13 January 2005 Surface crack detection for Al plate using the surface acoustic waves and neural network identification
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Abstract
This paper utilized the Finite Element Method to investigate the transient scattering of Rayleigh wave by a surface crack in a plate. The incident wave models the guided waves generated by a pulsed line source laser irradiation on the top surface of the plate. The pulsed laser is assumed to be transient heat source, and the surface acoustic wave is calculated based on the thermoelastic theory. We have computed the different results of the Al plates with the varied depth surface-breaking crack, then attained the temporal characteristics of reflected waves and transmitted waves which are generated by the initial surface acoustic waves interacted with the surface breaking cracks with different depth. The artificial neural networks (ANN) are applied to establish the mapping relationship between the characteristic of the reflected waveform and the crack depth. The results of crack damage detection for Al plates show that the method developed in this paper can be applied to online structural damage detection and health monitoring for various industrial structures.
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Jianfei Guan, Zhonghua Shen, Baiqiang Xu, Jian Lu, Xiaowu Ni, "Surface crack detection for Al plate using the surface acoustic waves and neural network identification", Proc. SPIE 5629, Lasers in Material Processing and Manufacturing II, (13 January 2005); doi: 10.1117/12.572871; https://doi.org/10.1117/12.572871
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