Estimating Functional Relationships Between Fiber Properties and the Strength of Open-End Spun Yarns'

Ethridge, M. Dean; Towery, Jack D.; Hembree, Joel F.

doi:10.1177/004051758205200106

Cited by 25 publications

(11 citation statements)

References 3 publications

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“…This feature makes these systems very suitable for dealing with the behaviour of non-linear relationships that are common in textile engineering. Neural network models have been applied to many different areas of cotton processing and performance: yarn hairiness (Babay et al, 2004), spinning performance (Beltran et al, 2004;Pynckels et al, 1997), yarn strength (Cheng and Adams, 1995), yarn ten-sile properties (Ramesh et al, 1995) and fabric properties (Fan and Hunter, 1998), and the relationships between fibre properties and the strength of open-end spun yarns (Ethridge et al, 1982). Furthermore, Basu et al (2002) employed an artificial NN to study the relationship between yarn and fabric handling properties.…”

Section: Introductionmentioning

confidence: 99%

Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

Tran

Phillips

2007

Journal of the Textile Institute

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The torque in single-spun yarns is an inherent property of the twisting and bending of staple fibres during the formation of yarn combined with the effect of applied tension on the yarn.The consequences of yarn torque are well known and are widely observed as yarn instability, e.g., yarn rotation under tension; local snarling and entanglement at low loads, and as distortion in fabric, i.e., edge-curl and skewing in knitted fabric. In this paper, a method for predicting the yarn torque based on the radial basis function networks is presented and evaluated. This method uses a "universal approximator" based on neural network methodology to minimize noise during training of the network and to approximate the yarn torque as a function of the geometrical and physical parameters of yarns (twist, linear density) and the applied load. The current method is an integral radial basis function network-based approach suitable for textile engineering and gives very good prediction of yarn torque across a range of yarn structural parameters and test conditions.

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Section: Introductionmentioning

confidence: 99%

Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

Tran

Phillips

2007

Journal of the Textile Institute

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“…They used samples from 96 bales of Texas High Plain cotton to determine the best functional expression for the impacts of High-Volume-Instrument-measured fibre properties on the strength of OE rotor yarns. According to the results, the most important fibre properties determined were strength, fineness, and length uniformity [13]. A report of the International Textile Manufacturers Federation's International Committee on Cotton Testing Methods (ITMF, ICCTM) implied that neps, SFC, dust/trash, maturity and stickiness are influential fibre parameters in an orderly manner.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cotton Fibre Properties in Compact Yarn Spinning Processes and Investigation of Fibre and Yarn Properties

Günaydin

Soydan

Palamutçu

2018

Fibres and Textiles in Eastern Europe

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Fibre properties are influential factors for yarn properties. Cotton, whose physical properties vary depending on the cultivation region, is still a very common fibre used in the textile industry. Properties such as fibre length, fineness, strength and maturity affect yarn tensility, evenness, imperfections and hairiness. Four different 100% cotton blends were used as raw material (American cotton, Aegean cotton, Urfa cotton, Greek cotton) to be converted into 20 tex compact yarns separately. HVI parameters of each blend type starting from the bale until the 2nd drawing passage machine revealed that yarn processing stages and machinery are influential factors for fibre the properties of fibres that are produced on a spinning line. Additionally ANOVA tests supported the idea that the evenness, tensility, yarn imperfections, and hairiness parameter of yarns produced from various cotton blends were statistically different. Principal Component Analyses (PCA) and the Correlation Matrix were also applied in order to analyse the relationship between fibre properties and compact yarn properties of different blends.

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“…Some previous studies have shown the nonlinear relationship between yarn strength and fiber properties [5]. Since the relationship between yarn strength and fiber properties is nonlinear, it is very difficult to use conventional techniques such regression to predict the yarn strength.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the new methodologies for predicting the CSP strength of rotor yarn

Nurwaha¹,

Wang²

2008

Fibers Polym

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This paper provides preliminary results on the relative performance of the adaptive neuro-fuzzy system inference (ANFIS) model versus linear multiple regression method, when applied to the use of cotton fiber properties to predict spun yarn strength obtained from open-end rotor spinning. Fiber properties and yarn count are used as inputs to train the two models and the output (dependent variable) would be the count-strength-product (CSP) of the yarn. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better average prediction successful in comparison with linear multiple regression model.

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Estimating Functional Relationships Between Fiber Properties and the Strength of Open-End Spun Yarns'

Cited by 25 publications

References 3 publications

Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

Evaluation of Cotton Fibre Properties in Compact Yarn Spinning Processes and Investigation of Fibre and Yarn Properties

Comparison of the new methodologies for predicting the CSP strength of rotor yarn

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