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Predicting the pilling propensity of fabrics through artificial neural network modeling

Beltran, Rafael, Wang, Lijing and Wang, Xungai 2005, Predicting the pilling propensity of fabrics through artificial neural network modeling, Textile research journal, vol. 75, no. 7, pp. 557-561.

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Title Predicting the pilling propensity of fabrics through artificial neural network modeling
Author(s) Beltran, Rafael
Wang, Lijing
Wang, Xungai
Journal name Textile research journal
Volume number 75
Issue number 7
Start page 557
End page 561
Publisher Sage
Place of publication Thousand Oaks, Calif.
Publication date 2005
ISSN 0040-5175
Keyword(s) fibre science
Summary Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.
Notes The final, definitive version of this article has been published in the Journal, Textile research journal, Vol 75, Issue Number 7, 2005, © SAGE Publications Ltd, 2005 by SAGE Publications Ltd at the Textile research journal page: http://trj.sagepub.com/ on SAGE Journals Online: http://online.sagepub.com/
Language eng
Field of Research 091012 Textile Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, SAGE Publications
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003050

Document type: Journal Article
Collections: School of Engineering and Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.