Dynamic stability improvement in spinning FG-piezo cylindrical structure using PSO-ANN and firefly optimization algorithm

The artificial neural networks (ANNs) are commonly used in prediction of different systems behavior. In the ANN network hyper parameters similar to number of hidden layers and learning rate, when required, are commonly chosen manually. In the present study, an ANN is designed for investigating stability analysis of a spinning microscale cylindrical structure. In this regard, the weights and biases in the network are optimized using particle swarm algorithm (PSO). A second concurrent optimization using firefly algorithm is engaged for the purpose of optimizing the number of perceptrons in two hidden layers. The ANN is trained using data obtained from numerical solution of modified strain gradient theory (MSGT) equations for dynamic behavior of the spinning cylinder equipped with piezo electric layer. The numerical procedure comprises differential quadrature method. At the next stage of the optimization, the input parameters including thickness, radius and length of different layers of cylinder, elasticity constants and model parameters are optimized using another round of PSO to obtain the optimum stability condition of the cylinder. The results show that ANN could predict the dynamic behavior and phase-plane diagram of the structure in an accurate way comparing to the numerical results. On the other hand, having a trained ANN, the optimization of the parameters are performed in a simple way.