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Composites: Mechanics, Computations, Applications: An International Journal

Publication de 4  numéros par an

ISSN Imprimer: 2152-2057

ISSN En ligne: 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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PREDICTION OF THE MECHANICAL PROPERTIES OF COPPER POWDER-FILLED LOW-DENSITY POLYETHYLENE COMPOSITES. A COMPARISON BETWEEN THE ANN AND THEORETICAL MODELS

Volume 6, Numéro 1, 2015, pp. 53-73
DOI: 10.1615/CompMechComputApplIntJ.v6.i1.30
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RÉSUMÉ

In the present study, the mechanical properties of copper (Cu) powder-filled low-density polyethylene (LDPE) composites are predicted by using artificial neural networks (ANNs) as a function of the filler concentration. An ANN is a form of artificial intelligence, which attempts to mimic the function of the human brain and nervous system. A three-layer feedforward ANN structure was constructed and a backpropagation algorithm was used for training ANNs. The ANN models are based on a feedforward backpropagation (FFBP) network with such training functions as the Levenberg-Marquardt (LM), conjugate gradient backpropagation with Polak-Ribiere updates (CGP), Broyden, Fletcher, Goldfrab and Shanno (BFGS) quasi-Newton (BFG), one-step secant (OSS), and resilient backpropagation (RP). The volume fraction and different mechanical properties of continuous (matrix) and dispersed (filler) phases are input parameters to predict the different mechanical properties such as elongation at break, stress at break, and Young's modulus. A training algorithm for neurons and hidden layers for different feedforward backpropagation networks runs at the uniform threshold function TANSIG-PURELIN for 1000 epochs. Our ANN approach confirms that the mechanical properties of copper powder-filled LDPE composites are predicted in excellent agreement with experimental results. A comparison with other models is also made and found that the values of mechanical properties predicted by using present model are in good agreement with the reported experimental values.

CITÉ PAR
  1. Singh Sukhmander, Luyt Adriaan S., Bhoopal R. S., Yogi Sonia, Vidhani Bhavna, Estimation of Mechanical Properties of Copper Powder Filled Linear Low-Density Polyethylene Composites, Journal of Vibration Engineering & Technologies, 2022. Crossref

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