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dc.contributor.authorSingh, R P-
dc.contributor.authorSingh, Sukhmander-
dc.contributor.authorGill, Reenu-
dc.contributor.authorKumar, Rishi-
dc.contributor.authorSharma, Pradeep-
dc.contributor.authorKumar, Gurupal-
dc.contributor.authorLuyt, Adriaan S-
dc.identifier.issn0975-0959 (Online); 0301-1208 (Print)-
dc.description.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.en_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJPAP Vol.58(06) [June 2020]en_US
dc.subjectEffective electrical conductivityen_US
dc.subjectArtificial neural networken_US
dc.subjectTraining functionsen_US
dc.subjectVolume fractionen_US
dc.titleComputational studies for the effective electrical conductivity of Copper powder filled LDPE/LLDPE compositesen_US
Appears in Collections:IJPAP Vol.58(06) [June 2020]

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