Predicting properties of single jersey fabrics using regression and artificial neural network models

GÜRKAN ÜNAL P., ÜREYEN M. E., Mecit D.

FIBERS AND POLYMERS, cilt.13, sa.1, ss.87-95, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 1
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s12221-012-0087-y
  • Dergi Adı: FIBERS AND POLYMERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.87-95
  • Anahtar Kelimeler: Artificial neural network, Knitted fabric, Regression analysis, Ring yarn, Cotton, Textile, SPUN YARN PROPERTIES, KNITTED FABRICS, FIBER PROPERTIES, DEFECTS, SYSTEM
  • Anadolu Üniversitesi Adresli: Evet

Özet

In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties were selected as independent variables together with wales' and courses' number per square centimeter. Firstly conventional ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35) and three different twist multipliers (alpha (e) 3.8, alpha (e) 4.2, and alpha (e) 4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully. However, ANN models exhibit higher predictive power than the regression models.