Analysis of Cotton Fibre Properties: A Data Mining Approach

Chakraborty, Shankar; Agarwal, Subham; Dandge, Shruti Sudhakar

doi:10.1007/s40034-018-0125-4

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…Finer cotton fibers are more susceptible to rupture, resulting in inferior spinning efficiency. It was also proved through the developed decision tree that higher values of FS, UI and maturity ratio would contribute to better SCI values (Chakraborty et al, 2018b). Using the data set of Table 1, the scatter plots depicting the influences of six cotton fiber properties on spinnability of cotton fiber and strength of the final yarn are, respectively, developed in Figures 3 and 4, which highly corroborate with the discriminant function-based observations.…”

Section: Discriminant Analysis For Spinning Consistency Indexsupporting

confidence: 71%

“…It has also been observed that the Rd of the completely mature cotton fibers is the maximum, and it has the beneficial effect on YS. Based on data mining approach, Chakraborty et al (2018b) also proved that SFC less than equal to 10%, UHML less than equal to 1.11 in. and FS greater than equal to 27.79 cN/ tex would result in higher YS (more than 14.925 g/tex).…”

Section: Discriminant Analysis For Yarn Strengthmentioning

confidence: 94%

“…On the other hand, VIKOR method ranked all the considered cotton fibers from the best to the worst. With the help of developed decision trees, Chakraborty et al (2018b) examined the influences of several physical properties of cotton fibers on their spinnability and also on yarn tenacity and unevenness. Oner et al (2018) studied the impacts of various cotton fiber properties on tenacity, elongation, unevenness and hairiness of compact yarns.…”

Section: Literature Reviewmentioning

confidence: 99%

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Discriminant analysis-based modeling of cotton fiber and yarn properties

Sarker¹,

Chakraborty

2021

RJTA

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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.

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Section: Discriminant Analysis For Spinning Consistency Indexsupporting

confidence: 71%

Section: Discriminant Analysis For Yarn Strengthmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

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Discriminant analysis-based modeling of cotton fiber and yarn properties

Sarker¹,

Chakraborty

2021

RJTA

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“…Chakraborty and his colleagues (Chakraborty et al, 2018) leveraged neural networks to identify the factors that influence the production of high-quality cotton yarn from natural fibers. They also employed dendrograms to predict the differential effects of cotton fiber properties on yarn strength and unevenness.…”

Section: Poy Type Texturizing Process Parametersmentioning

confidence: 99%

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

Sareminia,

Ghayoumian,

Haghighat

2024

IJCST

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PurposeThe 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/approachThis 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.FindingsThe 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/valueThis 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.

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“…Although these classical techniques might make sense in certain previous studies, the effectiveness and efficiency of these traditional tools would be unacceptable in the industry 4.0 era with the massive quantities of data as well as the high complexity of the textile manufacturing process. For example, the heuristic methods are time-consuming that can hardly be applied in the context of industrial practice, when the number of variables is very large, along with large change intervals [49]. By contrast, multi-agent reinforcement learning (MARL) is a machine learning approach using a relatively well understood and mathematically grounded framework of Markov decision process (MDP) based on game theory that has been broadly applied to tackle the practical multi-objective optimization problems in the industry [26,31].…”

Section: State Of Art and Contributionsmentioning

confidence: 99%

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

Tran

Thomassey

et al. 2022

Journal of Manufacturing Systems

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Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which ( -greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.

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Analysis of Cotton Fibre Properties: A Data Mining Approach

Cited by 12 publications

References 22 publications

Discriminant analysis-based modeling of cotton fiber and yarn properties

Discriminant analysis-based modeling of cotton fiber and yarn properties

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

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