Nonlinear frequencies of porous functionally graded piezo-elasto-magneto plates with non-uniform thickness: a hybrid FEM-ANN predictive approach

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Publicado en:Acta Mechanica vol. 235, no. 2 (Feb 2024), p. 633
Autor principal: Mahesh, Vinyas
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Springer Nature B.V.
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Acceso en línea:Citation/Abstract
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100 1 |a Mahesh, Vinyas  |u City, University of London, Department of Engineering, London, UK (GRID:grid.4464.2) (ISNI:0000 0001 2161 2573) 
245 1 |a Nonlinear frequencies of porous functionally graded piezo-elasto-magneto plates with non-uniform thickness: a hybrid FEM-ANN predictive approach 
260 |b Springer Nature B.V.  |c Feb 2024 
513 |a Journal Article 
520 3 |a Predicting coupled frequency response of piezo-elasto-magnetic (PEM) structures is crucial for accurately designing sensors/actuators, energy harvesters and other smart structures. The numerous coupling parameters involved make the numerical analysis through the conventional finite element methods (FEM) cumbersome and time-consuming, particularly for non-uniformly shaped/variable thickness structures. Hence, in this article, a hybrid approach integrating the computational benefits of FEM and artificial neural network (ANN) models has been proposed to predict the coupled nonlinear frequency response (NLFR) of porous functionally graded PEM (PFG-PEM) plates with non-uniform geometries. Through this approach, the computational efforts are substantially reduced retaining appreciable accuracy. A FEM model based on Hamilton’s principle, von Karman’s nonlinearity and higher-order shear deformation theory (HSDT) was initially developed for non-uniform PFG-PEM plates. The large datasets collected from the nonlinear FEM simulation are used to train an ANN model that can accurately predict the NLFR of non-uniform PFG-PEM plates for out-of-range input data sets. The plates in the current study have non-uniform thicknesses varying bi-linearly, linearly, and exponentially. The different variants of PFG-PEM composites and porosity patterns are evaluated, whose material property varies across the thickness according to a power law distribution. In addition, two forms of electromagnetic boundary conditions, such as open and closed circuits, are enforced on the plate, and its NLFR is assessed. Further, several numerical examples are presented to understand the interdependency of several material and geometrical parameters on the overall NLFR of PFG-PEM plates. This predictive tool can be readily used for further optimisation of smart structural design, significantly reducing the time consumed. 
653 |a Model accuracy 
653 |a Energy harvesting 
653 |a Finite element method 
653 |a Frequency response 
653 |a Material properties 
653 |a Artificial neural networks 
653 |a Numerical analysis 
653 |a Functionally gradient materials 
653 |a Boundary conditions 
653 |a Nonlinear response 
653 |a Mathematical models 
653 |a Plates 
653 |a Structural design 
653 |a Variable thickness 
653 |a Design optimization 
653 |a Parameters 
653 |a Hamilton's principle 
653 |a Actuators 
653 |a Shear deformation 
653 |a Smart structures 
653 |a Nonlinearity 
653 |a Smart materials 
653 |a Neural networks 
653 |a Shear strain 
773 0 |t Acta Mechanica  |g vol. 235, no. 2 (Feb 2024), p. 633 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2932783580/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/2932783580/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch