Liu Xiaoman
Department of Engineering Mechanics, Yanshan University, Qinhuangdao, China
Du Guojun
Department of Engineering Mechanics, Yanshan University, Qinhuangdao, China
Niu Xiaoxia
College of Information and Technology Engineering, Yanshan University, Qinhuangdao, China
ABSTRACT
The sandwich plate as a kind of composite materials was served in the field of aerospace, transportation, construction, packaging and so on. Sandwich plates due to the particularity of structure, applied in the accidental limit load will product damage or long time used will generate fatigue abrasion, these all make the sandwich plates whole or partial stiffness deterioration, results in early failure.In order to forecast critical load fast. The Radial basis function networks (RBFNNs) is employed as the calculate tool of the critical load for given geometric and physical parameters. Firstly using ANSYS finite element eigenvalue buckling method to analyse the square honeycomb sandwich plate buckling behavior, study on effect of honeycomb sandwich plate buckling in different size parameters and material properties. Secondly, Intelligent simulation by a large number of numerical data, All the models developed are of acceptable accuracy within the data range, considering the complexity and nonlinear of the property correlation of composite sandwich plate.The natural frequency should be optimization training in the genetic algorithms which can improve the prediction accuracy. The results show that: increased reasonably core high and increased face plate thickness and cell-wall thickness, reduced single length of cell size, selecting the fit elasticity modulus of core material will enable increased ability to resist buckling of honeycomb sandwich plates. Buckling behavior of honeycomb sandwich plates were more sensitive when change core high and face-plate thickness.
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How to cite this article
Liu Xiaoman, Du Guojun and Niu Xiaoxia, 2013. A Neural Network Methord Applied in Prediction Eigenvalue Buckling for Sandwich Plates. Information Technology Journal, 12: 8129-8134.
DOI: 10.3923/itj.2013.8129.8134
URL: https://scialert.net/abstract/?doi=itj.2013.8129.8134
DOI: 10.3923/itj.2013.8129.8134
URL: https://scialert.net/abstract/?doi=itj.2013.8129.8134
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