A new method for the determination of a crimping degree of textured PES filament yarns

Stepanović, Jovana; Trajković, Dušan; Ćirković, Nenad; Radmanovac, Nataša

doi:10.5937/savteh1801069s

Cited by 1 publication

(1 citation statement)

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“…Furthermore, this study employed regression analysis to predict breaking force values and identify the influential parameters. Another investigation by the same researchers centered on PES multifilament yarn, where they analyzed yarn shrinkage by considering the force-elongation function and manipulating the first heater temperature and texturing speed (Stepanović et al ., 2018).…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%