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Prediction of Drape Coefficient by Artificial Neural Network

100%

Ghith A. , Hamdi T. , Fayala F.

Autex Research Journal

2015

tom Vol. 15, no. 4

266--274

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.

Experimental study of capillary rise in fabrics using an electrical resistance technique

100%

Hamdaoui M. , Fayala F. , Perré P. , Ben Nasrallah S.

Autex Research Journal

2008

tom Vol. 8, no. 2

44--48

An electrical method has been used to study capillary rise in fabrics. This method is based on the measure of the electrical resistance and leads to the determination of time-space water content evolution. The obtained results allowed us to deduce the capillary pressure curve of the fabrics and the flow velocity.

A Principal Component Analysis (PCA) method for predicting the correlation between some fabric parameters and the drape

100%

Hamdi T. , Ghith A. , Fayala F.

Autex Research Journal

2014

tom Vol. 14, no. 1

22--27

A new method for predicting correlation between fabric parameters and the drape is developed. This method utilises a Principal Component Analysis (PCA) of intercorrelated influencing parameters (bending rigidity, weight, thickness) and the drape parameters (drape coefficient and the node number). This paper describes the PCA procedure and presents the similarities and contrasts between variables.

Porosity determination of jersey structure

100%

Benltoufa S. , Fayala F. , Cheikhrouhou M. , Ben Nasrallah S.

Autex Research Journal

2007

tom Vol. 7, no. 1

63--69

In our paper, we attempt to investigate methods of determining jersey porosity which is this fabric's principal physical characteristic. In fact, end use, liquid absorbency, thermal comfort and resistance are closely related to pore size and distribution. So it is important to study porosity, in order to classify and determine the right use of jersey knitted structure. Many methods are used to estimate porosity, but most concern air permeability, image processing and geometry modelling. The first mentioned is used for the stretched structure, the second is valid for fabrics with high porosity levels, and the last mentioned is used to confirm any structure's conformation. The aims of this study are twofold; firstly, to recognise the most suitable and easiest method of estimating the fabric's porosity, and secondly to study the influence on porosity of various knitting parameters of jersey structure such as yarn number and count, fabric thickness, loop length, and stitch density.