Abstract
The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.
Highlights
Polymer composite materials have gained substantial importance in high-end structural engineering fields such as the aerospace and automobile industries [1], biomedical industries [2], construction industries [3], naval industries [4], etc
The non-dimensional frequency for the clamped laminated sandwich plates operating in elevated temperature and moisture conditions are obtained considering a length to thickness (a/H) ratio of 100 and length to width (a/b) ratio of 0.5
The results obtained from the developed finite element (FE) model were used to train and develop an efficient artificial neural network (ANN) predictive model to estimate the fundamental frequencies of laminated composite sandwich (LCS) plates operating in elevated thermal and moisture environments
Summary
Polymer composite materials have gained substantial importance in high-end structural engineering fields such as the aerospace and automobile industries [1], biomedical industries [2], construction industries [3], naval industries [4], etc. Sit and Ray [22] investigated the effect of the hygrothermal environment on the free vibration characteristics of the laminated composite plates made of glass and bamboo fiber mats Both numerical and experimental results indicated that the reduction percentage in natural frequency values for the bamboo composite plate is Materials 2021, 14, 3170 higher than the glass epoxy composite plate for all the values of temperature and moisture concentrations considered. The ANN model was developed using the Levenberg–Marquardt backpropagation algorithm to predict the natural frequency and buckling loads of composite plates. A methodology is proposed to generate a predictive model to understand the influence of thermal and moisture environments on the free vibration characteristics of skew LCS plates. Ratio, core thickness to thickness of face sheet (tc /tf ) ratio, fiber orientation, skew angle, and boundary constraints on the vibrational characteristics are investigated under various hygrothermal conditions
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