Issue |
E3S Web Conf.
Volume 268, 2021
2020 6th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2020)
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Article Number | 01075 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/e3sconf/202126801075 | |
Published online | 11 June 2021 |
Mechanical structural health prognosis with nonlinear mixed frequency ultrasonic signal analysis
School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430074, China
* Corresponding author: pg01074075@163.com
In order to detect the early fatigue crack of mechanical components simply, this paper puts forward the ultrasonic testing technology of different side collinear mixing. Firstly, based on the nonlinear ultrasonic theory, the method of calculating the difference frequency and sum frequency nonlinear coefficients of mixing ultrasonic is deduced. Then, the ram-5000 SINAP ultrasonic system is used to detect the aluminum alloy specimens with five different depth fatigue cracks, and the corresponding spectrum diagram is drawn. From the experimental results, we get that the crack depth is positively correlated with the nonlinear coefficients of difference frequency and sum frequency within a certain crack depth. Finally, by analyzing and fitting the experimental data, the prediction models of the difference frequency and sum frequency nonlinear coefficients on the crack depth are established. Through the analysis and combination of the above two prediction models, the prediction model of the mixing relative nonlinear coefficient is established, and the average error of the three prediction models is compared. The results show that the mixing relative nonlinear model has better results. The research work in this paper makes a useful exploration for crack detection and crack depth prediction.
Key words: microdefect / collinear mixing / ultrasonic prognosis / nonlinear coefficient
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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