Magnetic Barkhausen noise (MBN) is one of the most effective methods for determining the easy axis of ferromagnetic materials and for evaluating texture and residual stress in a nondestructive manner. MBN signals from multiple angles and different magnetization sections can be used to characterize magnetic anisotropy caused by various magnetization mechanisms. This paper reviews the development and application of magnetic anisotropy detection technology, and the MBN anisotropy models that take into account domain wall motion and magnetic domain rotation are analyzed thoroughly. Subsequently, the MBN anisotropy detection devices and detection methods are discussed, and the application of magnetic anisotropy detection technology in stress measurement and texture evaluation is reviewed. From the perspective of improving detection accuracy, the influence of composite mechanisms on magnetic anisotropy is analyzed. Finally, the opportunities and challenges faced by current magnetic anisotropy detection technology are summarized. The relevant conclusions obtained in this paper can be used to guide the MBN evaluation of magnetic anisotropy in ferromagnetic materials.

This study proposes a novel modelling approach that integrates mutual information (MI)-based parameter screening with random forest (RF) modelling to achieve an accurate quantitative prediction of surface hardness and impact energy in two martensitic stainless steels (1Cr13 and 2Cr13). Preliminary analyses indicated that the magnetic parameters derived from Barkhausen noise (MBN), and the incremental permeability (IP) measurements showed limited linear correlations with the target properties (surface hardness and impact energy). To address this challenge, an MI feature screening method has been developed to identify both the linear and non-linear parameter dependencies that are critical for predicting target mechanical properties. The selected features were then fed into an RF model, which outperformed traditional multiple linear regression in handling the complex, non-monotonic relationships between magnetic signatures and mechanical performance. A key advantage of the proposed MI-RF framework lies in its robustness to small sample sizes, where it achieved high prediction accuracy (e.g., R² > 0.97 for hardness, and R² > 0.86 for impact energy) using limited experimental data. By leveraging MI’s ability to capture multivariate dependencies and RF’s ensemble learning power, it effectively mitigates overfitting and improves generalisation. In addition to demonstrating a promising tool for the non-destructive evaluation of martensitic steels, this study also provides a transferable paradigm for the quantitative assessment of other mechanical properties by magnetic feature fusion.

As an advanced interaction mode, gestures have been widely used for human–computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function $J_{CP}$ to solve the inconsistency between the gesture comfort $J_{CS}$ and measurement precision $J_{P_H}$ in the gesture interaction. The proposed comfort model $CS$ takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The $CS$ can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ${∆P}_H$ is calculated from the measurement error $({∆X}_H,{∆Y}_H,{∆Z}_H)$ caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function $J_{CS}$, based on the comfort model $CS$, and the objective function $J_{P_H}$, based on the measurement precision models ${∆P}_H$, the consistency and the difference can be found due to the variation of the radius $r_{B\_RHO}$ and the center coordinates $P_{B\_RHO}\left(x_{B\_RHO},y_{B\_RHO},z_{B\_RHO}\right)$. The proposed objective function $J_{CP}$ compromises the inconsistency between the objective function $J_{CS}$ and $J_{P_H}$. Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing.

In this study, the methods of tangential magnetic field (TMF), magnetic Barkhausen noise (MBN), and incremental permeability (IP) were employed for in the simultaneous, quantitative prediction of target properties (bidirectional residual stress, surface hardness, and case depth) in the 45 steel plate. The bidirectional magnetic signals and target properties were measured experimentally. The results of Pearson correlation analyses revealed that most parameters of the MBN and IP signals are strongly correlated with both residual stress and surface hardness under the influence of multiple target properties. The multiple linear regression (MLR) model demonstrated highly accurate quantitative prediction of residual stress and hardness in the y-direction. However, the simultaneous prediction of residual stress and case depth in the x-direction proved less effective than expected. To address this limitation, an inversion method was developed based on the regression model with the single parameter as the dependent variable and the target properties as the independent variable. By incorporating known magnetic parameters and target properties, the model effectively determined the unknown target properties. After applying the method, the coefficient of determination (R²) for x-direction residual stress increased from 0.89 to 0.96 and the absolute error (AE) of case depth decreased from 0.10 mm to 0.04 mm for case depths below 0.15.

The strong background noise largely interferes with the feature extraction results of early faults of rolling bearings and the number of decomposed modes in the feature mode decomposition (FMD) algorithm depends on empirical selection. An overlapping group sparsity (OGS) method has an unsatisfactory performance in noise reduction. Therefore, in order to solve the above problems, an adaptive feature mode decomposition (AFMD) method was proposed in this study. In AFMD, the number of decomposed modes was determined with the singular value distribution curve to establish the average fractal dimension determination criterion for the selection of sensitive modes. In addition, a nonconvex overlapping group sparsity (NCOGS) noise reduction method was proposed. In NCOGS, a nonconvex penalty function was introduced into the regularization term of the OGS method to enhance the sparsity of the signal, so that the regularization parameter and the convexity parameter in NCOGS could be screened with the standard deviation of noise and permutation entropy values, respectively. Then, AFMD and NCOGS were combined together to extract the features of early faults of rolling bearings. Simulation and experimental results showed that the combined method effectively suppressed the noise interference generated in feature mode decomposition and improved the feature extraction capability of early faults of rolling bearings.

To achieve high-precision detection of magnetic anisotropy in ferromagnetic materials, this paper develops a magnetic anisotropy detection system based on enhanced time–frequency analysis. The hardware platform mainly consists of an excitation board, a power amplifier, a data acquisition card, sensors, and a host PC. The software is developed on the LabVIEW platform and integrates data acquisition, real-time signal processing, and feature analysis into a unified measurement and control system. Through frequency-sweep and amplitude-sweep experiments, the optimal operating parameter range of the sensor is determined, and the system repeatability is evaluated using the coefficient of variation of the measured data. The results demonstrate that the system exhibits good repeatability for 21 characteristic parameters, satisfying the requirements for high-precision detection. On this basis, the short-time Fourier transform is introduced into the anisotropy analysis of magnetic Barkhausen noise, and a multidimensional feature parameter system is established. Experimental comparisons between 30QG130 grain-oriented silicon steel and X70 pipeline steel indicate that the proposed time–frequency domain method exhibits higher sensitivity in quantifying the degree of magnetic anisotropy and identifying the rolling direction of materials than conventional time-domain methods. The anisotropy coefficient k reaches its maximum value in the critical magnetization region. Further experimental results show that different anisotropic mechanisms exhibit distinct response patterns in the time and frequency domains. Time–frequency analysis can directly reveal the magnetic domain magnetization dynamics by simultaneously observing time-domain evolution and frequency-domain energy distribution, thereby providing a novel and effective approach for decoupling complex magnetic anisotropy coupling mechanisms.