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Artificial Neural Network System for Prediction of Dimensional Properties of Cloth in Garment Manufacturing: Case Study on a T-Shirt

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Warianty tytułu
PL
Zastosowanie sztucznego systemu sieci neuronowych do prognozowania wymiarów w produkcji odzieży: studium przypadku na koszulce
Języki publikacji
EN
Abstrakty
EN
The purpose of the present study was to estimate dimensional measure properties of T-shirts made up of single jersey and interlock fabrics through artificial neural networks (ANN). To that end, 72 different types of T-shirts were manufactured under 2 different fabric groups, each was consisting of 2 groups: one with elastane and the other without. Each of these groups were manufactured from six different materials in three different densities through two different knitting techniques of single jersey and interlock. For estimation of dimensional changes in these T-shirts, models including feed-forward, back-propagated, the momentum learning rule and sigmoid transfer function were utilized. As a result of the present study, the ANN system was found to be successful in estimation of pattern measures of garments. The prediction of dimensional properties produced by the neural network model proved to be highly reliable (R2> 0.99).
PL
Celem pracy było oszacowanie wymiarów koszulek przy użyciu sztucznego systemu sieci neuronowych (ANN). W tym celu wyprodukowano 72 różne typy koszulek. Koszulki wyprodukowano z sześciu różnych materiałów o trzech różnych gęstościach za pomocą dwóch różnych technik dziewiarskich. W celu oszacowania zmian wymiarów koszulek wykorzystano modele oparte na sprzężeniu zwrotnym, propagowaniu wstecznym, regule uczenia się impulsów i funkcji przenoszenia sigmoidów. Na podstawie wyników badań stwierdzono, że system ANN okazał się skuteczny w szacowaniu wymiarów odzieży. Przewidywanie wymiarów uzyskanych przy wykorzystaniu modelu sieci neuronowych okazało się bardzo wiarygodne (R2> 0,99).
Rocznik
Strony
135--140
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • Pamukkale University, Buldan Vocational College, Denizli, Turkey
autor
  • Dokuz Eylül University, Department of Textile Engineering, Izmir, Turkey
autor
  • Dokuz Eylül University, Department of Mechanical Engineering, Izmir, Turkey
autor
  • Adnan Menderes University, Department of Computer Engineering, Aydın, Turkey
autor
  • Dokuz Eylül University, Department of Textile Engineering, Izmir, Turkey
Bibliografia
  • 1. Kalkanci M, Kurumer G. Investigation of Dimensional Changes During Garment Production and Suggestions for Solutions. Fibres and Textiles in Eastern Europe 2015; 23, 3(111): 8-13.
  • 2. Çetinel H, Öztürk H, Çelik E, Karlik B. Wear 261, 2006; 1064–1068.
  • 3. Matusiak M. Application of Artificial Neural Networks to Predict the Air Permeability of Woven Fabrics. Fibres and Textiles in Eastern Europe 2015; 23, 1(109): 41-48.
  • 4. Bhattacharjee D, Kothari VK. A Neural Network System for Prediction of Thermal Resistance of Textile Fabrics. Textile Research Journal 2007; 77, 4-12.
  • 5. Hui CL, Lau TW, Ng SF, Chan KCC. Neural Network Prediction of Human Psychological Perceptions of Fabric Hand. Textile Research Journal 2004; 74, 375-383.
  • 6. Park SW, Hwang YG, Kang BC, Yeo SW. Applying Fuzzy Logic and Neural Networks to Total Hand Evaluation of Knitted Fabrics. Textile Research Journal 2000; 70, 675-681.
  • 7. Majumdar A. Modelling of Thermal Conductivity of Knitted Fabrics Made of Cotton-Bamboo Yarns Using Artificial Neural Network. The Journal of the Textile Institute 2011; 102(9), 752-762.
  • 8. Wong ASW, Li Y, Yeung PKW, Lee PWH. Neural Network Predictions of Human Psychological Perceptions of Clothing Sensory Comfort. Textile Research Journal, 2003; 73, 31-37.
  • 9. Kumar V, Sampath VR. Investigation on the Physical and Dimensional Properties of Single Jersey Fabrics made from Cotton Sheath–Elastomeric Core Spun. Fibres and Textiles in Eastern Europe; 2013; 21, 3(99): 73-75.
  • 10. Farooq A. Predicting the Dynamic Cohesion in Drafted Slivers at Draw Frame Using Artificial Neural Networks. Tekstil ve Konfeksiyon, 2014; 24(3).
  • 11. Murrels CM, Tao XM, Xu BG, ve Cheng KPS. An artificial neural network model for the Prediction of Spirality fully Relaxed Single Jersey Fabrics. Textile Research Journal 2009; 79(3), 227-234.
  • 12. Jianda C, Xiaojun G, Lianfu Y. Research On BP Neural Network Applied to Predict Cotton Fabric Handle. Proceedings of The Textile Institute 83 rd World Conference 2004, 1265-1268.
  • 13. Hui C-L, NG S-F, (). A New Approach for Prediction of Sewing Performance of Fabrics in Appreal Manufacturing Using Artificial Neural Networks. The Journal of Textile Institute 2005; 96, 6, 401-405.
  • 14. Witkowska B, Koszlaga J, Frydrych I. Modelling the Mechnical Properties of Cotton Fabrics Using Multilayer Perceptron Neural Network, Archtex'2005 Conference, Kraków, 2005, pp. 61-67.
  • 15. Witkowska B, Frydrych I A. Comparative Analysis of Modelling the Static Tear Strength by the Neural Networks and Statistical Model. AUTEX conference 2007, Tampere, Finland.
  • 16. Bhattacharya S. Application of Artificial Neural Networks for Prediction of Shrinkage of Single Jersey Cotton Knit Fabrics. 6th International Conference-TEXSCI 2007, 237-239, Czech Republic-Liberec.
  • 17. Saravana KT, Sampath V. An Artificial Neural Network System for Prediction of Dimensional Properties of Weft Knitted Rib Fabric. Journal of the Textile Association 2011; 71(5): 247–250.
  • 18. Saravana KT, Sampath VR. Prediction of Dimensional Properties of Weft Knitted Cardigan Fabric by Artificial Neural Network System. Journal of Industrial Textiles 2013, 42(4) 446–458.
  • 19. Zhi-Hua Hu. A hybrid System Based on Neural Network and Immuneco-evolutionary Algorithm for Garment Pattern Design Optimization. Journal of Computers 2009; 4, 11.
  • 20. Simon, H. Neural Networks, Macmillan, New Jersey, 1994
  • 21. Fausett, L. Fundamentals of Neural Networks, Prentice Hall, New Jersey, 1994
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-92639725-f735-42ed-b565-363a14c0f5ec
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