A Model to Estimate the Breaking Elongation of High Twist Ring Spun Cotton Yarns

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.

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confidence: 99%

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Üreyen

Kadoğlu

2006

“…However, prediction models dealing with the breaking elongation of cotton yarns are quite few in number. Mathematical models proposed by Aggarwal [1,2], Frydrych [6], and Zurec et al [16] predicted the breaking elongation of yarn only with limited success. On the other hand, statistical models yielded reasonably good elongation predictions [4,7].…”

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confidence: 99%

Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models

Majumdar

Majumdar²

2004

108

This paper presents a comparative study of three modeling methodologies for predicting the breaking elongation of ring spun cotton yarns. Constituent cotton fiber properties and yarn count are used as inputs to these models. The predictive powers of the three different models—mathematical, statistical, and artificial neural network—are estimated and com pared. The relative importance of various cotton fiber properties measured by a high volume instrument is also investigated using the artificial neural network model.

“…So, modeling and predicting yarn properties by putting forward the relationship between the fiber and the yarn properties are two of the most remarkable subjects for textile researchers. Many statistical [1][2][3][4][5][6][7][8][9] and mathematical [9][10][11][12][13][14][15][16] models have been developed to address prediction accuracy and general applicability. Mathematical models are usually based on certain idealized assumptions, so their success potential is mainly limited by the viability of these assumptions.…”

Section: Introductionmentioning

confidence: 99%

Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network

Turhan

Toprakci

2012

In this study, an artificial neural network (ANN) model is presented in order to predict the tenacity and hairiness of carded cotton yarns. Fiber measurement values generated by using a high-volume instrument (HVI) and an advanced fiber information system (AFIS) were used in the ANN model as input parameters. The radial basis function neural network (RBFNN) was used as ANN structure. The best RBFNN model was determined by analyzing the effect of epochs and the number of neurons on prediction performance. By using this ANN structure, the comparison between the performance of predicting yarn properties from HVIs and from AFISs was carried out. In the study, four different yarn counts (Ne20, Ne24, Ne30, and Ne40) for 10 different blends were applied. Each yarn count was spun at 4.34αe twist factor. In this study, the model presented a good rate of accuracy for predicting yarn tenacity and hairiness by using HVI and AFIS fiber values. The study showed that there was no significant difference between the accuracy of predicting these yarn properties from HVI fiber measurement results and those from an AFIS by using the RBF. From the results, it was noted that the performance of predicting yarn hairiness was better than that of predicting yarn tenacity. Also, this study could provide researchers with exclusive information on how to select the most appropriate ANN architecture and how to evolve the model for testing.