Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
WILEY
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, the detailed temperature and frequency-dependent (ac) conductivity analyzes of Polypyrrole/Edirne Kufeki Stone (PPy/EKS) composites have been realized by considering both experimental and Machine Learning (ML) algorithms predicted data. In this respect, the experimental ac conductivity data of pure PPy, PPy/5% EKS, PPy/10% EKS, and PPy/20% EKS composites between 1 Hz and 40 MHz at 296, 313, and 333 K temperatures have been used for the data set of ML. First, a benchmark study has been done for applied ML algorithms to obtain an eligible model. It is found that the Gaussian process regression (GPR) algorithm provided the best prediction performance. Since it has been observed a good conformity between the experimental and prediction data of GPR model, the ac conductivity (sigma(ac)) versus angular frequency (w) curves of the composites produced experimentally have been estimated for new temperature values, which were not treated experimentally. Then, the f(w) curves of at temperature values have been estimated by GPR which is for the EKS composites at various contributions that have not been experimentally produced. Ultimately, the GPR algorithm developed in the present work enables us to determine the optimum EKS additive percentage, working temperature, and frequency band for the PPy polymer matrix.
Açıklama
Anahtar Kelimeler
Polymer Composites, Machine Learning, Linear Regression, K-Nearest Neighbors Regressor, Gaussian Process Regression, Decision Tree Regressor, AC Conductivity
Kaynak
Journal of Applied Polymer Science
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
139
Sayı
21
Künye
Eyecioğlu, Ö. (2022). Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites. Journal of Applied Polymer Science, 139(21), 52194.
