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Crack Propagation Detection Method in the Structural Fatigue Process

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Abstract

In this paper, a method for measuring crack propagation length in the structural fatigue process is presented. A convolutional neural network is presented to eliminate noise interference of matching marks and recognize crack features. The initial region of the crack band is obtained through the convolution neural network result. Based on the initial region, an improved algorithm for tip recognition is proposed to calculate the exact position coordinates for crack tips. Finally, according to the position coordinates, the information of crack length is obtained. By increasing the number of cameras, cracks in different directions and locations can be detected simultaneously. The relationship between crack propagation length and the loading values, such as force and fatigue cycles, can be obtained by the proposed method. Meanwhile, compared with measuring means of the electromagnetic vortex, the effectiveness and accuracy of the proposed method are validated.

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Acknowledgments

We would like to thank the financial support by National Natural Science Foundation of China (grant No. 11602201, 11772268, 1152220 and 11527803), Natural Science Basic Research Plan in Shaanxi Provice of China (No. 2018JQ1060).

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Authors and Affiliations

  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    X. Guo, Y.T. Yuan & Y. Liu

  2. International Research Laboratory of Impact Dynamics and its Engineering Application, Xi’an, 710072, China

    X. Guo, Y.T. Yuan & Y. Liu

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  1. X. Guo
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  2. Y.T. Yuan
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  3. Y. Liu
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Correspondence to X. Guo.

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Guo, X., Yuan, Y. & Liu, Y. Crack Propagation Detection Method in the Structural Fatigue Process. Exp Tech 45, 169–178 (2021). https://doi.org/10.1007/s40799-020-00425-1

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