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Microsystem Technologies

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14-09-2016 | Technical Paper

Computing the pull-in voltage of fixed–fixed micro-actuators by artificial neural network

Authors: Cevher Ak, Ali Yıldız, Ali Akdağlı, Mustafa Berkan Biçer

Published in: Microsystem Technologies | Issue 8/2017

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Abstract

In this study, a feed-forward backpropagation (FFBP) artificial neural network (ANN) model based on multilayer perceptron (MLP) for computing the pull-in voltage value of fixed–fixed micro-actuators is presented. The proposed ANN model is trained using the numerous pull-in voltage value results of clamped–clamped actuators, which have various physical properties, simulated by a software that employs the finite element method (FEM). As the presented ANN model has been trained for the simulation data, it implicitly takes into account the fringing field, mid-plane stretching and size effects. The accuracy and robustness of the results obtained by the proposed ANN model are verified by comparing with those of the theoretical ones through the simulation and experimental studies previously presented in the literature. The average percentage errors for training and testing data are found to be 0.30 and 0.40 %, respectively, while the maximum percentage error for literature data is calculated as 1.96 %. These results show that the main advantage of the presented ANN model gives satisfying pull-in voltage results over solution space of the simulations with effortless way.

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Title: Computing the pull-in voltage of fixed–fixed micro-actuators by artificial neural network
Authors: Cevher Ak
Ali Yıldız
Ali Akdağlı
Mustafa Berkan Biçer
Publication date: 14-09-2016
Publisher: Springer Berlin Heidelberg
Published in: Microsystem Technologies / Issue 8/2017
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI: https://doi.org/10.1007/s00542-016-3128-4

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