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TECHNICAL BRIEFS

An Approach for Model Updating of a Multiphysics MEMS Micromirror

[+] Author and Article Information
Ryan J. Link, David Zimmerman

Mechanical Engineering Department, University of Houston, Houston, TX 77204

J. Dyn. Sys., Meas., Control 129(3), 357-366 (Sep 05, 2006) (10 pages) doi:10.1115/1.2719775 History: Received May 10, 2005; Revised September 05, 2006

In this work, a general approach is formulated for updating the parameters of systems governed by multiphysics equations. The optimization technique is based on Genetic Algorithms (GAs). GAs represent a class of probabilistic optimization strategies loosely patterned after a simplified evolutionary scheme and Darwin’s “survival of the fittest” concepts. The GA is coupled to a commercially available multiphysics finite element program. In the context of this, issues of mode tracking, eigenvector comparisons, and approximate function evaluations are discussed. The approach is demonstrated on a micro-electro-mechanical (MEMS) micromirror which is governed by both structural and electrostatic physics. The MEMS mirror is characterized dynamically using a laser vibrometer. These experimental measurements are then used in the model updating of the finite element structural model and the electrostatic model. Several interesting observations that were encountered in this development will also be discussed.

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Copyright © 2007 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Coding of a three design variable problem

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Figure 2

Structural overview of the Durascan™ micromirror

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Figure 3

Several Durascans™ compared to a postage Stamp, courtesy of Applied MEMS™

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Figure 4

The first six analytical modes of the nominal micromirror model

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Figure 5

Flowchart of structural model updating procedure

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Figure 6

Description of slopes θx and θy. The slopes are defined in the same way, but along different axes (note the coordinate systems in (a) and (b)).

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Figure 7

Total % parameter change versus iteration. This graph demonstrates the reduction in parameter changes with increasing iterations.

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Figure 8

Absolute value of the fitness function versus iteration

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Figure 9

Optimal design parameters versus the weighting factor β

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Figure 10

Comparison of an experimental FRF to a nominal model’s FRF and an updated model’s FRF

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Figure 11

Comparison of an experimental FRF to the nominal model’s FRF and an updated model’s FRF at a point not used in updating

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