Shirley N Cavalcanti, Moacy P da Silva, Túlio ACS Rodrigues, Pankaj Agrawal, Gustavo F Brito, Eudésio O Vilar, Tomás JA Mélo
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引用次数: 0
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.
期刊介绍:
The Journal of Thermoplastic Composite Materials is a fully peer-reviewed international journal that publishes original research and review articles on polymers, nanocomposites, and particulate-, discontinuous-, and continuous-fiber-reinforced materials in the areas of processing, materials science, mechanics, durability, design, non destructive evaluation and manufacturing science. This journal is a member of the Committee on Publication Ethics (COPE).