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Predicting worsted spinning performance with an artificial neural network model
journal contribution
posted on 2004-01-01, 00:00 authored by Rafael Beltran, Lijing Wang, Xungai WangFor a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.
History
Journal
Textile research journalVolume
74Issue
9Pagination
757 - 763Publisher
SageLocation
Thousand Oaks, Calif.Publisher DOI
ISSN
0040-5175eISSN
1746-7748Language
engNotes
The final, definitive version of this article has been published in the Journal, Textile research journal, Vol 74, Issue Number 9, 2004, © SAGE Publications Ltd, 2004 by SAGE Publications Ltd at the Textile research journal page: http://trj.sagepub.com/ on SAGE Journals Online: http://online.sagepub.com/Publication classification
C1 Refereed article in a scholarly journalCopyright notice
2004, SAGE PublicationsUsage metrics
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