A Critical Evaluation of the Relationship Between Fiber Quality Parameters and Hairiness of Cotton Yarns

Viswanathan, G.; Munshi, V.G.; Ukidve, A; Chandran, K.S. Ravi

doi:10.1177/004051758905901115

Cited by 18 publications

(16 citation statements)

References 10 publications

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“…Nep counts were higher for yarns from Site 6 than from Sites 5 and 7 or from 2007, but fewer neps were detected relative to yarns from 2006, which may be the result of high fiber neps and/or of fiber immaturity (Frydrych and Matusiak, 2002;Wakelyn et al, 2007). However, yarns from Site 6 in 2008, which were characterized by high SFC, low maturity ratios and low fiber elongation, were hairier than any other yarns analyzed in this study (Viswanathan et al, 1989;Zhu and Ethridge, 1997). Substantially more fibers from Site 6 were combed out as noils, and stripped samples had, on average, over 3% more noils than picked samples.…”

Section: 5-tex (40-ne) Yarnsmentioning

confidence: 66%

Evaluation of Modern Cotton Harvest Systems on Irrigated Cotton: Yarn Quality

Faulkner¹,

Wanjura²,

Héquet³

et al. 2011

Applied Engineering in Agriculture

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The effects of harvest methods (cotton picker vs. cotton stripper) on yarn quality from irrigated cotton harvested on the High Plains of Texas with modern equipment was measured using multiple cultivars from six sites over three years. Few differences were detected in carded yarn quality between harvest treatments, while more pronounced differences favoring picked cotton were seen in combed yarns, especially when fibers were immature. During both 2006 and 2007, the evenness of combed yarns was improved by picking over stripping as measured by yarn CV, thick places, and neps (+200%), and the hairiness of carded yarns was reduced by picking. However, evenness differences between harvest methods were not detected in carded yarns. In 2007, cultivar had a greater impact on yarn quality than harvest treatment. This study represents the first commercial-scale harvester comparison project conducted in the High Plains region and the first study to analyze the effects of harvest method on ring-spun yarn quality from a traditional production system.

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Section: 5-tex (40-ne) Yarnsmentioning

confidence: 66%

Evaluation of Modern Cotton Harvest Systems on Irrigated Cotton: Yarn Quality

Faulkner¹,

Wanjura²,

Héquet³

et al. 2011

Applied Engineering in Agriculture

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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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“…Subject to the limitations imposed by the physical problems of measuring yarn hairiness and the constraints of the data set, some of these findings may be different from other studies [6,7]. Our results are limited to the testing and spinning conditions we used in this study.…”

Section: Discussionmentioning

confidence: 86%

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

Zhu

Ethridge

1997

Textile Research Journal

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Models for predicting ring or rotor yarn hairiness are built using a back-propagation neural network algorithm. These models are based on fiber property input measured by three different systems, hvi, afis, and fmt. We compare the prediction results from the different models, which reveal that yarn hairiness measurements from hvi data are superior to other models. The optimum model is based on the availability of all three measurement systems. We also study the impact of each fiber property on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or rotor yarn hairiness.

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A Critical Evaluation of the Relationship Between Fiber Quality Parameters and Hairiness of Cotton Yarns

Cited by 18 publications

References 10 publications

Evaluation of Modern Cotton Harvest Systems on Irrigated Cotton: Yarn Quality

Evaluation of Modern Cotton Harvest Systems on Irrigated Cotton: Yarn Quality

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

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

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