A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models
JOURNAL OF THE TEXTILE INSTITUTE, cilt.109, sa.4, ss.560-568, 2018 (SCI-Expanded, Scopus)
- 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.