Analysis of the suspension beam in accelerometer for stiffness constant and resonant frequency by using analytical and numerical investigation

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Date
2007
Authors
Wong, Wai Chi
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Abstract
A successful and consistent performance of micro-accelerometer which has been applied in various applications can only be achieved when the resonant frequency and the sensitivity requirement are fulfilled. In view of this, structural analysis on stiffness constant and resonant frequency for the suspension beam in accelerometer, and subsequently optimization design of accelerometer with respect to sensitivity in term of displacement against acceleration must be performed. For that reason, a two-dimensional analytical formulation derived theoretically and numerically by using finite element method (FEM) has been developed with the focus on the suspension beam of micro-accelerometer. Apart from that, a three-dimensional finite element analysis (FEA) has been simulated by using ANSYS to analyze the stiffness constant and the resonant frequency of the comb finger type capacitive micro-accelerometer. For the FEA, two types of modeling, namely the complete model and the beam model, have been used. The beam models have been discretized in two different ways, namely mapped mesh and free mesh. Six types of suspension beam have been analyzed. The prediction of stiffness constant and resonant frequency obtained using ANSYS® FEA shows slight difference compared to that obtained analytically with 0-10% difference. The stiffness constant and resonant frequency obtained using ANSYS® FEA shows a good agreement with the published results where the resonance frequency achieves zero difference and only 0.59% difference in stiffness constant. The success of the ANSYS® FEA model provides further encouragement in using the model to analyze the performance of the device for various combinations of geometry and different design in suspension beam. An optimized design of accelerometer is then obtained by using Neuro-Genetic optimization which combined the artificial neural network (ANN) and genetic algorithms (GA).
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Master
Keywords
Science physic , Accelerometer , Suspension beam
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