Search results

1 – 10 of 320
Article
Publication date: 18 May 2021

Baneswar Sarker and Shankar Chakraborty

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the…

Abstract

Purpose

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

Design/methodology/approach

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Findings

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

Originality/value

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

Details

Research Journal of Textile and Apparel, vol. 26 no. 1
Type: Research Article
ISSN: 1560-6074

Keywords

Book part
Publication date: 18 January 2024

Naraindra Kistamah

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The…

Abstract

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Details

Artificial Intelligence, Engineering Systems and Sustainable Development
Type: Book
ISBN: 978-1-83753-540-8

Keywords

Article
Publication date: 20 February 2024

Saba Sareminia, Zahra Ghayoumian and Fatemeh Haghighat

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring…

Abstract

Purpose

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

Design/methodology/approach

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

Findings

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

Originality/value

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Details

International Journal of Clothing Science and Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 2 November 2015

Hanen Ghanmi, Adel Ghith and Tarek Benameur

The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a…

Abstract

Purpose

The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn.

Design/methodology/approach

The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error.

Findings

The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy.

Originality/value

Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

Details

International Journal of Clothing Science and Technology, vol. 27 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 December 2004

George K. Stylios

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects…

3517

Abstract

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Details

International Journal of Clothing Science and Technology, vol. 16 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 9 October 2007

B.K. Behera and Rajesh Mishra

The purpose of this paper is to investigate an alternative approach that can predict non‐linear relations.

Abstract

Purpose

The purpose of this paper is to investigate an alternative approach that can predict non‐linear relations.

Design/methodology/approach

An engineered approach to fabric development is described in which a radial basis function network is trained with worsted fabric constructional parameters to predict functional and aesthetic properties of fabrics. An objective method of fabric appearance evaluation with the help of digital image processing is introduced. The prediction of fabric properties by the network with changing basic fibre characteristics and fabric constructional parameters is found to have good correlation with the experimental values of fabric functional and aesthetic properties.

Findings

The radial basis function network can successfully predict the fabric functional and aesthetic properties from basic fibre characteristics and fabric constructional parameters with considerable accuracy. The network prediction is in good correlation with the actual experimental data. There is some error in predicting the fabric properties from the constructional parameters. The variation in the actual values and predicted values is because of small sample size. Moreover, the properties of worsted fabrics are greatly influenced by the finishing parameters which are not taken into consideration in the training of the network. Prediction performance can be further improved by including these parameters as input, during the training phase. In few cases, the network has predicted contradictory trends, which are found difficult to be explained.

Originality/value

The paper describes a radial basis function neural network model that can be used for the prediction of the fabric appearance values and comfort properties using fabric constructional parameters and some primary fibre mechanical properties as input parameters of the network.

Details

International Journal of Clothing Science and Technology, vol. 19 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 14 November 2008

George K. Stylios

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects…

1246

Abstract

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Details

International Journal of Clothing Science and Technology, vol. 20 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 August 2010

Abhijit Majumdar

Hairiness is a very important yarn quality parameter for high speed weaving. This paper presents a comparative analysis of two modeling methodologies for the prediction of ring…

Abstract

Hairiness is a very important yarn quality parameter for high speed weaving. This paper presents a comparative analysis of two modeling methodologies for the prediction of ring and rotor yarn hairiness. Cotton fibre properties measured by a high volume instrument (HVI) and yarn count have been used as inputs for artificial neural network (ANN) and linear regression models. The prediction accuracy for both of the models is found to be good as the correlation coefficient is higher than 0.92 and mean absolute error is less than 4%. However, ANN models have an edge over the regression model particularly for ring yarn hairiness prediction. The importance of the cotton fibre properties on yarn hairiness has also been analysed by the developed ANN and regression models. For ring spun yarns, the ranking of cotton fibre properties given by the ANN and regression models are generally in agreement although some disparities exist in the ranking of length properties. Both models yield almost identical ranking of cotton fibre properties for rotor yarn hairiness.

Details

Research Journal of Textile and Apparel, vol. 14 no. 3
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 13 November 2009

George K. Stylios

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects…

1093

Abstract

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Details

International Journal of Clothing Science and Technology, vol. 21 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 December 2002

George K. Stylios

Looks at the eighth published year of the ITCRR and the research, from far and near, involved in this. Muses on the fact that, though all the usual processes are to the fore, the…

Abstract

Looks at the eighth published year of the ITCRR and the research, from far and near, involved in this. Muses on the fact that, though all the usual processes are to the fore, the downside part of the industry is garment making which is the least developed side. Posits that the manufacture of clothing needs to become more technologically advanced as does retailing. Closes by emphasising support for the community in all its efforts.

Details

International Journal of Clothing Science and Technology, vol. 14 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

1 – 10 of 320