quality characteristics has been the most popular method during the second half of the twentieth century. Fast and accurate measurement of fiber properties by means of high volume instruments (HVI) and more powerful computers are the two main reasons for this tendency. With this method, important fiber and yarn properties can be measured for a range of samples and by using these results empirical relationships have been established by means of statistical analysis. One of the Abstract The main aim of the present study was to predict the most important yarn quality characteristics derived from cotton fiber properties that were measured by means of an HVI system. With this aim 15 different cotton blends were selected from different spinning mills in Turkey. The cotton fibers were processed in the short staple spinning line at Ege University Textile and Apparel Research-Application Center and were spun into ring yarns (20s, 25s, 30s and 35s). Each count was spun at three different coefficients of twist (α e 3.8, α e 4.2 and α e 4.6). Linear multiple regression methods were used for the estimation of yarn quality characteristics. Yarn count, twist and roving properties all had considerable effects on the yarn properties and therefore these parameters were also selected as predictors. After the goodness of fit statistics very large R 2 (coefficient of multiple determination) and adjusted R 2 values were observed. Furthermore, analysis of variance tables showed that our equations were significant at the α = 0.01 significance level.

An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

“…Testing with a High Volume Instrument (HVI) (Uster Technologies AG, Uster, Switzerland) (Schleth et al 2007) is standard practice to determine these fibre attributes in many production regions, and these properties are used to establish the value of cotton fibre (USDA 2005). HVI properties explain much but not all of the variation in yarn strength, and significant work has been conducted into understanding the relative contribution of fibre properties (El Sourady et al 1974;Ureyen and Kadoglu 2006) and how these properties relate to yarn performance, through the development of fibre quality indices (Hunter 2004) or modelling techniques (Ramey et al 1977;Zurek et al 1987;Cheng and Adams 1995). There remain opportunities to include fibre quality measurements that may better explain yarn strength, for example by employing alternative attributes for, among others, the still commonly used micronaire measure.…”

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties

“…Generally two approaches were used in studies to predict yarn quality from fiber and yarn characteristics: theoretical approaches and statistical approaches. Statistical or empirical models have relatively higher predictive power than theoretical models [1][2][3][4]. Multiple regression analyses are the most common statistical methods.…”

“…Multiple regression analyses are the most common statistical methods. Several regression equations have been established [1][2][3][4][5]. Prediction of cotton yarn properties from fiber properties have also been reviewed in details [6].…”

Regressional Estimation of Cotton Sirospun Yarn Properties from Fibre Properties

“…Statistical analysis demonstrated that there was a nearly linear relationship between fi bre properties measured with HVI and sirospun yarn properties. Because of this, the linear multiple regression analysis method was chosen for this study [10].…”