Thickness Measurement of an Iron Plate Using Low-Frequency Eddy Current Testing With an HTS Coil

2016 ◽  
Vol 26 (5) ◽  
pp. 1-5 ◽  
Author(s):  
Teruyoshi Sasayama ◽  
Tomoki Ishida ◽  
Masaaki Matsuo ◽  
Keiji Enpuku
Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 419
Author(s):  
Xiaobai Meng ◽  
Mingyang Lu ◽  
Wuliang Yin ◽  
Abdeldjalil Bennecer ◽  
Katherine J. Kirk

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm.


Author(s):  
Xiaobai Meng ◽  
Mingyang Lu ◽  
Wuliang Yin ◽  
Abdeldjalil Bennecer ◽  
Katherine Kirk

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 1119-1126
Author(s):  
Yun Song ◽  
Xinjun Wu

Pulsed Eddy Current Testing (PECT) has been used to measure the wall thickness of ferromagnetic metallic component with thick insulation. However, for the non-ferromagnetic metallic component, there is still the problem to be solved. The main purpose of this study is to find an effective feature, to measure wall thinning of the non-ferromagnetic metallic component under the large liftoff, and further expand application of the PECT technology. Hence, the time to the last peak point (TLPP) is proposed based on the analytical predicted signals. Furthermore, the influence of the variable liftoff is studied, and the error caused by the liftoff is within the acceptable range. Two sets of experiments are conducted to test the performance of the TLPP under various liftoffs. The results show that when the wall thickness is reduced by more than 40%, the measurement error based on the TLPP is within 11%.


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