Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models

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Date

2014

Journal Title

Journal ISSN

Volume Title

Type

Article

Publisher

TAYLOR & FRANCIS LTD

Series Info

JOURNAL OF THE TEXTILE INSTITUTE;Volume: 105 Issue: 11 Pages: 1221-1229

Abstract

Recently, core-spun yarns showed many improved characteristics. The tensile properties of such yarns are accepted as one of the most important parameters for assessment of yarn quality. The tensile properties decide the performance of post-spinning operations; warping, weaving, and knitting, and the properties of the final textile product; hence, its accurate prediction carries much importance in industrial applications. In this study, artificial neural network (ANN) and multiple regression methods for modeling the tensile properties of cotton/spandex core-spun yarns are investigated. Yarn breaking strength, breaking elongation, and work of rupture of the core-spun yarns are studied. The two models were assessed by verifying root mean square error, mean bias error, and coefficient of determination (R-2-value). The results of this study revealed that ANN has better performance in predicting comparing with multiple linear regression.

Description

Accession Number: WOS:000340152600012

Keywords

October University for University for neural networks, back-propagation, core-spun yarn, spandex, regression methods, tensile properties

Citation

Cited References in Web of Science Core Collection: 40