Imperial College London
Portrait Picture

DrVitoTagarielli

Faculty of Engineering, Department of Aeronautics

Reader in Mechanics of Solids
 
 
 
//

Contact

 

+44 (0)20 7594 5167v.tagarielli

 
 
//

Location

 

218City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Matos:2019:10.1016/j.scriptamat.2019.03.003,
author = {Matos, MAS and Pinho, ST and Tagarielli, VL},
doi = {10.1016/j.scriptamat.2019.03.003},
journal = {Scripta Materialia},
pages = {117--121},
title = {Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network},
url = {http://dx.doi.org/10.1016/j.scriptamat.2019.03.003},
volume = {166},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Matos,MAS
AU  - Pinho,ST
AU  - Tagarielli,VL
DO  - 10.1016/j.scriptamat.2019.03.003
EP  - 121
PY  - 2019///
SN  - 1359-6462
SP  - 117
TI  - Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network
T2  - Scripta Materialia
UR  - http://dx.doi.org/10.1016/j.scriptamat.2019.03.003
UR  - http://hdl.handle.net/10044/1/67892
VL  - 166
ER  -
Download