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

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...

Full description

Saved in:
Bibliographic Details
Published in:International journal of clothing science and technology 2011-01, Vol.23 (5), p.294-309
Main Authors: El-Ghezal Jeguirim, Selsabil, Sahnoun, Mahdi, Babay Dhouib, Amal, Cheickrouhou, Morched, Schacher, Laurence, Adolphe, Dominique
Format: Article
Language:English
Subjects:
Algorithms
Clothing industry
Engineering Sciences
Finishing
Fuzzy
Fuzzy logic
Fuzzy sets
Knit goods
Learning theory
Mathematical models
Mechanical properties
Multivariate analysis
Neural networks
Other
Principal components analysis
Product development
Regression analysis
Sensory perception
Standard deviation
Studies
Surface properties
Tactile
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0955-6222
1758-5953
DOI:10.1108/09556221111166239