Double Cracks Identification in Functionally Graded Beams Using Artificial Neural Network

Subject Areas : Engineering

F Nazari ¹ (Department of Mechanical Engineering, Ferdowsi University of Mashhad)
M.H Abolbashari ² (Department of Mechanical Engineering, Lean Production Engineering Research Center, Ferdowsi University of Mashhad)

Received: 2012-12-08 Accepted : 2013-01-26 Published : 2013-03-30

Keywords: Artificial Neural Network, Functionally graded beam, Double cracks, Model analysis,

Abstract :

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against available references. Furthermore, four Multi-Layer Perceptron (MLP) neural networks are employed for identification of locations and depths of both cracks of FGB. Back-Error Propagation (BEP) method is used to train the ANNs. The accuracy of predicted results shows that the proposed procedure is suitable for double cracks identification detection in FGBs.

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