Double Cracks Identification in Functionally Graded Beams Using Artificial Neural Network

Journal of Solid Mechanics

مقاله 2، دوره 5، شماره 1، خرداد 2013، صفحه 14-21   

نوع مقاله: Research Paper

نویسندگان

F Nazari¹؛ M.H Abolbashari^{* 2}

¹Department of Mechanical Engineering, Ferdowsi University of Mashhad

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

چکیده

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.

کلیدواژه‌ها

Double cracks؛ Functionally graded beam؛ Artificial Neural Network؛ Model analysis

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