Double Cracks Identification in Functionally Graded Beams Using Artificial Neural Network

Document Type : Research Paper


1 Department of Mechanical Engineering, Ferdowsi University of Mashhad

2 Department of Mechanical Engineering, Lean Production Engineering Research Center, Ferdowsi University of Mashhad


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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