An Artificial Neural Network Model for the Prediction of Spirality of Fully Relaxed Single Jersey Fabrics

Charlotte Marion Murrells, Xiaoming Tao, Bingang Xu, Kwok po Stephen Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)

Abstract

The present paper proposes an artificial neural network model for the prediction of the degree of spirality of single jersey fabrics made from 100 % cotton conventional and modified ring spun yarns. The factors investigated were the yarn residual torque as the measured twist liveliness, yarn type, yarn linear density, fabric tightness factor, the number of feeders, rotational direction and gauge of the knitting machine and dyeing method. The artificial neural network model was compared with a multiple regression model, demonstrating that the neural network model produced superior results to predict the degree of fabric spirality after three washing and drying cycles. The relative importance of the investigated factors influencing the spirality of the fabric was also investigated.
Original languageEnglish
Pages (from-to)227-234
Number of pages8
JournalTextile Research Journal
Volume79
Issue number3
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • artificial neural networks
  • fabric spirality
  • models
  • multiple regression
  • prediction
  • twist liveliness

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Polymers and Plastics

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