Predicting Hairiness for Ring and Rotor Spun Yarns and Analyzing the Impact of Fiber Properties

Zhu, Reiyao; Ethridge, M. Dean

doi:10.1177/004051759706700909

Cited by 59 publications

(28 citation statements)

References 5 publications

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“…This analytical system is also useful for discovering relationships between variables [9]. Chattopadhyay and Guha [10] have reviewed textile applications of artificial neural networks in detail.…”

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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“…Researches done between 1950-1992 about yarn hairiness have been reviewed [2,3] and statistical equations especially using regression analysis have been established for the relationship between fibre properties and yarn hairiness [4][5][6][7] and unevenness [8][9][10]. In recent years, Artificial Neural Network methods have been widely used for the prediction of hairiness and unevenness [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. II. Prediction of yarn hairiness and unevenness

Üreyen

Gürkan

2008

Fibers Polym

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The objective of this second part of the study is to develop artificial neural network models for the prediction of yarn hairiness and unevenness and to compare the performance of ANN models with our previous statistical models based on regression analysis. Besides HVI properties, yarn count, twist and roving properties were also selected as input variables. Part 1 provided detailed description of experimental procedure of the study. Yarn hairiness and unevenness tests were performed on Uster Tester 3. Following the developed ANN models, sensitivity analysis results and coefficient of multiple determination (R 2 ) values of ANN and regression models were compared. Analyses are showed that ANN models improve the prediction performance with regards to regression models.

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“…This computational model was trained to capture nonlinear relationship between input and output variables with scientific and mathematical basis. In recent days, commonly used model is layered feed-forward neural network with multi layer perceptions and back propagation learning algorithms (Vangheluwe et al, 1993, Rajamanickam et al, 1997, Zhu & Ethridge, 1997and Wen et al, 1998.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Debnath¹

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Predicting Hairiness for Ring and Rotor Spun Yarns and Analyzing the Impact of Fiber Properties

Cited by 59 publications

References 5 publications

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. II. Prediction of yarn hairiness and unevenness

Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

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