Dynamical analysis and vibration estimation of a flexible plate with enhanced active constrained layer damping treatment by combinatorial neural networks of surrogates

The effective estimation of the vibration of spacecrafts are frontier issues in the aerospace industry. But the vibration of aerospace components such as space solar panels and flexible manipulators under large overall rotating motion is usually very complex due to strong nonlinearity and often requires a lot of repetitive and burdensome calculations. Artificial neural network (ANN) is composed of interconnected neurons, and a surrogate model can be established to effectively predict the mechanical characteristics. In addition, the computational efficiency will be greatly improved if the object is changed from the model that is established by commercial finite element method (FEM) software or numerical calculations to the surrogates. However, the stochasticity of the estimation by single neural network is inevitable, and some neural networks much rely on the initial weight and threshold. This paper will implement a new combinatorial strategy that relies on multiple neural network models, which have different activation functions and regression feedback processes based on reasonable selection of sample points. To solve the elongated training time led by comparing these neural networks, genetic algorithm (GA) is used to optimize the iteration speed of one neural network when finding the optimal output value. Thus, better estimation accuracy will be achieved by designing an algorithm to combine these combinatorial neural networks of surrogates (CNNS). Based on the proposed method, the vibration estimation of a rotating flexible plate with enhanced active constrained layer damping (EACLD) treatment, and the vibration suppression capacity of the composite structure under various operating conditions will be investigated.