In this study, the natural frequencies of Functional Graded Materials (FGM) plates are predicted using Artificial Neural Network (ANN). A model based on Third-order Shear Deformation Theory (TSDT) and FEM is used to train the ANN model. Different training methods are tested to simulate input and output dependency. As this is a parametric model, several architectures and optimization algorithms were tested. The proposed model allows us to minimize the CPU time to evaluate candidate material properties for FGM plate material selection and demonstrate their influence on dynamic behavior. Consequently, the time required for the FGM design process (candidate materials for material selection) and the geometric optimization of the FGM structure would remain reasonable. The ANN model can help industries to produce FGM plates with good mechanical properties of the selected materials. I addition, this model can be used to directly predict vibration behavior by testing a large number of FGM plates, representing all possible combinations of metals and ceramics in today's industry, without having to solve any eigenvalue problems.
Key Words
artificial neural networks, CPU time, finite element method, natural frequencies, third order shear deformation theory
Address
Mohamed Janane Allah, Saad Hassouna and Abdelaziz Timesli — Hassan II University of Casablanca, National Higher School of Arts and Crafts of Casablanca, AICSE Laboratory, 20670 Casablanca, Morocco
Rachid Aitbelale — University of Chouaïb Doukkali, Faculty of sciences, Laboratory of Catalysis and Corrosion of Materials, El Jadida, Morocco
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