Using neural networks to model an electromagnetic-actuated microactuator

Jemmy Sutanto; Ronald Setia; Adam Papania; Gary Stephen May; Peter J. Hesketh; Yves H. Berthelot

doi:10.1117/1.2712864

1 January 2007 Using neural networks to model an electromagnetic-actuated microactuator

Jemmy Sutanto, Ronald Setia, Adam Papania, Gary Stephen May, Peter J. Hesketh, Yves H. Berthelot

Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 6, Issue 1, 013011 (January 2007). https://doi.org/10.1117/1.2712864

Abstract

We present the use of artificial neural networks (ANNs) to model an electromagnetic microelectromechanical system (MEMS) microactuator. It is inherently complex and time consuming to model/predict the response of an electromagnetic microactuator numerically by finite element analysis, particularly when it is actuated by a pulse of current in media with different properties (e.g., air, water, and diluted methanol). ANNs are used to model the maximum displacement (d_max) of the microactuator for a range of burst frequencies (f_b) and input currents (I_coil), as well as different mechanical designs and actuation media. The prediction errors of the ANN model in normal and pressurized air are <13 and <2%, respectively. The prediction error for the same response in water or 50% diluted methanol in water is <10%.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Jemmy Sutanto, Ronald Setia, Adam Papania, Gary Stephen May, Peter J. Hesketh, and Yves H. Berthelot "Using neural networks to model an electromagnetic-actuated microactuator," Journal of Micro/Nanolithography, MEMS, and MOEMS 6(1), 013011 (1 January 2007). https://doi.org/10.1117/1.2712864

Published: 1 January 2007

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