Wyniki wyszukiwania - BazTech

1

Open-End Yarn Properties Prediction Using HVI Fibre Properties and Process Parameters

Ghanmi H., Ghith A., Benameur T.

Autex Research Journal

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2017

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Vol. 17, no. 1

6--11

EN

This article provides three models to predict rotor spun yarn characteristics which are breaking strength, breaking elongation and unevenness. These models used noncorrelated raw material characteristics and some processing parameters. For this purpose, five different cotton blends were processed into rotor spun yarns having different metric numbers (Nm10, Nm15, Nm18, Nm22, Nm30 and Nm37). Each count was spun at different twist levels. Response surface method was used to estimate yarn quality characteristics and to study variable effects on these characteristics. In this study, predicting models are given by the analysis of response surface after many iterations in which nonsignificant terms are excluded for more accuracy and precision. It was shown that yarn count, twist and sliver properties had considerable effects on the open-end rotor spun yarn properties. This study can help industrial application since it allows a quality management-prediction based on input variables such as fibre characteristics and process parameters.

2

Prediction of Drape Coefficient by Artificial Neural Network

Ghith A., Hamdi T., Fayala F.

Autex Research Journal

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2015

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Vol. 15, no. 4

266--274

EN

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.

3

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

Hamdi T., Ghith A., Fayala F.

Autex Research Journal

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2014

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Vol. 14, no. 1

22--27

EN

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.