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Use of Artificial Neural Networks for Modelling of Seam Strength and Elongation at Break

Research and development


  • Yildiz Zehra
    Department of Textile Education, Faculty of Technical Educationn, University of Marmara, Istanbul, Turkey
  • Dal Vedat (j/w)
  • Ünal Muhammet
    Department of Electronic-Computer Education, Faculty of Technical Educationn, Marmara University, Istanbul, Turkey
  • Yildiz Kazim (j/w)

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The strength and elongation at break of a seam are very important characteristics of comfort clothing. Optimum seam strength must be durable enough to do our daily activities easily. Some parameters such as the type and count of the sewing yarn, the seam density, the size of the sewing needle, and type of stitch affecting the strength and elongation at break of the seam. In this study two kinds of fabrics (gabardine and poplin) were chosen for experiments. As sewing parameters, two different types of stitches (plain and chain stitch), five seam densities (3, 4, 5, 6 and 7 seams/cm), two kinds of sewing needles (SPI and SES), and three kinds of sewing yarns (cotton, core-spun, and PBT yarns) were used in experiments. With these materials 120 different seam variations were developed. Each sampless seam strength was tested according to the ISO 13935-1[1] standard using an Instron 4411 instrument. After the testing process, an artificial neural network model was developed to predict the seam strength and elongation at break values. The test results were applied to multi layer perceptron and radial basis function neural network modeling. These two neural network types were compared in terms of the accuracy of the modeling system. The results show that the artificial neural network model produces reliable estimates of seam strength and elongation at break (R=1, MSE=3.33E-05).


textile fabrics, artificial neural networks, seam strength, modeling.


Yildiz Z, Dal V, Ünal M, Yildiz K. Use of Artificial Neural Networks for Modelling of Seam Strength and Elongation at Break. FIBRES & TEXTILES in Eastern Europe 2013; 21, 5(101): 117-123.

Published in issue no 5 (101) / 2013, pages 117–123.


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