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

The present study deals with the development of a prediction model to investigate impact temperature and moisture on vibration response skew laminated composite sandwich (LCS) plate using artificial neural network (ANN) technique. Firstly, finite element is generated incorporate hygro-elastic thermo-elastic characteristics LCS first-order shear deformation theory (FSDT). Graphite-epoxy laminates are used as face sheets, DYAD606 viscoelastic material core material. Non-linear strain-displacement relations generate initial stiffness matrix in order represent from uniformly varying concentrations. mechanical derived linear associations. Then results obtained numerical train ANN. About 11,520 data points were collected analysis Levenberg–Marquardt algorithm. developed ANN influence various process parameters frequency system, outcomes compared model. Several examples presented conferred comprehend plates.