Stochastic investigation of natural frequency for functionally graded plates
Stochastic investigation of natural frequency for functionally graded plates
This paper presents the stochastic natural frequency analysis of functionally graded plates by applying artificial neural network (ANN) approach. Latin hypercube sampling is utilised to train the ANN model. The proposed algorithm for stochastic natural frequency analysis of FGM plates is validated and verified with original finite element method and Monte Carlo simulation (MCS). The combined stochastic variation of input parameters such as, elastic modulus, shear modulus, Poisson ratio, and mass density are considered. Power law is applied to distribute the material properties across the thickness. The present ANN model reduces the sample size and computationally found efficient as compared to conventional Monte Carlo simulation.
Karsh, P. K.
b039f77d-f480-4493-b2a9-325b04e3cbaf
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Dey, S.
aa10e238-f148-4f91-b74d-b747b93f2b6a
12 March 2018
Karsh, P. K.
b039f77d-f480-4493-b2a9-325b04e3cbaf
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Dey, S.
aa10e238-f148-4f91-b74d-b747b93f2b6a
Karsh, P. K., Mukhopadhyay, T. and Dey, S.
(2018)
Stochastic investigation of natural frequency for functionally graded plates.
3rd International Conference on Mechanical and Aeronautical Engineering, ICMAE 2017, , Dubai, United Arab Emirates.
13 - 16 Dec 2017.
(doi:10.1088/1757-899X/326/1/012003).
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Abstract
This paper presents the stochastic natural frequency analysis of functionally graded plates by applying artificial neural network (ANN) approach. Latin hypercube sampling is utilised to train the ANN model. The proposed algorithm for stochastic natural frequency analysis of FGM plates is validated and verified with original finite element method and Monte Carlo simulation (MCS). The combined stochastic variation of input parameters such as, elastic modulus, shear modulus, Poisson ratio, and mass density are considered. Power law is applied to distribute the material properties across the thickness. The present ANN model reduces the sample size and computationally found efficient as compared to conventional Monte Carlo simulation.
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Published date: 12 March 2018
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© 2018 Institute of Physics Publishing. All rights reserved.
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3rd International Conference on Mechanical and Aeronautical Engineering, ICMAE 2017, , Dubai, United Arab Emirates, 2017-12-13 - 2017-12-16
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Local EPrints ID: 483560
URI: http://eprints.soton.ac.uk/id/eprint/483560
PURE UUID: fd37f0ae-be94-4ec6-a987-5fdad0055e15
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Date deposited: 01 Nov 2023 18:01
Last modified: 18 Mar 2024 04:10
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Author:
P. K. Karsh
Author:
T. Mukhopadhyay
Author:
S. Dey
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