Prediction of Drape Coefficient by Artificial Neural Network

An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation al...

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Bibliographic Details
Published in:AUTEX Research Journal 2015-12, Vol.15 (4), p.266-274
Main Authors: Ghith, Adel, Hamdi, Thouraya, Fayala, Faten
Format: Article
Language:English
Subjects:
back-propagation
Bending rigidity
drape coefficient
Materials science
Neural networks
Textiles
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Summary:An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.
ISSN:2300-0929
1470-9589
2300-0929
DOI:10.1515/aut-2015-0045