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An artificial neural network-based hairiness prediction model for worsted wool yarns
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posted on 2009-01-01, 00:00 authored by Z Khan, Allan Lim, Lijing Wang, Xungai Wang, Rafael BeltranThis 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.
History
Journal
Textile research journalVolume
79Issue
8Pagination
714 - 720Publisher
SAGE PublicationsLocation
Lancaster, Pa.Publisher DOI
ISSN
0040-5175eISSN
1746-7748Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2009, SAGE PublicationsUsage metrics
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