Research on Homogenization of Composite Materials

Zhen Yu Zhou; Qi Wen Xue

doi:10.4028/www.scientific.net/AMR.663.426

Paper Titles

Preparation and Character Measurements of AlN Films for RF Magnetron Sputtering
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Electrochemical Pre-Oxidation of Drinking Water by On-Site Electro-Generation of Hydrogen Peroxide
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Fabrication and Field Emission Properties of In₂O₃ Nanorods
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Environmentally Synthesis of Metallic Oxide Short-Nanorod with Biological Compatibility by Organic Solvent Assisted Annealing Process
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Research on Homogenization of Composite Materials
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Characterization of Rapid Thermal and Micro-Wave Annealed Germanium Thin Films Grown by E-Beam Evaporation on Glass Substrates
p.431

Influence of Doping Mn or Ce on the Electrical Properties of PSZT
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DEM Simulation of Micro-Macro Mechanical Behaviour of Granular Materials
p.441

Composite Material of MnO₂ and CNT for Supercapacitor
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 663Research on Homogenization of Composite Materials

Research on Homogenization of Composite Materials

Abstract:

A numerical model is given to identify equivalent parameters of composite materials, using BP neural network algorithm. Taking Filament-wound composite pressure vessels as the research object, finite element models are first constructed .Getting node displacements as network training samples, the mechanical parameters as output information of network for effective training, the equivalent material parameters can be obtained. The satisfactory numerical validation is given and results show that the proposed method can identify the equivalent modulus and the equivalent Poisson’s ratio of the Filament-wound composite pressure vessels with precision. The computational efficiency is improved with BP neural network.

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Periodical:

Advanced Materials Research (Volume 663)

Pages:

426-430

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.663.426

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Online since:

February 2013

Authors:

Zhen Yu Zhou, Qi Wen Xue

Keywords:

Composite Material, Equivalent, Finite Element, Neural Network (NN)

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