Open AccessUltrasonic flaw detection spectrogram characterization of vermicular graphite cast iron engine cylinder head
Author(s) -
Changliang Guo,
Duo Fang,
Chengzong Wang,
Tao Qin,
Zenghua Liu,
Zehua Liu,
Yu Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1996/1/012005
Subject(s) - spectrogram , hilbert–huang transform , cast iron , wavelet transform , fourier transform , materials science , ultrasonic sensor , acoustics , short time fourier transform , cylinder head , signal (programming language) , wavelet , ultrasonic testing , nondestructive testing , waveform , time–frequency analysis , computer science , artificial intelligence , fourier analysis , mathematics , computer vision , engineering , physics , composite material , mechanical engineering , filter (signal processing) , mathematical analysis , quantum mechanics , programming language , internal combustion engine , telecommunications , radar
The defects formed in the manufacture of the vermicular graphite cast iron engine cylinder head seriously affect the operation of the engine, which is necessary to detect. Ultrasonic testing is a non-destructive testing method that has the advantages of quick response, high resolution, and high security. In this paper, various types of specimens are prepared corresponding to different types of actual defects in the vermicular iron cylinder head. An ultrasonic A-scan system was built to test the specimens. The short-time Fourier transform, the continuous wavelet transform, the empirical wavelet transform, and the empirical modal decomposition were adopted to transform the signals into spectrograms which were further analyzed to reveal the inherent features of defects. The results show that the short-time Fourier transform can be used to distinguish all the common defects comparing to other methods. Comparing to the time-domain waveforms, the transformed spectrograms provide clear time-frequency distribution and highlight the inherent characteristics of the signal.