The prediction of elongation and recovery of woven Bi-stretch fabric using artificial neural network and linear regression models

OGULATA S. N., Sahin C., Ogulata R. T., BALCI O.

FIBRES & TEXTILES IN EASTERN EUROPE, cilt.14, sa.2, ss.46-49, 2006 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 2
  • Basım Tarihi: 2006
  • Dergi Adı: FIBRES & TEXTILES IN EASTERN EUROPE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.46-49
  • Anahtar Kelimeler: woven fabric, elongation, recovery, artificial neural network, regression model, DEFECTS
  • Çukurova Üniversitesi Adresli: Evet

Özet

Stretch woven fabrics are widely used because of their good elongation and recovery (residual extension) properties. Several parameters and test method are used to measure the properties of these fabrics. Each different set of test parameters means a different test application. Sometimes, repeating tests for different test involves wasting time and labour. In this study, the test results were used to try and predict elongation and recovery using neural network and linear regression models. Certain test parameters such as rate of extension, gauge length (jaw separation), and maximum load were selected as input variables. The accuracies of predictions of elongation in the direction of warp and weft by both models were found to be similar and satisfactory. The predictions for the recovery test showed differences as to fabric warp and weft direction. All the statistical results indicate that predicting the fabrics' test results from an unseen data set is very good for both models.