Relationship of Cotton Fiber Properties to Yarn Tenacity

Ramey, H. H.; Lawson, Reba; Worley, Smith

doi:10.1177/004051757704701008

Cited by 53 publications

(38 citation statements)

References 3 publications

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“…Similar anomalies, however, can be seen in the regressions quoted by other authors (Parker, 1982;Ramey, Lawson & Worley, 1977;El Hattab, El-Shaer & Samra, 1972).…”

Section: Resultssupporting

confidence: 87%

The effects of environment on the relationship between cotton fibre properties and yarn characteristics

Heerden¹,

Staden²,

Ark³

1990

South African Journal of Plant and Soil

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Cotton fibre samples from three different localities were commercially processed into 15 and 30 tex ring spun yarns and 20, 30 and 50 rotor spun yarns. Multiple regression analyses were used to relate yarn tenacity and irregularity to the different fibre characteristics for all five yarn types. Multi-collinearity amongst fibre properties suggested that micronaire and fibre fineness and maturity should not be used simultaneously in a multiple regression analysis. The high correlation between 2,5% span length and 50% span length also required that only one of these two measured fibre properties be used as an independent variable. With backward stepwise regression analysis highest R2 values were realized for the samples from Loskop. A larger percentage of the variation in yarn tenacity and irregularity was explained by the independent X-variables in the case of Loskop samples than was the case with samples from Rustenburg or Vaalhartz. Fibre maturity and fibre fineness gave better results as independent variables than micronaire units. Breeders should be able to obtain maturity and fineness evaluations on a routine basis.Katoenvesel monsters afkomstig vanaf drie verskillende lokaliteite is kommersieel gespin in 15 en 30 tex ring spin garings en 20, 30 en 50 tex rotor spin garings. Meervoudige regressie is gebruik om die verwantskap tussen veseleienskappe en garing taaiheid en garing egaalheid vir die vyf garing tipes te bepaaL Multikollineariteit tussen veseleienskappe het aangedui dat mikronaire waardes en veselrypheid en fynheid verkieslik nie gelyktydig in 'n meervoudige regressie analise as onafhanklike veranderlikes gebruik moet word nie. Die hoe korrelasie tussen 2,5% spanlengte en 50% spanlengte het ook vereis dat slegs een van die twee vesel karaktereienskappe as 'n onafhanklike veranderlike gebruik word. Terugwaartse stapsgewyse regressie het aangetoon dat van die drie lokaliteite betrokke die hoogste R2-waardes verkry is met monsters vanaf Loskop. 'n Groter persentasie van die variasie in garing taaiheid en egaalheid is deur die onafhanklike veranderlikes verduidelik in die geval van Loskop monsters as wat die geval met Rustenburg of Vaalhartz monsters was. Veselrypheid en veselfynheid bepalings in plaas van blote mikronaire bepalings het as onafhanklike veranderlikes by hierdie studie beter resultate gegee. Telers behoort in staat te wees om informasie oor rypheid en fynheid van vesels op roetine basis te kan bekom.

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“…Similar anomalies, however, can be seen in the regressions quoted by other authors (Parker, 1982;Ramey, Lawson & Worley, 1977;El Hattab, El-Shaer & Samra, 1972).…”

Section: Resultssupporting

confidence: 87%

The effects of environment on the relationship between cotton fibre properties and yarn characteristics

Heerden¹,

Staden²,

Ark³

1990

South African Journal of Plant and Soil

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“…Our statistical curve estimation analysis showed that the relationship between fiber properties and yarn elongation was not very linear and so the prediction power of our regression model was not very high. Some other studies [2][3][4][5]21] have shown that there is a contrary relationship between fiber strength and fiber elongation. One possible explanation of the negative sign for fiber elongation, similar to the prediction of yarn tenacity, is that autocorrelation (between fiber strength and fiber elongation) could cause instability in the estimation coefficients.…”

Section: Influence Of Fiber Properties On Yarn Elongationmentioning

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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“…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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Relationship of Cotton Fiber Properties to Yarn Tenacity

Cited by 53 publications

References 3 publications

The effects of environment on the relationship between cotton fibre properties and yarn characteristics

The effects of environment on the relationship between cotton fibre properties and yarn characteristics

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

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

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