Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams.

This paper proposes a model of a doubly clamped electrically actuated microbeam, a structure frequently utilized in microelectromechanical systems (MEMS). The model investigates the effect of electrostatic forces on the deflection of the beam, based on the Euler-Bernoulli beam theory. The Galerkin technique is employed to calculate the beam's deflection, while the parallel plate capacitor model simulates the electric field. We also evaluate the performance of multi-walled carbon nanotubes (MWCNTs) in MEMS. MWCNTs are promising for MEMS applications due to their significant thermal, mechanical, and electrical properties. However, predicting the behavior of these systems is challenging due to their stiffness, parametric sensitivity, and non-linearity. Deep learning strategies for handling dynamical systems are a rapidly emerging field of research. In this paper, we employ a machine learning method, called deep neural networks (DNN), to solve the non-linear systems that arise in MEMS. The primary aim of this study is to investigate the nonlinear vibration properties of MEMS oscillators, specifically those related to nanotubes and electrically actuated microbeams, using DNN algorithms. Different optimizers are used to analyze the performance and capability of these non-linear dynamical models. Numerical simulations and graphical demonstrations are presented to verify the accuracy and efficiency of the algorithm.•The study develops a novel DNN-based model to solve non-linear systems in MEMS, particularly for oscillators with MWCNTs.•Deep learning optimizers are applied to improve the accuracy and efficiency of predicting MEMS behavior.•Numerical simulations confirm the effectiveness of the proposed methodology.