Large-scale parallel computation for the reconstruction of natural stress corrosion cracks from eddy current testing signals

2003 ◽  
Vol 36 (7) ◽  
pp. 449-459 ◽  
Author(s):  
Noritaka Yusa ◽  
Zhenmao Chen ◽  
Kenzo Miya ◽  
Tetsuya Uchimoto ◽  
Toshiyuki Takagi
2006 ◽  
Vol 129 (4) ◽  
pp. 719-728 ◽  
Author(s):  
Zhenmao Chen ◽  
Ladislav Janousek ◽  
Noritaka Yusa ◽  
Kenzo Miya

In this paper, a novel nondestructive strategy is proposed for distinguishing differences between a stress corrosion crack (SCC) and a fatigue crack (FC) based on signals from eddy current testing (ECT). The strategy consists of measurement procedures with a special ECT probe and crack type judgment scheme based on an index parameter that is defined as the amplitude ratio of the measured signals. An ECT probe, which can induce eddy current flowing mainly in a selected direction, is proposed and applied to detect crack signals by scanning along the crack with different probe orientations. It is clear that the ratio of the amplitudes of signals detected for parallel and perpendicular probe orientations is sensitive to the microstructure of the crack, i.e., the parameter is much bigger for a fatigue crack than that of a SCC. Therefore, whether a crack is a SCC or a FC can be recognized nondestructively by comparing the index parameter with a threshold value that can be previously determined. In order to verify the validity of the proposed strategy, many artificial SCC and FC test pieces were fabricated and ECT inspections were performed to measure the corresponding crack signals. Numerical simulations were also conducted to investigate the physical principles of the new methodology. From both the numerical and experimental results, it is demonstrated that the strategy is very promising for the distinction of artificial SCC and FC; there is also good possibility that this method can be applied to natural cracks if the threshold value can be properly determined.


2013 ◽  
Vol 28 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Li Wang ◽  
Shejuan Xie ◽  
Zhenmao Chen ◽  
Yong Li ◽  
Xiaowei Wang ◽  
...  

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