To realize high efficiency, low/no damage and high precision machining of tungsten carbide used for lens mold, a high frequency ultrasonic vibration cutting system was developed at first. Then, tungsten carbide was precisely machined with a polycrystalline diamond (PCD) tool assisted by the self-developed high frequency ultrasonic vibration cutting system. Tool wear mechanism was investigated in ductile regime machining of tungsten carbide. The cutter back-off phenomenon in the process was analyzed. The subsequent experimental results of ultra-precision machining with a single crystal diamond tool showed that: under the condition of high frequency ultrasonic vibration cutting, nano-scale surface roughness can be obtained by the diamond tool with smaller tip radius and no defects like those of ground surface were found on the machined surface. Tool wear mechanisms of the single crystal diamond tool are mainly abrasive wear and micro-chipping. To solve the problem, a method of inclined ultrasonic vibration cutting with negative rake angle was put forward according to force analysis, which can further reduce tool wear and roughness of the machined surface. The investigation was important to high efficiency and quality ultra-precision machining of tungsten carbide.

Natural frequency and frequency response are two important indicators for the performances of resonant microelectromechanical systems (MEMS) devices. This paper analytically and numerically investigates the vibration identification of the primary resonance of one type of folded-MEMS comb drive resonator. The governing equation of motion, considering structure and electrostatic nonlinearities, is firstly introduced. To overcome the shortcoming of frequency assumption in the literature, an improved theoretical solution procedure combined with the method of multiple scales and the homotopy concept is applied for primary resonance solutions in which frequency shift due to DC voltage is thoroughly considered. Through theoretical predictions and numerical results via the finite difference method and fourth-order Runge-Kutta simulation, we find that the primary frequency response actually includes low and high-energy branches when AC excitation is small enough. As AC excitation increases to a certain value, both branches intersect with each other. Then, based on the variation properties of frequency response branches, hardening and softening bending, and the ideal estimation of dynamic pull-in instability, a zoning diagram depicting extreme vibration amplitude versus DC voltage is then obtained that separates the dynamic response into five regions. Excellent agreements between the theoretical predictions and simulation results illustrate the effectiveness of the analyses.

The 7075-T7451 aluminum alloy, widely used in aerospace, aviation, and automotive fields for critical load-bearing components due to its excellent mechanical properties, suffers from residual stresses induced by thermo-mechanical coupling during milling, which deteriorate workpiece performance. This study explores how key milling parameters—spindle speed *n_c*, feed per tooth *f_z*, cutting depth *a_p*, and cutting width *a_e*—affect surface residual stress and cutting force via orthogonal experiments and finite element analysis (FEA). Results show *a_e* is critical for X-direction residual stresses, while *f_z* dominates Y-direction ones. Cutting force increases with *f_z*, *a_p*, and *a_e* but decreases with higher *n_c*. Multivariate regression-based prediction models for residual stress and cutting force were established, which effectively characterize parameter–response relationships with maximum prediction errors of 18.69% (residual stress) and 12.27% (cutting force), showing good engineering applicability. The findings provide theoretical and experimental foundations for multi-parameter optimization in aluminum alloy milling and residual stress/cutting force control, with satisfactory practical effectiveness.

This paper attempts to qualitatively identify the static pull-in position, pull-in voltage, and fundamental frequency of one-electrode microresonators from a physical perspective. During theoretical derivation, a generalized one-degree-of-freedom (1-DOF) model in nondimensional form derived using the differential quadrature method (DQM) is first introduced and then transformed for frequency normalization. Based on the deduced formulas, the upper and lower bounds of the static pull-in position and pull-in voltage are both deduced through mathematical proof. To distinguish the monotonic and nonmonotonic behavior of the fundamental frequency versus direct current (DC) voltage, a critical condition decided only by cubic stiffness is then determined. For the first time, two extreme static positions, as well as the corresponding fundamental frequencies and DC voltages to identify different frequency behaviors are derived, and their variations versus cubic stiffness are then discussed and verified. During the simulation process, a high-order DQM and COMSOL 2D model are both applied for numerical analyses. Guided by nondimensional results, typical behaviors with specific physical parameters are examined in detail. Results demonstrate that the curve tendencies between all the qualitative results and quantitative numerical simulations in dimensional form agree well with each other, implying the possibility of using 1-DOF model to qualitatively discuss physical parameters effects on the system statics and dynamics.

The prediction of remaining tool life is critical in practical manufacturing, as it enables early identification of tool wear conditions, preventing downtime and quality issues caused by tool failure. This, in turn, helps reduce costs and improve production efficiency. To address the limitations of traditional models in modeling complex temporal dependencies and capturing critical moment information, a hybrid deep learning model based on a TCN-BiLSTM-Attention architecture was proposed. The model first enhanced low-level features from raw multivariate signals using an initial feature extraction module. Local temporal patterns were then extracted by an improved Temporal Convolutional Network (TCN), followed by a Bidirectional Long Short-Term Memory (BiLSTM) network to capture global sequential dependencies. An attention mechanism was further introduced to strengthen the model’s ability to focus on and select critical features over time, enabling deeper fusion and modeling of complex temporal dynamics. Finally, a multi-layer regression network was applied to predict the remaining tool life. Experimental validation on the PHM 2010 dataset demonstrates that the proposed model outperforms other comparative models. Among the three evaluation metrics (RMSE, MAE, and R²), the optimal results are 0.0184, 0.0127, and 0.9959 respectively, which fully proves that the model exhibits excellent prediction performance. Ablation studies further confirm the contribution of each module to the overall performance improvement.

Thin-walled partition frame parts are key load-bearing components in aerospace structures. Machining deformation directly affects assembly accuracy and service reliability. During milling, the release of residual stress caused by material removal is one of the main factors leading to deformation of thin-walled parts. To address this problem, aluminum alloy thin-walled partition frame parts are taken as the research object. A machining accuracy control method based on stress-sensitive region analysis is proposed. Key machining accuracy is used as the constraint condition. The concepts of stress-sensitive regions and sensitive directions are introduced. A coupled analysis model of residual stress and machining deformation is established. Residual stress is applied to element meshes in a finite element analysis platform and released under a free state. The influence of residual stress in different regions on part deformation is qualitatively identified. The dominant deformation directions are also determined. Based on these results, the milling tool path is specifically optimized. Strategies are adopted to avoid highly stress-sensitive regions or to control residual stress release by region. Overall machining deformation is effectively reduced. Experimental results show that the optimized tool path significantly suppresses part deformation compared with the conventional tool path. The flatness of the bottom surface is improved by up to 25.33%. The proposed method provides a feasible approach for machining process optimization of aerospace thin-walled parts with high precision.