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Three Dimensional Natural Frequency Analysis of Sandwich Plates with Functionally Graded Core Using Hybrid Meshless Local Petrov-Galerkin Method and Artificial Neural Network

Foad Nazari1, Mohammad Hossein Abolbashari1,2, Seyed Mahmoud Hosseini3
Mechanical Engineering Department, Lean Production Engineering Research Center, Ferdowsi University of Mashhad, PO Box 91775-1111, Mashhad, Iran.
Corresponding author. Tel: +98-51-38805004; Fax: +98-51-38763304; E-mail: abolbash@um.ac.ir
Industrial Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, PO Box 91775-1111, Mashhad, Iran.

Computer Modeling in Engineering & Sciences 2015, 105(4), 271-299. https://doi.org/10.3970/cmes.2015.105.271

Abstract

Present study is concerned with three dimensional natural frequency analysis of functionally graded sandwich rectangular plates using Meshless Local Petrov-Galerkin (MLPG) method and Artificial Neural Networks (ANNs).The plate consists of two homogeneous face sheets and a power-law FGM core. Natural frequencies of the plate are obtained by 3D MLPG method and are verified with available references. Convergence study of the first four natural frequencies for different node numbers is the next step. Also, effects of two parameters of “FG core to plate thickness ratio” and “volume fraction index” on natural frequencies of plate are investigated. Then, four distinct ANNs are used to predict the first four natural frequencies of the plate. Back-Error Propagation (BEP) method is used to train the ANNs. The predicted data shows a good agreement with respect to the actual data. Finally, the trained ANNs are used for prediction of natural frequencies of some conditions where MLPG data are not available.

Keywords

Natural Frequency, Sandwich Plate with FG Core, Hybrid MLPG & ANN.

Cite This Article

Nazari, F., Abolbashari, M. H., Hosseini, S. M. (2015). Three Dimensional Natural Frequency Analysis of Sandwich Plates with Functionally Graded Core Using Hybrid Meshless Local Petrov-Galerkin Method and Artificial Neural Network. CMES-Computer Modeling in Engineering & Sciences, 105(4), 271–299.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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