In this paper, a global amplitude-controlled discrete hyperchaotic memristive map is designed utilizing the hyperbolic tangent function. This map exhibits fixed points arranged in a line along the y-axis, and the stability distributions of these fixed points are delineated based on variations in both the initial conditions of the map and the parameter plane. The dynamic characteristics of the map were examined through the analysis of its 2D dynamics and the largest Lyapunov exponent (LE) distribution. The existence of multistability was robustly confirmed through a comprehensive analysis of the basin of attraction, the spectra of LE that depend on initial values, bifurcation diagrams, and trajectory plots. Additionally, the amplitude of the map can be adjusted both globally and locally through manipulation of the non-bifurcation parameter. Subsequently, a digital circuit powered by a microcontroller was designed to embody the map. In comparison to recent maps, the newly devised map exhibits superior efficacy in the realm of image encryption applications.

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends for the selected time- and frequency-domain statistics, and the deviations from these trends form the statistical residuals. Computed order tracking then converts the vibration signal to the angular domain, where five mechanism features describe meshing energy, harmonic structure, sideband modulation, and order-spectrum entropy. Removing the corresponding healthy speed trends yields the mechanism residuals. Robust Bounded Health-Consistency Weighting (RB-HCW) weights these residuals according to their variability in healthy data before they are fused with the statistical residuals and modeled by Deep Support Vector Data Description (DeepSVDD). The Sequential Bayesian Queue-Based Alarm (SBQA) module then confirms whether abnormal decisions persist across successive windows. Across the four fault types under the two separately modeled load conditions, the proposed method achieved macro-averaged true positive rate (TPR), accuracy (ACC), and F1-score values of 93.31%, 92.58%, and 94.02%, respectively.

The face of cutting tools serves as the critical interface for chip–tool interaction and wear initiation, significantly influencing tool performance and service life. By implementing micro-groove structures on the face to reduce the chip–tool contact area, the cutting mechanics of the tool are altered. Theoretical analysis indicates that the cutting equations of the grooved tool have changed, with the modified tool exhibiting a larger shear angle compared to the original design. Finite element simulations and experiments demonstrate that grooved tool exhibit optimized cutting mechanics, characterized by a larger shear angle and improved edge sharpness. The shear angle of grooved tool is increased by about 3 degrees and the chip thickness is reduced by about 0.05 mm. Cutting tests confirm that the grooved tool reduces the main cutting force by more than 18%, with a smaller wear area on the face and improved wear conditions near the cutting edge. Due to materials such as stainless steel and titanium alloy, which have similar difficult-to-machine properties. The present results are based on AISI 201 and the specific groove geometry used in this study, and further work is required before generalizing to other difficult-to-cut materials and groove designs. In summary, based on the experimental data, the micro-groove cutting tool outperforms the original tool in terms of shear angle, cutting force, and durability. Specifically, the shear angle of the micro-groove cutting tool is larger, the cutting force is reduced, and the wear on the face is decreased.