View article

In complex aeroengine structures, it is necessary to consider multi-physical loads involving vibrational, structural, aerodynamical and heat loads for safe design. Turbine blisk commonly involves multi-failure modes such as stress, strain, deformation, fatigue, and creep under multi-physical loads during operation, so that efficient and accurate simulating approach is one of main issues in structural safety design. Machine learning (ML) approaches using artificial intelligence (AI) are workable to improve the simulation efficiency of mechanical responses under nonlinear dynamic structural analysis. In this work, hybrid artificial neural network (ANN) models are proposed to simulate the failure modes of turbine blisk. The accuracy of AI-based ANN is discussed using six music-inspired optimization algorithms named as harmony search (HS), improved HS (IHS), global-best HS (GHS), improved GHS (IGHS), adaptive GHS …