<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through artificial neural networks (ANN). Fusible threads were used to obtain waterproof seams for the first time. Therefore, estimating the value of the waterproofness variable with the help of models created from test values can contribute to accelerating the progress of further studies. Hence, ten different samples were prepared for two fabrics, and the waterproofness values of the seams obtained were tested using a Textest FX 3000 Hydrostatic Head Tester III. For the prediction of waterproofness values of the seams, the Levenberg-Marquardt backpropagation algorithm was used for artificial neural network pattern models with sigmoid and positive linear transfer functions. Finally, the ANN model was successful in estimating the waterproofness of the seams. The highest correlation coefficient was R = 0.95081 which indicated that the prediction made by the neural network model proved to be reliable.</p>
</abstract>

The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through artificial neural networks (ANN). Fusible threads were used to obtain waterproof seams for the first time. Therefore, estimating the value of the waterproofness variable with the help of models created from test values can contribute to accelerating the progress of further studies. Hence, ten different samples were prepared for two fabrics, and the waterproofness values of the seams obtained were tested using a Textest FX 3000 Hydrostatic Head Tester III. For the prediction of waterproofness values of the seams, the Levenberg-Marquardt backpropagation algorithm was used for artificial neural network pattern models with sigmoid and positive linear transfer functions. Finally, the ANN model was successful in estimating the waterproofness of the seams. The highest correlation coefficient was R = 0.95081 which indicated that the prediction made by the neural network model proved to be reliable.

Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads

Faculty of Engineering, Department of Textile Engineering

Faculty of Engineering, Department of Electrical and Electronics Engineering

Faculty of Engineering, Department of Mechanical Engineering

Graduate School of Natural and Applied Sciences

Fibres & Textiles in Eastern Europe

This work is licensed under the Creative Commons Attribution 3.0 Public License.

The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through...

Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads

Article Category: Research Article

Online veröffentlicht: 23. Aug. 2022

Seitenbereich: 27 - 32

DOI: https://doi.org/10.2478/ftee-2022-0019

Schlüsselwörter
waterproof, seams, sewing threads, fusible threads, artificial neural network (ANN)

© 2022 Gulseren Karabay et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads

Article Category: Research Article

Online veröffentlicht: 23. Aug. 2022

Seitenbereich: 27 - 32

DOI: https://doi.org/10.2478/ftee-2022-0019

Schlüsselwörterwaterproof, seams, sewing threads, fusible threads, artificial neural network (ANN)

© 2022 Gulseren Karabay et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Schlüsselwörter
waterproof, seams, sewing threads, fusible threads, artificial neural network (ANN)