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https://hdl.handle.net/11499/9288
Title: | Artificial neural network system for prediction of dimensional properties of cloth in garment manufacturing: Case study on a T-shirt | Authors: | Kalkancı, Mihriban Kurumer, G. Öztürk, H. Sinecen, M. Kayacan, Ö. |
Keywords: | Artificial neural networks Cloth dimensional change Knitted fabric Relaxation Fibers Industrial engineering Textiles Dimensional changes Dimensional properties Garment manufacturing Momentum learning rules Neural network model Sigmoid transfer function Neural networks |
Publisher: | Institute of Biopolymers and Chemical Fibres | Abstract: | 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). © 2017, Institute of Biopolymers and Chemical Fibres. All rights reserved. | URI: | https://hdl.handle.net/11499/9288 https://doi.org/10.5604/01.3001.0010.2859 |
ISSN: | 1230-3666 |
Appears in Collections: | Buldan Meslek Yüksekokulu Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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