Journal Article

·2016 OPEN ACCESS

Artificial Neural Networks Study on Prediction of Dielectric Permittivity of Basalt/PANI Composites

Önder EYECİOĞLU YTU , Mehmet Kılıç YTU , Yaşar Karabul YTU , Ümit Alkan YTU , Orhan İçelli YTU

International Journal of Engineering Technologies IJET

Abstract

In the present study, the dielectric permittivity change of basalt (two type basalt; CM-1, KYZ-13) reinforced PANI composites were studied to determine the effects of PANI additivities (10.0, 25.0, 50.0 wt.%) at several frequencies from 100 Hz to 17.5 MHz by a dielectric spectroscopy method at the room temperature and artificial neural networks (ANNs) simulation. Also, the dielectric permittivity at 30.0 wt.% of PANI additivity was obtained by ANNs without experimental process. That process, a significant predictive instrument was produced which allows optimization of dielectric properties for numerous composites without substantial experimentation. It has been observed that PANI additivities decreased to dielectric constant of composites at low frequencies. Furthermore, the ANNs method have satisfactory accuracy for prediction of dielectric parameters.

Keywords

Composite material Dielectric Materials science Permittivity Dielectric permittivity Artificial neural network Basalt Computer science Machine learning Geology

Subject Areas

Conducting polymers and applications ·Polymers and Plastics ·Physical Sciences
Dielectric materials and actuators ·Biomedical Engineering ·Physical Sciences
Ferroelectric and Piezoelectric Materials ·Materials Chemistry ·Physical Sciences

Citations by Year