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Composites: Mechanics, Computations, Applications: An International Journal

Publicou 4 edições por ano

ISSN Imprimir: 2152-2057

ISSN On-line: 2152-2073

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.2 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00004 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.08 SJR: 0.153 SNIP: 0.178 CiteScore™:: 1 H-Index: 12

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EXPERIMENTAL AND NUMERICAL INVESTIGATIONS OF EFFECTIVE THERMAL CONDUCTIVITY OF LOW-DENSITY POLYETHYLENE FILLED WITH Ni AND NiO PARTICLES

Volume 3, Edição 1, 2012, pp. 79-93
DOI: 10.1615/CompMechComputApplIntJ.v3.i1.60
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RESUMO

In the present communication, we report our experimental results of Ni and NiO particles as a filler material to enhance the effective thermal conductivity (ETC) of low-density polyethylene (LDPE) composites. ETC of the present composites with varying volume fraction of fillers is measured using a 30-mm-long dual needle sensor (SH-1), which consists of two parallel needles spaced 6 mm apart. An artificial neural network (ANN) model is developed to predict ETC of these materials based on feedforward backpropagation (FFBP) networks with the training functions, i.e., Gradient descent (GD), Gradient descent with adaptive learning rate (GDA), Gradient descent with momentum (GDM), and Gradient descent with momentum and adaptive learning rate (GDX). The best outcome for the use of artificial neural network appertained to feedforward backpropagation network with different training and threshold functions, i.e., Tangent sigmoid (TANSIG) and Pure-linear (PURELIN) functions.

CITADO POR
  1. Misiura A. I., Mamunya Ye. P., Kulish M. P., Metal-Filled Epoxy Composites: Mechanical Properties and Electrical/Thermal Conductivity, Journal of Macromolecular Science, Part B, 59, 2, 2020. Crossref

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