Fibers and Polymers

, Volume 13, Issue 1, pp 87–95 | Cite as

Predicting properties of single jersey fabrics using regression and artificial neural network models

  • Pelin Gurkan Unal
  • Mustafa Erdem Üreyen
  • Diren Mecit


In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties were selected as independent variables together with wales’ and courses’ number per square centimeter. Firstly conventional ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35) and three different twist multipliers (α e 3.8, α e 4.2, and α e 4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully. However, ANN models exhibit higher predictive power than the regression models.


Artificial neural network Knitted fabric Regression analysis Ring yarn Cotton Textile 


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Copyright information

© The Korean Fiber Society and Springer Netherlands 2012

Authors and Affiliations

  • Pelin Gurkan Unal
    • 1
  • Mustafa Erdem Üreyen
    • 2
  • Diren Mecit
    • 3
  1. 1.Department of Textile EngineeringNamık Kemal UniversityTekirdagTurkey
  2. 2.School of Industrial ArtsAnadolu UniversityEskişehirTurkey
  3. 3.MARTUR A.S. R&D CenterBursaTurkey

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