High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis.

To address the complex challenge of identifying the contact state between a shaft and a hole and to improve the efficiency of robotic shaft-hole assembly tasks, a robotic shaft-hole assembly method based on variable admittance control is proposed. In this method, admittance control serves as the foundational force controller for shaft-hole assembly. On this basis, the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm from deep reinforcement learning is utilized to optimize the parameters of the admittance controller. Additionally, a nonlinear reward function is designed, which not only prevents the assembly strategy from converging to local optima but also further accelerates the training speed of the assembly task. Experiments conducted with a collaborative robotic arm performing 15° inclined hole assembly demonstrated that the assembly efficiency of the variable admittance method was 9.6% higher than that of the fixed admittance parameter method, validating the feasibility and effectiveness of the proposed variable admittance shaft-hole assembly method.

Electric ducted fans (EDFs) have emerged as promising propulsion systems due to their compact design, high thrust density, and enhanced operational safety. Accurate prediction of aerodynamic thrust is essential for EDF design and performance evaluation; however, existing numerical studies have not yet provided a systematic comparison of the thrust-prediction capability of different k−ω-based turbulence models in micro-EDF applications. In this study, a dedicated thrust-measurement platform was developed for a 120 mm EDF, and experimental thrust data were obtained under three representative hover operating conditions. Based on these measurements, six turbulence models, including SST, SK$\omega$, BSL, GEKO, EARSM, and SST-$\gamma$(alg.), were evaluated using three-dimensional CFD simulations. The numerical model was assessed through thrust validation, centerline velocity comparison, power-consistency analysis, grid independence verification, and qualitative flow-field interpretation. A two-factor full-factorial analysis was further conducted to quantify the effects of rotational speed and turbulence model on prediction accuracy and computational cost. The results show that the turbulence model has a stronger influence on the normalized thrust-prediction error than the rotational speed factor over the investigated operating range. The SST-$\gamma$(alg.) model achieves the highest thrust-prediction accuracy, with an average relative deviation of 0.47%, but requires the highest computational cost. In comparison, the SST model provides a favorable balance between accuracy and efficiency, with an average relative deviation of 1.79% and an average computation time of 184.33 min, approximately 33% lower than that of the SST-$\gamma$(alg.) model. The centerline velocity and power-consistency results further support the comparative model assessment. Overall, this study provides an experimentally validated comparative reference for turbulence model selection in simulations of similar 120 mm EDF under hover conditions. Considering both prediction accuracy and computational efficiency, the SST model can serve as a practical turbulence model choice for engineering parameter optimization of similar micro-EDF configurations.