期刊
TEXTILE RESEARCH JOURNAL
卷 79, 期 8, 页码 714-720出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0040517508094171
关键词
Hairiness Prediction; Worsted Wool Yarns; Spinning; Artifical Neural Network; Top Specification
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.
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