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Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques

Predicting compression and surfaces properties of knits using fuzzy logic and neural networks... Purpose – The purpose of this paper is to model the relationship between manufacturing parameters, especially finishing treatments and instrumental tactile properties measured by Kawabata evaluation system. Design/methodology/approach – Two soft computing approaches, namely artificial neural network (ANN) and fuzzy inference system (FIS), have been applied to predict the compression and surface properties of knitted fabrics from finishing process. The prediction accuracy of these models was evaluated using both the root mean square error and mean relative percent error. Findings – The results revealed the model's ability to predict instrumental tactile parameters based on the finishing treatments. The comparison of the prediction performances of both techniques showed that fuzzy models are slightly more powerful than neural models. Originality/value – This study provides contribution in industrial products engineering, with minimal number of experiments and short cycles of product design. In fact, models based on intelligent techniques, namely FIS and ANNs, were developed for predicting instrumental tactile characteristics in reference to finishing treatments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Clothing Science and Technology Emerald Publishing

Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0955-6222
DOI
10.1108/09556221111166239
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to model the relationship between manufacturing parameters, especially finishing treatments and instrumental tactile properties measured by Kawabata evaluation system. Design/methodology/approach – Two soft computing approaches, namely artificial neural network (ANN) and fuzzy inference system (FIS), have been applied to predict the compression and surface properties of knitted fabrics from finishing process. The prediction accuracy of these models was evaluated using both the root mean square error and mean relative percent error. Findings – The results revealed the model's ability to predict instrumental tactile parameters based on the finishing treatments. The comparison of the prediction performances of both techniques showed that fuzzy models are slightly more powerful than neural models. Originality/value – This study provides contribution in industrial products engineering, with minimal number of experiments and short cycles of product design. In fact, models based on intelligent techniques, namely FIS and ANNs, were developed for predicting instrumental tactile characteristics in reference to finishing treatments.

Journal

International Journal of Clothing Science and TechnologyEmerald Publishing

Published: Oct 4, 2011

Keywords: Textile industry; Fuzzy inference; Neural networks; Finishing treatments; Knitted fabric; Tactile properties; KES‐F; Surface properties of materials

References