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AuthorSingh, R. P.
AuthorSingh, S.
AuthorGill, R.
AuthorKumar, R.
AuthorSharma, P.
AuthorKumar, G.
AuthorLuyt, A. S.
Available date2022-04-17T09:47:34Z
Publication Date2020
Publication NameIndian Journal of Pure and Applied Physics
ResourceScopus
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092055997&partnerID=40&md5=15a5e254da014bda712313855bcddeeb
URIhttp://hdl.handle.net/10576/29902
AbstractThe effective electrical conductivity (EEC) of low density polyethylene (LDPE) and linear low density polyethylene (LLDPE) polymer composites filled with copper has been studied. The nonlinear behavior has been observed for effective electrical conductivity versus filler content. Several approaches have been described to predict the electrical conductivities of polymer composites. EEC is described by artificial neural network (ANN) and it demonstrates the accurate match of experimental data for EEC with different training functions (TRAINOSS, TRAINLM, TRAINBR, TRAINSCG, TRAINBFG, and TRAINRP). The ANN approach satisfied the experimental data for EEC of polymer composites reasonably well. The complex structure encountered in LDPE/Cu and LLDPE/Cu, along with the difference in the EEC of the components, make it difficult to estimate the EEC exactly. This is the reason for which artificial neural network has been employed here. By using ANN approach experimental results indicate that EEC of polymer composites increases with increasing filler content at the same concentration.
SponsorOne of the author Sukhmander Singh appreciates the financial support given by the University Grants Commission (UGC), New Delhi, for providing the startup Grant (Project No. F. 30-356/2017/BSR).
Languageen
PublisherNational Institute of Science Communication and Information Resources
SubjectArtificial neural network
Effective electrical conductivity
Training functions
Volume fraction
TitleComputational studies for the effective electrical conductivity of copper powder filled LDPE/LLDPE composites
TypeArticle
Pagination486-493
Issue Number6
Volume Number58
dc.accessType Abstract Only


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