Comparison of high volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network
Yazarlar (2)
Yıldıray Turhan
Pamukkale Üniversitesi, Türkiye
Doç. Dr. Ozan TOPRAKÇI
Pamukkale Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Textile Research Journal | ||
| Dergi ISSN | 0040-5175 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2013 |
| Cilt / Sayı / Sayfa | 83 / 2 / 130–147 | DOI | 10.1177/0040517512445334 |
| Makale Linki | http://trj.sagepub.com/cgi/doi/10.1177/0040517512445334 | ||
| Özet |
| In this study, an artificial neural network (ANN) model is presented in order to predict the tenacity and hairiness of carded cotton yarns. Fiber measurement values generated by using a high-volume instrument (HVI) and an advanced fiber information system (AFIS) were used in the ANN model as input parameters. The radial basis function neural network (RBFNN) was used as ANN structure. The best RBFNN model was determined by analyzing the effect of epochs and the number of neurons on prediction performance. By using this ANN structure, the comparison between the performance of predicting yarn properties from HVIs and from AFISs was carried out. In the study, four different yarn counts (Ne20, Ne24, Ne30, and Ne40) for 10 different blends were applied. Each yarn count was spun at 4.34αe twist factor. In this study, the model presented a good rate of accuracy for predicting yarn tenacity and hairiness by … |
| Anahtar Kelimeler |
| Radial basis function | yarn tenacity | yarn hairiness | artificial neural networks | prediction models | fiber properties |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 13 |
| Google Scholar | 22 |