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

• Autorzy: Karabay Gulseren , Senol Yavuz , Ozturk Hasan , Mesegul Cansu

Identyfikatory

DOI

10.2478/ftee-2022-0019

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

waterproof; seam; sewing thread; fusible thread; artificial neural network; ANN

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2022
• Tom: Vol. 30, no. 3
• Strony: 27--32
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Karabay Gulseren

• Dokuz Eylul University, Faculty of Engineering, Department of Textile Engineering, Turkey

• https://orcid.org/0000-0002-1231-2816

autor

Senol Yavuz

• Dokuz Eylul University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Turkey

• https://orcid.org/0000-0002-3686-5597

autor

Ozturk Hasan

• Dokuz Eylul University, Faculty of Engineering, Department of Mechanical Engineering, Turkey

• https://orcid.org/0000-0002-8308-8428

autor

Mesegul Cansu

• Dokuz Eylul University, Graduate School of Natural and Applied Sciences, Turkey

Bibliografia

• 1. Jakubčionienė, Ž, Masteikaitė, V., Kleveckas, T., Jakubčionis, M., Kelesova, U. Investigation of the Strength of Textile Bonded Seams Materials Science (Medžiagotyra) 18(2) 2012: pp. 172 – 176. https://doi.org/10.5755/j01.ms.18.2.1922
• 2. Grineviciute, D., Valaseviciute, L., Narviliene, V., Dubinskaite, K., Abelkiene, R. Investigation of Sealed Seams Properties of Moisture Barrier Layer in Firefighters Clothing Materials Science (Medžiagotyra) 20 (2) 2014: pp. 198 – 204. https://doi.org/10.5755/j01.ms.20.2.3396
• 3. Vlad, L., Stan, M., Buhai, C. The Optimization of the Assemblies Applied to Products Made of Waterproof Fabrics Tekstil ve Konfeksiyon 23 (2) 2013: pp. 273 – 279.
• 4. Jevsnik, S., Eryuruk, S.H., Kalaoglu, F., Karaguzel Kayaoglu, B., Komarkova, P., Golombikova, V., Stjepanovic, Z. Seam Properties of Ultrasonic Welded Multilayered Textile Material Journal of Industrial Textiles 46 (5) 2017: pp. 1193 – 1211. https://doi.org/10.1177/1528083715613632
• 5. Trang T.T., Thao P.T. (2020) Study on the Optimal Sealing Technological Regime for Making Sport Wears from Waterproof Fabric. In: Parinov I., Chang SH., Long B. (eds) Advanced Materials. Springer Proceedings in Materials, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-45120-2_13
• 6. Mesegul, C., Karabay, G. Usage of Fusible Sewing Threads to Improve the Waterproof Property of Seams, Materials Science (Medžiagotyra). 26(4) 2020: pp. 498-504. http://dx.doi.org/10.5755/j01.ms.26.4.24147
• 7. Mesegul C., Examining the seam performance of waterproof clothing, Dokuz Eylul University Graduate School of Natural and Applied Sciences, Master’s thesis, 2019
• 8. Kalkanci M, Kurumer G, Öztürk H, Sinecen M, Kayacan Ö. Artificial Neural Network System for Prediction of Dimensional Properties of Cloth in Garment Manufacturing: Case Study on a T-Shirt. 135 FIBRES & TEXTILES in Eastern Europe 2017; 25, 4(124): 135-140. DOI: 10.5604/01.3001.0010.2859
• 9. Matusiak M. Application of Artificial Neural Networks to Predict the Air Permeability of Woven Fabrics. 41 FIBRES & TEXTILES in Eastern Europe 2015; 23, 1(109): 41-48.
• 10. Namsoon K, Hwa Kyung S, Sungmin K, and Wolhee D. An Effective Research Method to Predict Human Body Type Using an Artificial Neural Network and a Discriminant Analysis. Fibers and Polymers 2018, Vol.19, No.8, 1781-1789. DOI 10.1007/s12221-018-7901-0
• 11. Rolich, T., Šajatović, A.H. & Pavlinić, D.Z. Application of artificial neural network (ANN) for prediction of fabrics’ extensibility. Fibers Polym 11, 917–923 (2010). https://doi.org/10.1007/s12221-010-0917-8
• 12. Kanat, Z.E., Ozdil, N.,(2018) Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content, The Journal of The Textile Institute, 109:9, 1247-1253, DOI: 10.1080/00405000.2017.1423003
• 13. Bhattacharjee D, Kothari VK. A Neural Network System for Prediction of Thermal Resistance of Textile Fabrics. Textile Research Journal. 2007; 77(1):4-12. DOI:10.1177/0040517506070065
• 14. Dursun, M, Senol, Y, Bulgun E., Akkan T., Neural network based thermal protective performance prediction of three-layered fabrics for firefighter clothing. Ind Text 2019; 70: 57–64. DOI: 10.35530/IT.070.01.1527
• 15. MATLAB User’s Guide, Neural network toolbox, The Math Works Inc.