A Method of Predicting the Strength and Breaking Strain of Cotton Yarn

Zurek, Witold; Frydrych, Iwona; Zakrzewski, S.

doi:10.1177/004051758705700802

Cited by 36 publications

(20 citation statements)

References 5 publications

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“…Testing with a High Volume Instrument (HVI) (Uster Technologies AG, Uster, Switzerland) (Schleth et al 2007) is standard practice to determine these fibre attributes in many production regions, and these properties are used to establish the value of cotton fibre (USDA 2005). HVI properties explain much but not all of the variation in yarn strength, and significant work has been conducted into understanding the relative contribution of fibre properties (El Sourady et al 1974;Ureyen and Kadoglu 2006) and how these properties relate to yarn performance, through the development of fibre quality indices (Hunter 2004) or modelling techniques (Ramey et al 1977;Zurek et al 1987;Cheng and Adams 1995). There remain opportunities to include fibre quality measurements that may better explain yarn strength, for example by employing alternative attributes for, among others, the still commonly used micronaire measure.…”

Section: Introductionmentioning

confidence: 99%

An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

et al. 2013

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Abstract. Knowing the yarn-strength performance potential of cotton fibre is advantageous to spinners during mill preparation, and to researchers developing new genotypes and management strategies to produce better fibre. Standard High Volume Instrument (HVI) fibre quality attributes include micronaire (a combined measure of fibre linear density and maturity) and bundle tensile properties. While these attributes relate well to yarn strength, alternative fibre quality attributes may better explain the variation in yarn strength. Two field experiments over two seasons were conducted to assess the fibre and yarn performance of some Australian cotton genotypes. The aim was to assess and compare alternative measures for micronaire, and to compare bundle and single-fibre tensile measurements, and assess the relative yarn-strength predictive performance of these attributes. Specific fibre measurement comparisons were for linear density (double-compression Fineness Maturity Tester (FMT) and gravimetric), maturity ratio (FMT, polarised light, calculated, and cross-sectional), and tensile properties (HVI bundle and Favimat Robot single fibre). Multiple linear regression models for yarn strength that included yarn manufacturing variables and standard HVI fibre quality parameters performed well (standard error of prediction (SEP) 2.40 cN tex -1 ). Multiple linear regression models performed better when alternatives to micronaire were used, e.g. using gravimetric linear density (SEP, 2.15 cN tex -1 ) or laser photometric determined ribbon width (SEP 1.71 cN tex -1 ). Yarn strength models were also better when single fibre tensile properties were substituted for bundle tensile properties (SEP 1.07 cN tex -1 ). The substitution of alternative fineness variables for micronaire or single-fibre strength for bundle strength in a simple fibre quality index also improved the prediction of yarn strength.

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Section: Introductionmentioning

confidence: 99%

An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

et al. 2013

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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%

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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“…According to Majumdar and Majumdar the impact of fiber length on yarn breaking elongation is listed well behind fiber elongation, length Uniformity index, yellowness, yarn count, reflectance, fiber strength and micronaire. While the effect of different fiber parameters and machine factors on yarn breaking elongation keep on varying from author to author [12][13][14], it is worthy noting that previous researchers [1,9,10,[12][13][14] have used at most eight inputs for the reported yarn breaking elongation prediction models. This could have limited the study of the factors affecting yarn breaking elongation.…”

Section: Factors Affecting Yarn Breaking Elongationmentioning

confidence: 99%

Performance of neural network algorithms during the prediction of yarn breaking elongation

2008

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Yarn breaking elongation is one of the most important yarn quality characteristics, since it affects the manufacture and usability of woven and knitted fabrics. One of the methods used to predict the breaking elongation of ring spun yarn is artificial neural network (NN). The design of an NN involves the choice of several parameters which include the network architecture, number of hidden layers, number of neurons in the hidden layers, training, learning and transfer functions. This paper endeavors to study the performance of NN as the design factors are varied during the prediction of cotton ring spun yarn breaking elongation. A study of the relative importance of the input parameters was also undertaken. The results indicated that there is a significant difference in the types of transfer and training functions used. Of the two transfer functions used, purelin performed far much better than logsig function. Among the five training functions, the best training functions in terms of performance was Levenberg-Marquardt. The study of the relative importance of input factors revealed that yarn twist, yarn count, fiber elongation, length, length uniformity and spindle speed, were the six most influential factors.

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A Method of Predicting the Strength and Breaking Strain of Cotton Yarn

Cited by 36 publications

References 5 publications

An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

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

Performance of neural network algorithms during the prediction of yarn breaking elongation

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