Magnetic particle testing (MT) has been widely used in nondestructive testing (NDT) of ferromagnetic materials. MT can be divided into wet method and dry method. The wet method is highly sensitive for microcracks and easy to operate. But the problem is that magnetic suspension liquid utilized in the wet method pollutes both test objects and environment. Dry magnetic particles utilized in dry method are recyclable and environmentally friendly. But the irrelevant magnet particles must be manually removed to reveal the magnetic particle indication (MPI). This operation limits both the detection efficiency and sensitivity of dry method. To increase the automation in MT, this article proposes a thermography-based dry MT method (TDMT). Specifically, the dry magnetic particles are heated before utilization and then thermal data are utilized for crack identification. TDMT inherits the economic and environmental advantages of the dry method. Additionally, the time-varying thermal data can provide more useful information than conventional MPI, which makes it possible to identify cracks without manual assistance. The mathematical model of TDMT is designed to prove its feasibility and a dimensionality reduction-based algorithm is applied for crack signal enhancement. Experiments on different specimens with artificial and natural cracks have been conducted. The notch with both a depth of $15~\mu \text{m}$ on the standard test shim and a width up to 0.8 mm on the steel plate can be successfully detected. The high sensitivity and wide detection range can be seen from TDMT.

Having been predominantly observed in rigid metal and metal alloys since 1865, the magnetoelastic effect was recently experimentally discovered in a soft matter system and used as a new working mechanism for energy and health care applications. Here, a theoretical framework is presented and proven to be universally accurate and robust in interpreting the giant magnetoelastic effect across soft systems subjected to various deformation modes, micromagnet concentrations, magnetization profiles, and geometric structures. The theory uncovers substantial, unique magnetoelastic phenomena in soft systems, including the magnetic pole reversal under localized compression. This work lays a firm foundation for an in-depth understanding and practical applications of the giant magnetoelastic effect in soft matter systems.

Magnetic particle testing (MT) has the advantages of high sensitivity and adaptability. However, during the formation of magnetic particle indication (MPI) corresponding to surface opening defects, the defects are filled with magnetic particles, resulting in the weakening of the leakage magnetic field (LMF), a phenomenon that has been ignored in existing studies. In order to enhance the visual feature of MPI, this paper proposes an isolation strip-based magnetic particle testing method (ISMT), which can avoid the attenuation of LMF. Meanwhile, ISMT can isolate the surface conditions of the specimen to avoid the formation of pseudo-magnetic particle indication (PMPI). The enhancement principle of ISMT is analyzed by simulation. Comparative experiments between ISMT and traditional MT were conducted to verify the visual enhancement effect of ISMT on defects of multiple depths. The results show that the enhancement effect of ISMT is 1.51 times, 1.36 times, and 1.08 times for the standard test shims of 60/100, 30/100, and 15/100, respectively. In addition, the advantage of ISMT in detecting larger defects is verified by experiments on hole defects of different sizes, and the isolation effect against surface oil stains is discussed.

During electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel composite sensor of an electromagnetic acoustic transducer (EMAT) and pulse eddy current (PEC) is designed. We use the amplitude of a bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on the amplitude of bottom echo are distinguished by combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect a bottom crack as small as 0.1 mm × 0.3 mm. The signal fusion method can accurately correct the amplitude of defect signals and the relative error is less than ±8%.

This paper proposes a novel composite measurement method for defect detection, which integrates PMFP (permanent magnetic field perturbation) testing and lift-off measurement. The PMFP method is used to detect defects, while the permanent magnet is utilized to construct a magnetic bridge for lift-off measurement. In PMFP testing, the variation of lift-off can significantly affect the signal. To mitigate this effect, the proposed method compensates for the defect signal using the measured lift-off information, which effectively reduces the impact of lift-off variation during the testing. Therefore, this paper constructs a magnetic dipole model based on the PMFP method and analyzes the numerical relationship between the defect signal and lift-off. Based on this, a composite lift-off measurement method for PMFP with a signal compensation function is proposed. This multifunctional sensing method has a compact structure, which significantly reduces the impact of lift-off on the defect signal. The experimental results demonstrate that the confidence level can reach 93% after compensating for the amplitude of the defect signal. The proposed method exhibits excellent performance in practical applications.