Study of the Hairiness of Polyester-Viscose Blended Yarns. Part III - Predicting Yarn Hairiness Using an Artificial Neural Network

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Authors:

Haghighat Ezzatollah
Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran
Johari Majid Safar (j/w)
Etrati Seyed Mohammad (j/w)
Tehran Mohammad Amani (j/w)

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Abstract:

The hairiness of blended yarns is influenced by several parameters at the ring frame. For this reason, it is necessary to develop a model based on experimental evidence that includes all known processing factors. The generalised from of this model is a candidate for predicting yarn hairiness. In this paper, an artificial neural network and multiple linear regression
were used for modelling and predicting the hairiness of polyester-viscose blended yarns based on various process parameters. The models developed were assessed by applying PF/3, the Mean Square Error (MSE), and the Correlation Coefficient (R-value) between the actual and predicted yarn hairiness. The results indicated that the artificial neural network has better performance (R = 0.967) in comparison with multiple linear regression
(R = 0.878).

Tags:

polyester-viscose blended yarn, hairiness, artificial neural network, ring frame parameters.

Citation:

Haghighat, E.; Johari, M. S.; Etrati, S. M.; Tehran, M. A. Study of the Hairiness of Polyester-Viscose Blended Yarns. Part III - Predicting Yarn Hairiness Using an Artificial Neural Network. FIBRES & TEXTILES in Eastern Europe, 2012, 20, 1(90) 33-38.

Published in issue no 1 (90) / 2012, pages 33–38.

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