A New Approach for Predicting Strength Properties of Yarn

Frydrych, Iwona

doi:10.1177/004051759206200606

Cited by 53 publications

(32 citation statements)

References 21 publications

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“…15 Many other workers have since followed the examination of fiber and yarn relationships, particularly of yarn strength. [16][17][18][19][20][21][22][23][24] The advent of instruments to measure new aspects of fiber quality, especially from the 1970s with the introduction of HVI lines, meant easier examination of these original relationships. Improved computing power from the 1980s also meant fiber and yarn relationships were defined more in terms of their fit with statistical models, for example, multiple linear regression, principal component analysis and more recently Neural Network or Fuzzy Logic models.…”

Section: Yarn Quality Prediction Modelsmentioning

confidence: 99%

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Accurate prediction of cotton ring-spun yarn quality from high-volume instrument and mill processing data

Yang

Gordon

2016

Textile Research Journal

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The derivation and performance of yarn quality prediction models in a program called Cottonspec is reported. Cottonspec incorporates a large database of fiber and yarn data from commercial spinning mills, a series of regression-based models predicting yarn quality from measured cotton fiber quality parameters and yarn specifications and a user interface. The inclusion of independent variables into prediction equations was dependent on the criteria that their inclusion was statistically significant and that variables had a theoretically direct influence on yarn structure. Yarn data was corrected to allow for twist, yarn count and yarn irregularity before correlation with fiber properties. Differences in yarn testing results between mills could be corrected by a Mill Correction Factor. Adherence to these criteria and the ability to draw on the very large database meant prediction ability of the models was excellent, as demonstrated in a series of cross-validations.

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Section: Yarn Quality Prediction Modelsmentioning

confidence: 99%

“…Theory and empirical evidence [16][17][18][19][20][21][22][23] have long shown that yarn tenacity is primarily but not solely determined by fiber tenacity . Figures 8(a) and (b) show the strong correlations observed between cotton fiber tenacity and yarn tenacity for Datasets 1 and 2.…”

Section: Yarn Tenacitymentioning

confidence: 99%

Accurate prediction of cotton ring-spun yarn quality from high-volume instrument and mill processing data

Yang

Gordon

2016

Textile Research Journal

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“…The performance of the ANN was assessed using the root mean of fibers, yarns, and fabrics, the prediction of yarn strength by Frydrych [12], and works performed by Vitro et al, [13] Kim and El-sheikh [14] and Yong Ku Kim [15] and El-sheikh [16] can be mentioned. However, statistical regression models for this purpose have been used by some researchers, namely El-Mogahzy [17], Hunter [18] and Alsaid Ahmed Almetwally [19,20] and M M Mourad [21].…”

Section: Performance Of the Neural Networkmentioning

confidence: 99%

Predicting the Tensile and Air Permeability Properties of Woven Fabrics Using Artificial Neural Network and Linear Regression Models

Abou-Nassif¹

2015

J Textile Sci Eng

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Prediction of yarn and woven fabric propertiesGenerally, modeling and prediction of yarn and fabric properties based on fiber properties, fabric characteristics and process parameters have been considered by many researchers. Over the years, one of the first approaches has been the use of mechanistic models. In this category, some studies such as the work of Alsaid A Almetwally [9] and Pinar [10] to predict cotton yarn strength and spinning quality, Hearle, El-Behery and Thakur [11] in relation to structural mechanics AbstractThe objective of this paper is to investigate the predictability of some of woven fabric properties using artificial neural network (ANN) and regression models. For achieving this purpose, a neural network with three layers was adopted. The regression model was of type a multiple -linear regression one. The independent variables were weft yarn count, twist multiplier and weft density; and the dependent ones were tensile strength, breaking extension and air permeability of the woven fabrics. The ANN and regression models were assessed using the Root means square error (RMSE) and the coefficient of determination (R2-value). The findings of this study revealed that ANN is superior to regression model in predicting the woven fabric properties.

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“…Treloar and Riding (1963) used strain energy approach and interpreted the tensile properties of twisted filament yarn in terms of their geometrical structure and the properties of constituent filaments. Fredrych (1992) has predicted the tensile response of cotton yarns by incorporating the migrating properties of the fibres. Onder and Baser (1996) have investigated the stress-strain and breakage behaviour of staple yarn based on conical helix model of fibre migration.…”

Section: Introductionmentioning

confidence: 99%

Effect of yarn extension on migration parameters of fibres in yarns

Kumar¹,

Ishtiaque²,

Das³

2012

Journal of The Textile Institute

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The present paper deals with a detailed study on the effect of progressive yarn extension on fibre migration. An image processing-based system to characterise and visualise the configuration of fibres in yarn in extended mode has been adopted. A detailed study has been reported on the changes in migration parameters, like mean fibre position (MFP), mean migration intensity (MMI) and root mean square deviation (RMSD), during axial extension of ring-spun yarn. It has been observed that the fibre migration parameters, like MFP, MMI and RSMD, are significantly affected by extension of ring-spun yarn and the trends are different for different levels of extension. It has been observed that the mean fibre position (MFP) increases slightly up to 4% of yarn extension and then decreases up to 8% extension but with a further increase of yarn extension up to 12%, the value of MFP slightly increases again. It has also been reported that with the increase of yarn extension the average RMSD and MMI.

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A New Approach for Predicting Strength Properties of Yarn

Cited by 53 publications

References 21 publications

Accurate prediction of cotton ring-spun yarn quality from high-volume instrument and mill processing data

Accurate prediction of cotton ring-spun yarn quality from high-volume instrument and mill processing data

Predicting the Tensile and Air Permeability Properties of Woven Fabrics Using Artificial Neural Network and Linear Regression Models

Effect of yarn extension on migration parameters of fibres in yarns

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