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Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network

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Title: Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network
Authors: Matos, MAS
Pinho, ST
Tagarielli, VL
Item Type: Journal Article
Abstract: Industrial applications of conductive polymer composites with carbon nanotubes require precise tailoring of their electrical properties. While existing theoretical methods to predict the bulk conductivity require fitting to experiments and often employ power-laws valid only in the vicinity of the percolation threshold, the accuracy of numerical methods is accompanied with substantial computational efforts. In this paper we use recently developed physically-based finite element analyses to successfully train an artificial neural network to make predictions of the bulk conductivity of carbon nanotube-polymer composites at negligible computational cost.
Issue Date: 1-Jun-2019
Date of Acceptance: 1-Mar-2019
URI: http://hdl.handle.net/10044/1/67892
DOI: https://dx.doi.org/10.1016/j.scriptamat.2019.03.003
ISSN: 1359-6462
Publisher: Elsevier BV
Start Page: 117
End Page: 121
Journal / Book Title: Scripta Materialia
Volume: 166
Copyright Statement: © 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 642890
Keywords: 0912 Materials Engineering
0913 Mechanical Engineering
Materials
Publication Status: Published
Embargo Date: 2020-03-22
Online Publication Date: 2019-03-22
Appears in Collections:Aeronautics



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