Maximization of Eigenfrequency Gaps in a Composite Cylindrical Shell Using Genetic Algorithms and Neural Networks

Abstract

This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency within the considered range. The proposed optimization algorithm, which involves genetic algorithms, artificial neural networks, and iterative retraining of the networks using data obtained from tentative optimization loops, is accurate, robust, and significantly faster than typical genetic algorithm optimization in which the objective function values are calculated using the finite element method. The combined genetic algorithm–neural network procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples illustrate a combined approach to avoiding resonance through the maximization of a frequency gap around external excitation frequencies complemented by the maximization of the fundamental natural frequency. The necessary changes in natural frequencies are caused only by appropriate changes in the lamination angles. The investigated structures are thin-walled, laminated one- or three-segment shells with different boundary conditions.

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

“…A four-node multilayer shell FE (first-order shear theory) was applied; in the Adina FE code ([ 38 ], used in all FE calculations) it is called MITC4. The FEM model was built using the experience gained by the authors in previous research (see [ 6 , 11 , 39 , 40 ]), where FE convergence was verified and the results from different FEM systems were compared.…”

Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

“…All the calculation have been performed using Adina FE code; the choice of finite element and FE mesh density was based on the experience gained by the authors in previous research (see [34,35,60,61]), where FE convergence was verified and the results from different FEM codes were compared. Neither higher FE mesh density nor the change of an FE element to a higher order one caused a significant changes in the analyzed quantities, and in particular, do not affect the mode shapes investigated in this paper.…”

A New Analytical Approach for Nonlinear Global Buckling of Spiral Corrugated FG-CNTRC Cylindrical Shells Subjected to Radial Loads

“…Substituting the deflection form (19) and stress function (20) into Equation (15) and then applying the Galerkin method in the ranges 0 ≤ x ≤ L and 0 ≤ y ≤ 2πR leads to…”

“…Nonlinear buckling behavior of large deflection FGM and porous-core FGM spiral stiffened cylindrical shells subjected to axial compressive, radial and torsional loads with and without elastic foundation by using the improved Lekhnitskii's smeared stiffener technique and the Galerkin method was studied by Nam et al [15][16][17][18][19]. Based on genetic algorithms and neural networks, Miller and Ziemiański [20] maximized the eigenfrequency gaps of laminated composite cylindrical shells.…”