ANN based optimization of nano-beam oscillations with intermolecular forces and geometric nonlinearity

In this study, we investigate the effect of Van der Waals and Casimir forces on the mathematical model of nano-electromechanical systems (NEMS) such as nano-beam actuators that contain cantilever and double cantilever beams. The singular nonlinear boundary value problem governing the beam-type actua...

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Vydáno v:International journal of solids and structures Ročník 304; s. 113054
Hlavní autoři: Khan, Naveed Ahmad, Sulaiman, Muhammad, Lu, Benzhou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2024
Témata:
Applied voltage
Artificial intelligence
Beam-type actuators
Casimir attractions
Dispersion forces
Kármán model
Van der Waals (VdW) force
Van der Waals (VdW) force
Casimir attractions
Applied voltage
Dispersion forces
Artificial intelligence
Kármán model
Beam-type actuators
ISSN:0020-7683
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Shrnutí:In this study, we investigate the effect of Van der Waals and Casimir forces on the mathematical model of nano-electromechanical systems (NEMS) such as nano-beam actuators that contain cantilever and double cantilever beams. The singular nonlinear boundary value problem governing the beam-type actuators, including geometric nonlinearity is solved by using an intelligent strength of feedforward artificial neural networks (ANNs) and hybridization of optimization algorithms such as arithmetic optimization algorithm (AOA) and active set algorithm (ASA). The proposed ANN-AOA-AS algorithm is employed to quantify the effect of changes in applied voltage, dispersion forces, geometric nonlinearity parameters, and initial axial strain on the deflection of the beam. Furthermore, to validate the results obtained by the proposed algorithm, statistical analyses are conducted to compare the approximate solutions with state-of-the-art methodologies available in the latest literature. In addition, performance indicators are defined such as mean square error (MSE), Nash–Sutcliffe efficiency (NSE), mean absolute deviations (MAD), root mean square error (RMSE), and Error in Nash–Sutcliffe efficiency (ENSE) to study the accuracy and efficiency of the solutions. The results show that these indicators’ mean percentage values lie around 10−4 to 10−6 which reflects the perfect modeling of the approximate solutions. •Developed a model analyzing geometric nonlinearity, Van Der Waals, and Casimir forces in NEMS.•Hybridized AOA, ASA, and ANNs to solve nonlinear boundary value problems in NEMS.•Designed ANN-AOA-AS technique model solutions for MEMS/NEMS in an unsupervised manner.•Validated ANN-AOA-AS with statistical analysis, outperforming traditional methods in beam deflection predictions.
ISSN:0020-7683
DOI:10.1016/j.ijsolstr.2024.113054