A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

ERBİL Y., BABAARSLAN O., İLHAN İ.

JOURNAL OF THE TEXTILE INSTITUTE, cilt.109, sa.4, ss.560-568, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 109 Sayı: 4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1080/00405000.2017.1361164
  • Dergi Adı: JOURNAL OF THE TEXTILE INSTITUTE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.560-568
  • Anahtar Kelimeler: Ternary blends, open-end yarn, prediction, multiple linear regression, artificial neural networks, LINEAR-REGRESSION, STRENGTH, ELONGATION, FABRICS
  • Çukurova Üniversitesi Adresli: Evet

Özet

This study focused on predicting tensile properties of PES/CV/PAN blended Open-End Rotor yarns. The effective factors were fiber blend ratios (six stages from 0 to 100%), linear density (three count levels), mixing method (carding machine and drawframe), and number of passages in drawframe (one and two times) as production parameters. We performed a stepwise multiple linear regression (MLR) analysis and established an artificial neural network (ANN) model that trained with backpropagation rule as Levenberg-Marquardt. Then, we conducted a comparative analysis for both models in terms of prediction performance. As a result, ANN has given a slightly better prediction values than MLR for breaking strength but significantly better prediction values for breaking elongation.