Analytical model of tilted driver–pickup coils for eddy current nondestructive evaluation

Cao, Binghua; Li, Chao; Fan, Mengbao; Ye, Bo; Tian, Guohui

doi:10.1088/1674-1056/27/3/030301

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Thus, the skin depth of the induced eddy current is 0.15 mm, which is substantially smaller than the depth of the machined slot (with a minimum depth of 0.4 mm, as shown in Table 2). The eddy current is designed as a driver-pickup or T-R sensor instead of a single or co-axial sensor, which is verified to have a higher spatial resolution, which doubles the resolution of single-coil sensor of the same size [36], has a wider frequency range, higher gain, and is barely affected by thermal drift [37]. Moreover, as shown in Table 1, the mean radius of the coil winding is only 1 mm, which achieves a relatively high sensitivity on the voltage mapping of notches. )…”

Section: Methodsmentioning

confidence: 99%

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Lü

Meng

Huang

et al. 2021

Sensors

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Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter–receiver (T-R) sensor is designed with a small mean radius of 1 mm. In addition, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution, with a limit of 0.2 μm and selected as 0.25 mm. Experiments were conducted for imaging of an aluminium sheet with seven machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10% for tilt angles up to 60 degrees.

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Section: Methodsmentioning

confidence: 99%

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Lü

Meng

Huang

et al. 2021

Sensors

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show abstract

“…The eddy current is designed as driver-pickup or T-R sensor instead of single or co-axial sensor, which is verified to having a higher spatial resolution, which doubles the resolution of single-coil sensor with the same size [36], wider frequency range, higher gain, and being barely affected by the thermal drift [37]. Moreover, as shown in Table 1, the mean radius of the coil winding is only 1 mm, which achieves a relatively high sensitivity on the voltage mapping of notches.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Lü

Meng

Huang³

et al. 2021

Preprint

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Electromagnetic eddy current sensors are commonly used to identify and quantify the surface notches of metals. However, the unintentional tilt of eddy current sensors affects results of size profiling, particularly for the depth profiling. In this paper, based on the eddy current thin-skin regime, a revised algorithm has been proposed for the analytical voltage or impedance of a tilted driver–pickup eddy current sensor scanning across a long ideal notch. Considering the resolution of the measurement, the bespoke driver–pickup, also termed as transmitter-receiver (T-R) sensor is designed with a small mean radius of 1 mm. Besides, the T-R sensor is connected to the electromagnetic instrument and controlled by a scanning stage with high spatial travel resolution , with a limit of 0.2 μm and selected as 0.25 mm. Experiments have been out on the voltage imaging of an aluminium sheet with 7 machined long notches of different depths using T-R sensor under different tilt angles. By fitting the measured voltage (both real and imaginary part) with proposed analytical algorithms, the depth profiling of notches is less affected by the tilt angle of sensors. From the results, the depth of notches can be retrieved within a deviation of 10 % for tilt angles up to 60 degrees.

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“…A schematic of the T-R probe is shown in Figure 5. Compared with absolute probes with a single coil, T-R probes have a higher gain, have a wider frequency range, and are unaffected by thermal drift (Cao et al, 2018). In addition, the spatial resolution of T-R probes along a scan line is almost twice as high as that of absolute probes with the same coil size.…”

Section: Experimental Setup and Materialsmentioning

confidence: 99%

A Deep Belief network and Least Squares Support Vector Machine Method for Quantitative Evaluation of Defects in Titanium Sheet Using Eddy Current Scan Image

Bao

Wang

et al. 2020

Front. Mater.

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Titanium (Ti) is an ideal structural material whose use is gradually emerging in civil engineering. Regular defect evaluation is indispensable during the long-term use of Ti sheets, which can be performed effectively using eddy current (EC) imaging, a method of visualizing defects that is convenient for inspectors. However, as EC scan images contain abundant information and have discrepancies in terms of their quality, it is difficult to extract effective features, thus affecting the evaluation results. In this article, we propose a method that combines the EC imaging technology with a quantitative evaluation method for Ti sheet defects based on the deep belief network (DBN) and least squares support vector machine (LSSVM). A multilayer DBN is constructed to extract the effective features from EC scan images for Ti sheet defects. Based on the extracted feature vectors, a multi-objective regression model of defect dimensions is established using the LSSVM algorithm. Then, the dimensions of Ti sheet defects such as length, diameter, and depth are quantitatively evaluated by the effective features and the efficient regression model. The experimental results show that the evaluation errors for the defect lengths and depths tested are less than 3 and 5%, respectively, confirming the validity of the proposed method.

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Analytical model of tilted driver–pickup coils for eddy current nondestructive evaluation

Cited by 10 publications

References 25 publications

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

Analysis of Tilt Effect on Notch Depth Profiling Using Thin-Skin Regime of Driver-Pickup Eddy-Current Sensor

A Deep Belief network and Least Squares Support Vector Machine Method for Quantitative Evaluation of Defects in Titanium Sheet Using Eddy Current Scan Image

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