Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30322
Title: An artificial neural network model for the prediction of spirality of fully relaxed single jersey fabrics
Authors: Murrells, CM
Tao, XM 
Xu, BG 
Cheng, KPS
Keywords: Artificial neural networks
Fabric spirality
Models
Multiple regression
Prediction
Twist liveliness
Issue Date: 2009
Publisher: SAGE Publications
Source: Textile research journal, 2009, v. 79, no. 3, p. 227-234 How to cite?
Journal: Textile research journal 
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.
URI: http://hdl.handle.net/10397/30322
ISSN: 0040-5175
EISSN: 1746-7748
DOI: 10.1177/0040517508094091
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