Neural identification for critical flutter load of a cracked shaft simultaneously subjected to a follower force with an axial force

Authors:
I. Takahashi
Affiliations:
Department of Mechanical Engineering, Kanagawa Institute of Technology, Japan
Venue:
ICECT'03 Proceedings of the third international conference on Engineering computational technology
Year:
2002

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Abstract

With increasing size and complexity of machines and vessels, the inverse problems of continuous bodies are becoming important. In this paper, the possibility of using a Multilayer Perceptron Network trained with the Backpropagation Algorithm for detecting the critical flutter load of cracked shafts is studied. The considered model is a tapered shafts, using a transfer matrix method, to estimate the changes in various modal parameters, caused by the shape parameters and support conditions of shafts.