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

journal contribution
posted on 2009-01-01, 00:00 authored by Z Khan, Allan Lim, Lijing Wang, Xungai Wang, Rafael Beltran
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.

History

Journal

Textile research journal

Volume

79

Issue

8

Pagination

714 - 720

Publisher

SAGE Publications

Location

Lancaster, Pa.

ISSN

0040-5175

eISSN

1746-7748

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2009, SAGE Publications

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