Articles

Using neural networks to model an electromagnetic-actuated microactuator

[+] Author Affiliations
Jemmy Sutanto

Intel Corporation, Mail Stop CH5-157, 5000 West Chandler Boulevard, Chandler, Arizona 85226

Ronald Setia

Intel Corporation, Ronler Acreas 2, 2501 Northwest 229th Avenue, Hillsboro, Oregon 9712

Adam Papania

Georgia Institute of Technology, George Woodruff School of Mechanical Engineering, 771 Ferst Drive Northwest, Atlanta, Georgia 30332

Gary S. May

Georgia Institute of Technology, School of Electrical and Computer Engineering, 777 Atlantic Drive Northwest, Atlanta, Georgia 30332

Peter J. Hesketh

Georgia Institute of Technology, George Woodruff School of Mechanical Engineering, 771 Ferst Drive Northwest, Atlanta, Georgia 30332

Yves H. Berthelot

Georgia Institute of Technology, George Woodruff School of Mechanical Engineering, 771 Ferst Drive Northwest, Atlanta, Georgia 30332

J. Micro/Nanolith. MEMS MOEMS. 6(1), 013011 (March 12, 2007). doi:10.1117/1.2712864
History: Received May 12, 2006; Revised September 23, 2006; Accepted September 27, 2006; Published March 12, 2007
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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 (dmax) of the microactuator for a range of burst frequencies (fb) and input currents (Icoil), 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%.

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© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Jemmy Sutanto ; Ronald Setia ; Adam Papania ; Gary S. May ; Peter J. Hesketh, et al.
"Using neural networks to model an electromagnetic-actuated microactuator", J. Micro/Nanolith. MEMS MOEMS. 6(1), 013011 (March 12, 2007). ; http://dx.doi.org/10.1117/1.2712864


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