The high sensitivity for low-frequency fields makes the tiny giant magnetoresistance (GMR) sensors suitable for high-resolution eddy-current (EC) imaging. In this work, we proposed a novel design of the miniature differential EC probe with a spin-valve GMR sensor for surface and subsurface EC imaging. The differential EC probe consists of a full-bridge GMR chip mounted vertically on the edge of a tiny printed circuit board to reduce the liftoff distance. The excitation signal is perpendicular to the sensing axis of the GMR chip, which makes it sensitive to the unbalance EC signal on the surface of a conductive sample. The miniature EC probe operated at frequencies from 5 to 65 kHz is sensitive to the hidden defects on a flat conductor. The spatial resolution and stability of the device are verified by taking the 2-D scan images over the copper film samples with hidden defects, showing that the spatial resolution is better than 2 mm for the flaw 0.5 mm below the top surface of a multilayer structure. The proposed device is useful in the detection of small surface and subsurface defects, such as cracks, metal loss, and other mechanical damages.

This study proposes a novel approach for contactless conductivity measurement of nonmagnetic metal sheets for a production line. The system uses a self-resonant eddy-current (EC) sensor as the conductivity probe, of which the sensitivity is enhanced by minimizing the parasitic resistance in the resonant circuit. The output responses at various liftoff distances are nonlinear curves unique to the sample’s conductivity. The quaternary polynomial regression is utilized to establish the analytic approximation function to depict the correlation between the phase and the logarithmic magnitude for standard samples. The phase–magnitude relationships are generally monotonic and useful for conductivity conversion using the standard data. The polynomial interpolation is employed to establish the phase–conductivity relations based on the standard samples. The self-developed computer software can instantaneously convert the probe output into the conductivity of the metallic material under test. The experimental findings point to the feasibility of the suggested method that can perform conductivity measurement with a relative error of less than 3% at a liftoff distance of less than 1 mm, indicating the practicability of the proposed technique in conductivity mapping for nonferrous metals.

Objective: The quality and work-life of ball bearings depend on the material properties of the steel ball, hence it is necessary to carefully classify their properties for bearings and related applications. This paper presents the classification of steel balls based on the subtle differences in their electromagnetic properties. Approach: The conductivity and magnetic susceptibility for steel balls of the same kind are measured to investigate their correlation with eddy-current (EC) signals. Main results: The developed EC sensor works at the resonant frequency of 117 kHz with an optimal readout resistance of 15 kΩ, which helps to boost the signal level without a high-gain preamplifier. To detect the EC signal, the steel ball under test moves through the pickup coil, and the recorded data are used to build a voltage probability map (VPM) for the classification of the steel ball properties. Experimental results show that the steel balls with and without the hardening process can be identified by the change in the amplitude and phase of the EC signal, which is consistent with the observed change in the electromagnetic properties of steel balls. Significance: The built system can be applied to the related industries to check the quality of steel balls before use.

The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples.

The accuracy in the measurement of magnetization curve (M-H) for soft materials is easily influenced by the external static field in the unshielded environment. To eliminate the influence from the environmental field, the measurement system must be placed in a zero-gauss chamber. However, such a device is not widely available and also costly. In this work, the rapid M-H measurement system with a giant magnetoresistance (GMR) sensor serving as the zero-field detector is proposed. The M-H curve is obtained by integrating the voltage in the pickup coil to determine the magnetization of the test sample under a sweeping magnetic field. The environment field component along the direction of the sweeping field is suppressed by using the output of the GMR sensor as the trigger signal to determine the offset field in the measured M-H curve. The experimental results show that the M-H curves for various kinds of soft magnetic samples, e.g. mu-metal wire, can be obtained under the interference of an applied static field. The proposed method helps improve the accuracy of the rapid M-H measurement system in the unshielded environment.

In this work, a method for identifying counterfeit coins based on an electromagnetic acoustic transducer (EMAT) to detect the difference in the coin’s natural acoustic frequency response is presented. In the experimental system, the acoustic oscillation induced by a pulsed magnetic field is received by a microphone and recorded by an oscilloscope. The natural acoustic frequency of the coin is resolved by the fast Fourier transform (FFT) method on the computer. It is found that the natural frequencies of the possible counterfeit coins deviate significantly from the standard ranges of 16.9 to 17.4 kHz for the authentic 50 New Taiwan Dollar (NTD) coins. The observed natural frequencies of the coin are consistent with the values predicted by analytical estimation. We also built a prototype EMAT coin classification system to detect the natural acoustic frequency by direct frequency counting using a microcontroller. The prototype system demonstrates that a counterfeit coin can be identified by its natural frequency in less than 30 ms using the EMAT method. The proposed technique can be applied to the vending machine to improve the accuracy in discriminating between authentic and counterfeit coins.