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

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

Open Access
|Aug 2022

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.

DOI: https://doi.org/10.2478/ftee-2022-0019 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 27 - 32
Published on: Aug 23, 2022
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2022 Gulseren Karabay, Yavuz Senol, Hasan Ozturk, Cansu Mesegul, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution 3.0 License.