The worst response of mistuned bladed disk system using neural network and genetic algorithm

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for predicting the worst response of mistuned bladed disk. ANN is used to predict the responses of bladed disk system which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained from finite element model for different bladed disk configurations. Subsequently, GA is employed for arriving at optimum configuration of the bladed disk system by maximizing the blade responses. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on mistuned bladed disk systems for different sets of mistuning parameters, namely mistuning in modulus of elasticity and length of blades. Finally, the effect of adding shroud at the tip of blades in reducing the maximum response of the bladed disk system was investigated.