Author(s): Hajer Souid | Amel Babay | Mehdi Sahnoun
Journal: International Journal of Computer Technology and Applications
ISSN 2229-6093
Volume: 03;
Issue: 01;
Start page: 356;
Date: 2012;
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Keywords: Fabric | quality | desirability function | optimization | yarn | neural networks
ABSTRACT
The present paper presents a new method to estimate objective reflection of Denim fabric quality by using desirability function and neural networks. The global fabric quality was defined through one index belonging to the closed interval [0, 1]. For this reason, we have created a first algorithm that is modified when the definition of fabric quality is changed. This prediction would allow fabric producer to estimate customer’s quality satisfaction level. The present approach has conferred a good evaluation and prediction of the all-encompassing denim fabric quality. In the second stage of the study, we developed a model to predict global fabric quality from fiber, yarn, weaving parameters and finishing characteristics by using neural networks. The neural network model is accomplished by using a second algorithm based on back-propagation concept. The results have shown that the neuronal networks could predict global fabric quality of the untrained fabrics with better precision
Journal: International Journal of Computer Technology and Applications
ISSN 2229-6093
Volume: 03;
Issue: 01;
Start page: 356;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Fabric | quality | desirability function | optimization | yarn | neural networks
ABSTRACT
The present paper presents a new method to estimate objective reflection of Denim fabric quality by using desirability function and neural networks. The global fabric quality was defined through one index belonging to the closed interval [0, 1]. For this reason, we have created a first algorithm that is modified when the definition of fabric quality is changed. This prediction would allow fabric producer to estimate customer’s quality satisfaction level. The present approach has conferred a good evaluation and prediction of the all-encompassing denim fabric quality. In the second stage of the study, we developed a model to predict global fabric quality from fiber, yarn, weaving parameters and finishing characteristics by using neural networks. The neural network model is accomplished by using a second algorithm based on back-propagation concept. The results have shown that the neuronal networks could predict global fabric quality of the untrained fabrics with better precision
