Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/54890
Title: Computational studies for the effective electrical conductivity of Copper powder filled LDPE/LLDPE composites
Authors: Singh, R P
Singh, Sukhmander
Gill, Reenu
Kumar, Rishi
Sharma, Pradeep
Kumar, Gurupal
Luyt, Adriaan S
Keywords: Effective electrical conductivity;Artificial neural network;Training functions;Volume fraction
Issue Date: Jun-2020
Publisher: NISCAIR-CSIR, India
Abstract: The 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.
Page(s): 486-493
URI: http://nopr.niscair.res.in/handle/123456789/54890
ISSN: 0975-0959 (Online); 0301-1208 (Print)
Appears in Collections:IJPAP Vol.58(06) [June 2020]

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