Min Wang’s research while affiliated with Beijing University of Technology and other places

The surface texture technology has been applied to ball screws. However, the rough grinding surface of ball screws is not considered, and the elastohydrodynamic lubrication (EHL) characteristics and anti-friction and anti-wear mechanisms are not comprehensive and in-depth. Theoretical simulation and experimental measurement of the ground surface topography of the screw raceways are conducted to take into account the impact of the grinding surface on the EHL interaction between the ball and the raceway. The EHL model and friction torque model of ball screws have been established simultaneously, considering the ground surface topography of the raceway and the geometric features of the textures manufactured on the raceway surface. The friction reduction mechanism of the textured raceway of ball screws is elucidated in detail from the microscopic point of view, and the influence of the geometric features of the textures on the anti-friction characteristics of ball screws under different axial loads and rotation speeds is further analyzed and discussed. The proof-of-principle experiments of the friction-reducing performances of the textured raceways of the ball screws are conducted. The textured raceway of the ball screws provides an effective anti-friction effect that reduces the friction coefficient of the contact system of the ball screws by 15.2% at a normal contact force of 60.23 N, an entrainment speed of 167.5 m/s, a texture diameter of 40 μm, a texture depth of 10 μm and a texture areal density of 10%.

Control charts, as essential tools in Statistical Process Control (SPC), are frequently used to analyze whether production processes are under control. Most existing control chart recognition methods target fixed-length data, failing to meet the needs of recognizing variable-length control charts in production. This paper proposes a variable-length control chart recognition method based on Sliding Window Method and SE-attention CNN and Bi-LSTM (SECNN-BiLSTM). A cloud-edge integrated recognition system was developed using wireless digital calipers, embedded devices, and cloud computing. Different length control chart data is transformed from one-dimensional to two-dimensional matrices using a sliding window approach and then fed into a deep learning network combining SE-attention CNN and Bi-LSTM. This network, inspired by residual structures, extracts multiple features to build a control chart recognition model. Simulations, the cloud-edge recognition system, and engineering applications demonstrate that this method efficiently and accurately recognizes variable-length control charts, establishing a foundation for more efficient pattern recognition.

The cutting force coefficients are important parameters in the milling process, which not only affects the milling stability but also directly reflects the tool wear state. This paper proposed a method for online identification of cutting force coefficients and prediction of tool wear state based on the current and voltage of the feed drive. The method is based on the instantaneous electromagnetic torque theory and utilizes a neural network to establish a complex mapping relationship between current, voltage, and instantaneous milling force, achieving high-precision online prediction of the milling force waveform during the milling process. Then, a method for online identification of cutting force coefficients and prediction of tool wear state is proposed by integrating the predicted milling force with machining information. Through the experiments on different cutting conditions and materials, the results show that the proposed method can identify the cutting force coefficients under different machining conditions by fully training the prediction model and predicting the wear state of milling cutters by online monitoring the variation trend of cutting force coefficients along with the wear process developing, and accurately determine the key points of milling cutters replacement.

The time-varying lateral vibration of the radial ribs induced by the deployment movement of the satellite antenna has adverse effects on its surface accuracy and fatigue life. This paper utilizes the method of a hollow component of the radial ribs containing multi-petal friction dampers (MPFD) with high energy dissipation and a wide frequency band to achieve lateral time-varying vibration control of the satellite antenna. Firstly, the dynamic model of the loss factor of the oscillation system is developed, which takes advantage of friction loss energy of nonlinear structure damping. After establishment of the lateral dynamic model of the component installed with the MPFD, the effects of the thickness and the number of petals on the optimal design parameters of the MPFD are analyzed based on the optimization algorithm. Subsequently, the transfer matrix method is used to establish a dynamic sub-scale model of the component of the radial ribs. The comparative damping validation experiments are performed to measure the loss factor of the lateral oscillation system subjected to dynamic excitation. Finally, the simulations investigate the multiple resonance response of the radial ribs installed with optimally designed MPFDs to demonstrate the effectiveness of suppressing the time-varying lateral vibration after and during the antenna deployment. Theoretical studies and experimental results show that the proposed design method of the MPFD has a significant effect on suppressing the lateral time-varying and multi-mode lateral vibration of the satellite antenna.

Additive manufacturing has been widely used in industries. The plasticity and fatigue behaviors of AlSi10Mg alloy parts manufactured by selective laser melting (SLM) are generally poor. Thus, this paper studies the microstructure and mechanical properties of SLM-manufactured AlSi10Mg alloy under aging, annealing, solution and T6 heat treatments. The results demonstrate that these heat treatments enhance the plasticity of the material, especially for the T6 treatment, which exhibits a plasticity 3.2 times greater than that of the as-deposited alloy. However, the tensile and yield strengths of the AlSi10Mg alloy decrease. The T6 treatment exhibits the highest fatigue life, which is five times higher than that of the as-deposited alloy. The microstructure of the annealing treatment consists of reticulated eutectic Si, while the aging treatment displays reticulated Si fractures and an increased presence of spherical Si particles. In contrast, the solution and T6 treatments exhibit blocky Si structures and micropores. The microstructural alteration of the silicon state after heat treatment is attributed to the enhanced fatigue life.

Multi-tooth milling cutters have the advantages of minimal surface damages and low cutting forces and are widely used in composite materials milling such as carbon fiber and wood. Cortical bone is a composite biomaterial similar to carbon fiber and wood; however, the cutting performance of multi-tooth milling cutters on bone cutting is unclear. Here, this paper proposes a multi-tooth milling cutter longitudinal torsional ultrasonic vibration assisted milling (LTVUM) method for cortical bone low damage cutting. The cutting performance of sinusoidal and corn edge cutters for cortical bone LTVUM was studied, and its material removal mechanisms were also established. The results indicated that the multi-tooth milling cutter has a smaller average cutting force, and suppressing the chip burrs and tearing damages. Compared to straight edge cutter, the resultant cutting forces of sinusoidal and corn edge cutters are decreased by 52.58%–61.01% and 45.60%−65.96%. The reason is that the secondary cutting edge of multi-tooth cutter can participate in cutting, reducing the tool squeeze interference. This work provides experimental guidance for the application of multi-tooth milling cutters LTVUM in bone cutting.

In orthopedic surgery, cortical bone cutting usually involves washing and cooling with physiological saline. However, how the saline changes the cutting behaviors of bone ultrasonic vibration cutting remains challenging. Hence, this paper simulates the clinical ultrasonic cutting condition in orthopedics to reveal the cutting behaviors of bone ultrasonic vibration orthogonal cutting immersed in physiological saline. The dynamic equation and motion process curves of ultrasonic cavitation bubbles were established. The results showed that the bone cutting immersed in physiological saline significantly improved the surface quality, reduced surface roughness and mechanical damage, and avoided large brittle cracks propagation. In saline immersed cutting, the physiological saline changes the mechanical behaviors of bone materials, resulting in plastic behaviors for the material removal and crack deflection. This study establishes the influence of physiological saline on the ultrasonic vibration cutting performance, providing guidance for orthopedic bone cutting surgery methods.

Polyamide is a light weight and high strength polymer material and it has been widely used in light equipments as hinge joint. The reliability and service life of polyamide hinge joint are crucial for its application. This paper investigates the static and fatigue behaviors of polyamide 12 hinge joint. The polyamide 12 hinge joint specimens were manufactured using multi-jet fusion (MJF). The fatigue experiments of polyamide joint under different stress levels were conducted, and the fatigue life S-N curve of the joint specimens was obtained. The structural stress of the joint was also analyzed by finite element method. The results indicated that the fatigue fractures of polyamide hinge joints occur at the side of the hole in the transverse direction. The ultimate tensile and fatigue strengths of the polyamide hinge joints are 41 MPa and 11.4 MPa, respectively. The fatigue striations are observed on the fracture surface, and the fatigue initiation starts from microvoids. The fatigue cracks tend to occur in sintered interfaces, where the crosslinked molecular chains are easily broken. This paper provides support for the design and application of polyamide hinge joint manufactured using MJF.

The ball screw is a vital component in the feed drive systems of machine tools. It is susceptible to thermal errors that significantly impact its accuracy. Current thermal error modeling methods for ball screws face significant challenges in achieving full-time series prediction. Furthermore, these methods impose stringent requirements for a complex temperature data collection process, further constrained by the compact structure of machine tools. Additionally, valuable working condition data in thermal error prediction remains underutilized. This paper proposes a new hybrid-driven model that combines mechanism and data-driven approaches to achieve full-time series thermal error prediction of ball screws. The proposed model utilizes the operating rotational speed as a key input parameter, eliminating the need for temperature collection during the modeling stage and the compensation process. The temperature model is proposed as a mechanism-driven model based on heat transfer theory to calculate the temperature of the thermal sensitive points by utilizing operating rotational speed. The accuracy of the model is validated through thermal characteristic experiments of ball screws under four different working conditions. The data-driven models based on different traditional neural networks are established to predict thermal errors according to the time series temperature data from the temperature model. Moreover, hyperparameters of different neural networks are optimized by the Beetle Antennae Search (BAS). Comparative analysis among different neural network-based hybrid-driven models reveals that the convolutional neural network (CNN) model optimized by BAS consistently exhibits lower absolute errors predominantly below 10 μm, as well as lower root mean squared error (RMSE) and mean absolute error (MAE) values in each working condition. The BAS-CNN model, within the hybrid-driven model framework, is better suited for the full-time series prediction of thermal errors in ball screws. The BAS-CNN model is a foundation for thermal error compensation by utilizing working condition data.