Abstract

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.

Highlights

  • License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

  • The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS

  • In this research radial basis function (RBF) neural network has been used for modeling the pull-in instability voltage of microcantilever beams

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Summary

Preliminaries

In the modified couple stress theory, the strain energy density u for a linear elastic isotropic material in infinitesimal deformation is written as [17]. It is noted that parameter z represents the selected through preliminary calculations carried out on distance of a point on the section with respect to the axis instability pull-in voltage of microbeam. By neglecting Poisson’s effect, substitution of (8) into (2) gives the following expressions for the main components of the symmetric part and Qh are the higher-order resultants in a section, caused by higher-order stresses acting on the section. These two higher-order resultants are work conjugate to εxx = ∂u/∂x + 1/2(∂w/∂x)2and ∂2w/∂x2, respectively. The normalized nonlinear governing equation of motion of the beam can be written as [21]

Overview of Neural Networks
A Figure 5
D Linear
A Figure 14
E References
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