Application of artificial neural networks as a tool for the prediction of electrical conductivity in polymer composites

Abstract

In this work, conductive polymeric composites (CPCs) of renewable source high-density polyethylene (HDPE) (BioPe) with various carbon black (CB) concentrations were developed. To corroborate the electrical conductivity prediction techniques, an artificial neural network (ANN) was modeled and trained to predict electrical conductivity using processing parameters, filler information, and polymeric matrix. Thus, the obtained neural network and the proposed methodology could serve as experimental support for the development of new materials based on parametric variation and consequent prediction of electrical conductivity. Therefore, the use of artificial neural networks from processing data and filler concentration proved to be an efficient technique for predicting the electrical conductivity of CPCs using conductive carbon black as conductive filler.