Mill specific prediction of worsted yarn performance

Beltran, Rafael, Wang, Lijing and Wang, Xungai 2006, Mill specific prediction of worsted yarn performance, Journal of the textile institute, vol. 97, no. 1, pp. 11-16.

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Title Mill specific prediction of worsted yarn performance
Author(s) Beltran, Rafael
Wang, Lijing
Wang, Xungai
Journal name Journal of the textile institute
Volume number 97
Issue number 1
Start page 11
End page 16
Publisher Taylor & Francis
Place of publication Manchester, England
Publication date 2006
ISSN 0040-5000
1754-2340
Keyword(s) artificial neural network
mill specific prediction
worsted spinning performance
yarn quality
Summary Different spinning mills use different raw materials, processing methodologies, and equipment, all of which influence the quality of the yarns produced. Because of many variables, there is a difficulty in developing a universal empirical/theoretical model. This work presents a multilayer perceptron algorithm (MLP) model for the purpose of building a mill specific worsted spinning performance prediction tool. Sixteen inputs are used to predict key yarn properties and spinning performance, including number of fibers in cross-section, unevenness (U%), thin places, neps, yarn tenacity, elongation at break, thick places, and spinning ends-down. Validation of the model on mill specific commercial data set shows that the general fit to the target values is good. Importantly, the performance of the MLP shows a certain degree of stability to different, random selections of independent test data. Subsequent comparison against the predicted outputs of Sirolan Yarnspec™ confirms the overall performance of the artificial neural network (ANN) method to be more accuratefor mill specific predictions.
Language eng
Field of Research 091012 Textile Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ┬ęThe Textile Institute, 2006
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003645

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