Thermomechanical Bending Analysis of FG Sandwich Plates Using a Quasi-Three-Dimensional Theory

Sayyad, Atteshamuddin S.; Ghumare, Shantaram M.

doi:10.1061/(asce)as.1943-5525.0001249

Cited by 5 publications

(1 citation statement)

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“…In recent years, substantial effort has been dedicated to understanding the vibrational characteristics of laminated composite structures in the presence of various environmental conditions. Several scholars have investigated the dynamic response of the composite structures such as beams [3,[12][13][14], shells [6,7,15,16], and plates [2,17,18] using various analytical, numerical, and experimental methods. Sobhy [12] proposed a new four-variable shear deformation theory to investigate the vibration and buckling behavior of functionally graded (FG) sandwich plates.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

et al. 2021

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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.

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