Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 4 October 2011
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.
Keywords
Citation
El‐Ghezal Jeguirim, S., Sahnoun, M., Babay Dhouib, A., Cheickrouhou, M., Schacher, L. and Adolphe, D. (2011), "Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques", International Journal of Clothing Science and Technology, Vol. 23 No. 5, pp. 294-309. https://doi.org/10.1108/09556221111166239
Publisher
:Emerald Group Publishing Limited
Copyright © 2011, Emerald Group Publishing Limited