An artificial neural network-based hairiness prediction model for worsted wool yarns

Khan, Zulfiqar, Lim, Allan E. K., Wang, Lijing, Wang, Xungai and Beltran, Rafael 2009, An artificial neural network-based hairiness prediction model for worsted wool yarns, Textile research journal, vol. 79, no. 8, pp. 714-720.


Title An artificial neural network-based hairiness prediction model for worsted wool yarns
Author(s) Khan, Zulfiqar
Lim, Allan E. K.
Wang, Lijing
Wang, Xungai
Beltran, Rafael
Journal name Textile research journal
Volume number 79
Issue number 8
Start page 714
End page 720
Publisher The Institute and the Foundation
Place of publication Lancaster, Pa.
Publication date 2009
ISSN 0040-5175
1746-7748
Keyword(s) hairiness prediction
worsted wool yarns
spinning
artifical neural network
top specification
wool
fibre science
Summary This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.
Language eng
Field of Research 091012 Textile Technology
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, SAGE Publications
Persistent URL http://hdl.handle.net/10536/DRO/DU:30021474

Document type: Journal Article
Collection: Centre for Material and Fibre Innovation
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