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Autex Research Journal

The Journal of Association of Universities for Textiles (AUTEX)

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IMPACT FACTOR 2016: 0.716
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2300-0929
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Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

Samander Ali Malik
  • Corresponding author
  • Department of Textile Engineering, Mehran University of Engineering & Technology, 76062 Jamshoro, Sindh, Pakistan
  • Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Assad Farooq
  • Department of Fiber and Textile Technology, University of Agriculture Faisalabad, 38000 Faisalabad, Pakistan
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  • De Gruyter OnlineGoogle Scholar
/ Thomas Gereke
  • Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany
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  • De Gruyter OnlineGoogle Scholar
/ Chokri Cherif
  • Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany
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Published Online: 2016-05-20 | DOI: https://doi.org/10.1515/aut-2015-0018

Abstract

The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Keywords: Blended yarn evenness; tenacity; model; artificial neural network; multiple linear regression; rank analysis

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About the article

Published Online: 2016-05-20

Published in Print: 2016-06-01


Citation Information: Autex Research Journal, ISSN (Online) 2300-0929, DOI: https://doi.org/10.1515/aut-2015-0018.

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© 2016 Autex Research Journal. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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