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

Year 2016, , 42 - 48, 22.06.2016

Onder Eyecioglu Yasar Karabul Umit Alkan Mehmet Kilic Orhan Icelli

https://doi.org/10.19072/ijet.27769

Cited By: 3

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

Artificial neural networks, dielectric permittivity, basalt, PANI, composite

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