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

Research and development

Authors:

• Kalkanci Mihriban
  Buldan Vocational College, Pamukkale University, Denizli, Turkey
• Kurumer Gülseren
  Department of Textile Engineering, Faculty of Engineering, Dokuz Eylül University, Izmir, Turkey
• Öztürk Hasan
  Department of Mechanical Engineering, Dokuz Eylül University, Izmir, Turkey
• Sinecen Mahmut
  Department of Computer Engineering, Adnan Menderes University, Aydın, Turkey
• Kayacan Özlem
  Department of Textile Engineering, Faculty of Engineering, Dokuz Eylül University, Izmir, Turkey

Nr DOI: 10.5604/01.3001.0010.2859

Full text

references

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).

Tags:

cloth dimensional change, knitted fabric, relaxation, artificial neural networks.

Citation:

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. FIBRES & TEXTILES in Eastern Europe 2017; 25, 4(124): 135-140. DOI: 10.5604/01.3001.0010.2859

Published in issue no 4 (124) / 2017, pages 135–140.