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Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory
Joint Authors
Heidari, Ali
Haydari, Muhammad
Homaei, Hadi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated.
Two engineering cases including cantilever and double cantilever microbeam are considered.
Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect.
By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage.
A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams.
Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam.
The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam.
Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior.
The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated.
Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam.
Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach.
The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
American Psychological Association (APA)
Haydari, Muhammad& Heidari, Ali& Homaei, Hadi. 2014. Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-481686
Modern Language Association (MLA)
Haydari, Muhammad…[et al.]. Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-481686
American Medical Association (AMA)
Haydari, Muhammad& Heidari, Ali& Homaei, Hadi. Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-481686
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-481686