Journal of Vibration Testing and System Dynamics
Neural Network for SurfaceProfile Estimation of Atomic Force Microscope
Journal of Vibration Testing and System Dynamics 5(1) (2021) 117  DOI:10.5890/JVTSD.2021.03.001
Nyesunthi Apiwattanalunggarn
Department of Mechanical Engineering, Kasetsart University, 50 Phaholyothin Road, Jatujak, Bangkok
10900, Thailand
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Abstract
This paper describes a methodology for implementing an identification algorithm for an atomic force microscope to estimate a surface profile of a sample. A microbeam of the atomic force microscope is modeled as an EulerBernoulli beam. A tipsample interaction force used here is a piecewise function described by the attractive van der Waals force and the repulsive DerjaguinMullerToporov force which can represent the indentation made by a tip of the microbeam into the sample surface. The identification of the instantaneous gap between the tip of the undeformed microbeam and the sample surface is implemented on line based on a trained inputoutput recurrent network. With the measured cantileveredbeamtip motion supplied to the recurrent network, the recurrent network is going to compute the estimated gap. The approach being used in this study is in contrast to the conventional approach in that it is based on the signal in the time domain instead of in the frequency domain; therefore, the number of steps involved is fewer. When comparing the proposed approach with the adaptivetracking controllers based on adaptive control Lyapunov functions, the proposed approach outperforms the adaptivetracking controllers significantly. Whereas the adaptivetracking controllers require much more measured signals to supply to the controllers and the parameter estimator. Instead, the proposed approach has to store two units in the tappeddelayline memory while the identification algorithm is running.
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