Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks

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• 作者：Zulfiqar Ali Malik (2)
  Noman Haleem (2)
  Mumtaz Hasan Malik (2)
  Anwaruddin Tanwari (1)
  
• 关键词：Fabric strength ; Artificial neural network ; Sensitivity analysis ; Polyester cotton blend ; Modeling
• 刊名：Fibers and Polymers
• 出版年：2012
• 出版时间：October 2012
• 年：2012
• 卷：13
• 期：8
• 页码：1094-1100
• 全文大小：274KB
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• 作者单位：Zulfiqar Ali Malik (2)
  Noman Haleem (2)
  Mumtaz Hasan Malik (2)
  Anwaruddin Tanwari (1)
  
  2. Department of Yarn Manufacturing, Faculty of Engineering and Technology, National Textile University, Shiekhupura Road, Faisalabad, 37610, Pakistan
  1. Department of Textile Engineering, Mehran University of Engineering & Technology, Jamshoro, 76001, Pakistan
  
• ISSN：1875-0052

文摘

Tensile strength plays a vital role in determining the mechanical behavior of woven fabrics. In this study, two artificial neural networks have been designed to predict the warp and weft wise tensile strength of polyester cotton blended fabrics. Various process and material related parameters have been considered for selection of vital few input parameters that significantly affect fabric tensile strength. A total of 270 fabric samples are woven with varying constructions. Application of nonlinear modeling technique and appreciable volume of data sets for training, testing and validating both prediction models resulted in best fitting of data and minimization of prediction error. Sensitivity analysis has been carried out for both models to determine the contribution percentage of input parameters and evaluating the most impacting variable on fabric strength.