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

IF 3.6 4区 材料科学 Q2 MATERIALS SCIENCE, COMPOSITES
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.
应用人工神经网络作为预测聚合物复合材料导电性的工具
在这项研究中,开发了由可再生资源高密度聚乙烯(HDPE)(BioPe)与不同浓度的炭黑(CB)组成的导电聚合物复合材料(CPCs)。为了证实电导率预测技术,对人工神经网络(ANN)进行了建模和训练,以利用加工参数、填料信息和聚合物基质预测电导率。因此,获得的神经网络和建议的方法可作为基于参数变化和电导率预测的新材料开发的实验支持。因此,根据加工数据和填料浓度使用人工神经网络被证明是预测使用导电碳黑作为导电填料的 CPC 电导率的有效技术。
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来源期刊
Journal of Thermoplastic Composite Materials
Journal of Thermoplastic Composite Materials 工程技术-材料科学:复合
CiteScore
8.00
自引率
18.20%
发文量
104
审稿时长
5.9 months
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).
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