34—a Generalized Equation for Predicting the Lea Strength of Ring-Spun Cotton Yarns

Subramanian, Thyagarajan; Ganesh, K.; Bandyopadhyay, Siddhartha

doi:10.1080/00405007408630474

Cited by 19 publications

(4 citation statements)

References 3 publications

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“…It is, therefore, important to establish which fiber and yarn parameters influence the yarn tensile properties and if possible, to derive functional relationship between them. So far, numerous mathematical and empirical models have been established for the estimation of single yarn tenacity [3,5,[11][12][13] and count strength product (CSP) [1,3,14,15] using fiber properties and some yarn parameters.…”

Section: And Hüseyin Kadoglumentioning

confidence: 99%

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Üreyen

Kadoğlu

2006

Textile Research Journal

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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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Section: And Hüseyin Kadoglumentioning

confidence: 99%

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Üreyen

Kadoğlu

2006

Textile Research Journal

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“…Mathematical models developed by Bogdan [1,2], Subramanian, Ganesh and Bandyopadhyay [3], Zurek, Frydrych and Zakrzewski, [4] and Frydrych [5] are very appealing as they are based on the fundamental theories of basic sciences and give good understanding about the mechanics of the process. However, the prediction accuracy of mathematical models is not very encouraging due to the assumptions or simplifications used while building these models.…”

Section: Introductionmentioning

confidence: 99%

Yarn Strength Modelling Using Fuzzy Expert System

Majumdar¹,

Ghosh²

2008

Journal of Engineered Fibers and Fabrics

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Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.

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“…Several research workers have developed theoretical models for predicting yarn strength from fiber properties [1][2][3][5][6][7][8][9][10][11][12][13][14][15][16][17]: Bogdan [1,2], Hearle [6], Subramanium et al [17], Pheonix [14], Pitt and Pheonix [15], Zurek et al [19] Frydrych [3], Ö nder and Baser [12], Langenhove [10], Rajamanickam et al [16], Suh et al [18], Pan et al [13], Jiang et al [8], to name a few, have made significant contributions in this field. Other researchers have used statistical regression analysis to predict yarn strength: Hafez [5], Hunter and Gee [7], Mogahzy [11], etc.…”

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confidence: 99%

Analysis of Spun Yarn Failure. Part II: The Translation of Strength from Fiber Bundle to Different Spun Yarns

Ghosh

Ishtiaque

Rengasamy

2005

Textile Research Journal

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A generalized mathematical model has been developed for predicting the strength of spun yarns produced by ring, rotor, air-jet and open-end friction spinning systems. The model is based on the translation of fiber bundle tenacity into the yarn tenacity and it is applicable to all spun yarns. The model describes the failure behavior of spun yarns. Experimental strength results are compared with the predicted ones on the basis of theory, and a satisfactory agreement is reached. The limitations of the model are also analyzed.

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34—a Generalized Equation for Predicting the Lea Strength of Ring-Spun Cotton Yarns

Cited by 19 publications

References 3 publications

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Yarn Strength Modelling Using Fuzzy Expert System

Analysis of Spun Yarn Failure. Part II: The Translation of Strength from Fiber Bundle to Different Spun Yarns

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