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

Üreyen, Mustafa Erdem; Gürkan, Pelin

doi:10.1007/s12221-008-0015-3

Cited by 36 publications

(21 citation statements)

References 8 publications

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“…The hairiness of yarn is influenced mainly by the micronaire of fibers, yarn count, and short-fiber index. Similar results for these effects on yarn properties were obtained by other researchers (Strumillo et al, 2007;Ureyen & Gürkan, 2008a, 2008bUreyen & Kadoglu, 2006). Among various properties, roving unevenness has the greatest effect on yarn imperfections.…”

Section: Effect Of Raw Materials and Machine Factorssupporting

confidence: 88%

“…It is notable that we added yarn imperfection as a dependent new variable and obtained the optimal model for this important property. Most researchers considered only four yarn properties such as tenacity, breaking elongation, hairiness, and evenness of yarn (Majumdar & Majumdar, 2004;Majumdar et al, 2005;Ureyen & Gürkan, 2008a, 2008bUreyen & Kadoglu, 2006). As it might be expected, yarn strength is highly influenced by fiber strength and yarn count.…”

Section: Effect Of Raw Materials and Machine Factorsmentioning

confidence: 99%

“…It is mentioned that, in this case, we must attend to spinning operations such as machinery line, adjustment of machines, and adjustment of auto-levelers. Our emphasis was to quantitatively evaluate the effect of the various properties (the value of effect), whereas most researchers have previously found the order of importance of properties (Majumdar & Majumdar, 2004;Majumdar et al, 2005;Ureyen & Gürkan, 2008a, 2008bUreyen & Kadoglu, 2006). Equation (2) shows that the fiber properties can be predicted from the yarn properties.…”

Section: Effect Of Raw Materials and Machine Factorsmentioning

confidence: 99%

“…Recently, Ureyen and Gürkan (2008a) developed statistical equations for the prediction of tensile properties of 100% cotton ring-spun yarn. They investigated the prediction of hairiness and unevenness of the yarn in the second part of their study (Ureyen & Gürkan, 2008b). Also, some researchers (Babay, *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

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Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

Fattahi

Ravandi

Taheri

2011

TJTI

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This paper explains the feasibility of two-way prediction by developing direct models relating fiber to yarn and reverse models relating yarn to fiber using multivariate methods simultaneously. These models evaluate the dependencies of cotton yarn properties on fiber properties and vice versa with minimum random errors and maximum accuracy. To this end, cotton fiber properties were measured from rovings carefully untwisted. An HVI system and an evenness tester of premier were used to measure the various properties. The samples of cotton yarns (108 samples) produced yarn counts ranging from 16 to 32 Ne with optimum twist factor. In this study, effective variables were selected by multivariate statistical test ( m -test). Then, multivariate analysis of variance (MANOVA) was used for evaluating the significance of obtained models. Next, the optimal separate equations were determined through multivariate multiple regression. After solving the linear equation system, a reverse model was achieved. By selecting fiber properties and machine factors as appropriate variables, the relative importance of these factors was also investigated. The results showed that the obtained equations were significant at the significance level α = 0.01.Keywords: multivariate test ( m -test); multivariate analysis of variance (MANOVA); multivariate multiple regression; cotton spinning; quality properties of yarn; cotton fiber properties IntroductionPredicting the qualitative characteristics of yarns, such as tensile properties, unevenness, and hairiness, from the raw material properties (fiber properties) has been the main purpose of many studies in recent decades. Two main approaches used in these studies are statistical and mathematical approaches. Statistical models have relatively higher predictive power than mathematical models. One of the most common statistical approaches is the multiple regression method. So far, the statistical models for the prediction of cotton yarn properties from fiber properties have been established by

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Section: Effect Of Raw Materials and Machine Factorssupporting

confidence: 88%

Section: Effect Of Raw Materials and Machine Factorsmentioning

confidence: 99%

Section: Effect Of Raw Materials and Machine Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

Fattahi

Ravandi

Taheri

2011

TJTI

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“…ANNs have been used successfully as a predicting tool in all areas of textiles from fibre to complex composites. In textile spinning domain ANNs have been used to detect and classify trash particles in cotton web [6], control of draw frame sliver evenness and levelling action point from machine and material parameters [7], optimisation of spinning process at ring frame using draw frame parameters [8], the prediction of ring spun cotton yarn properties from HVI (high volume instrument) characteristics of fibres [9], comparison of ANN and regression models for yarn hairiness, evenness and tensile properties using fibre HVI properties, roving properties, yarn count and twist multiplier [10,11]. Apart from ring spinning ANN has also served as a prediction tool for other processes as well.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

Malik¹,

Farooq

Gereke

et al. 2016

Autex Research Journal

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The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

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Modeling of textile manufacturing processes using intelligent techniques: a review

Tran

et al. 2021

Int J Adv Manuf Technol

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The development of manufacturing process modeling has attracted growing attention from the textile industry as the need for quickly exploring a textile manufacturing process is increasingly costly along with the growing complexity in the process. More and more researches shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of it is in line with the procedure of textile processes from yarn to fabrics, and to garments. The analysis and discussion of the previous studies are conducted from the different applications in a different process. The factors and performance properties considered in process modeling are collected in comparison. The considerations of the previous studies in terms of inputs' relative importance, feature selection, modeling techniques, data distribution and performance estimations are analyzed and summarized. On the basis of the summaries of more than 128 related articles from the point of views of textile engineering and artificial intelligence, the limitations, challenges and future perspectives in this issue are also concluded.

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

Cited by 36 publications

References 8 publications

Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

Modeling of textile manufacturing processes using intelligent techniques: a review

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