Jieqiong Lin

Precision glass molding (PGM) is a near-net-shape process for fabricating high-precision glass optical components, whose replication accuracy depends on coupled thermal, force, displacement, and pressure-holding conditions. To support process-parameter optimization and micro-optical structure replication, an open-architecture, research-oriented molding apparatus was designed, constructed, and evaluated. The compact system integrates short-wave infrared heating, independently regulated upper and lower thermal zones, vacuum/nitrogen atmosphere control, water-cooled seal protection, and force/displacement feedback. Thermal stability tests showed that under a 600 °C holding condition, the deviations of the upper and lower mold-base temperatures from the set temperature eventually converged to within 7 °C, while the two mold bases temperature difference was reduced to within ±1 °C during the stable stage. Both mold bases were maintained above 700 °C for more than 600 s in the ultimate heating test. Mechanically, the apparatus separates chamber actuation from molding loading. A 400 mm electric vertical linear guide opens, closes, and seals the vacuum heating chamber, whereas the lower loading shaft provides a 48 mm loading travel. Micro-displacement control tests showed a minimum return positioning error of 1.226 μm over a 200 μm total travel. Force feedback is provided by an external 5 kN pressure sensor with 0.05% full-scale accuracy, while the actual force-tracking behavior was shown to depend on target force and pressure-holding regulation speed rather than on a single universal control-accuracy value. Finally, an As2Se3 chalcogenide glass molding experiment was conducted, confirming that the apparatus can serve as an open research-oriented platform for PGM process studies.

SiCp/Al is widely used in the manufacturing of high-end equipment, but it is difficult to process and poor in surface quality. Conversely, Quasi-intermittent Vibration Assisted Swing Cutting (QVASC) combines the intermittent cutting mechanism of EVC (Elliptical Vibration Cutting) while effectively reducing cutting forces via its unique offset swing motion. This study undertakes detailed analysis of the kinematics and friction characteristics intrinsic to QVASC. A predictive model for cutting temperature is subsequently developed, incorporating the influence of reinforcing particles. This model primarily ascribes the generated heat to two sources: the shear heat source and the frictional heat source, further examining resultant temperature distributions in the tool, workpiece, and chip alongside corresponding heat partition ratios. Single-factor experiments were conducted with spindle speed, cutting depth, frequency, amplitude, and feed amount as parameters. Theoretical and experimental cutting temperatures are compared, revealing consistent trends. Calculated average relative errors for these parameters are 4.65%, 9.50%, 3.32%, 2.58%, and 3.81% respectively. These errors lie within acceptable limits, validating the theoretical model’s accuracy. Consequently, this research provides valuable insights into the QVASC cutting mechanism and offers an efficient approach for determining optimal cutting parameters.

This paper presents a self-powered piezoelectric wearable sensor (PWS) with motion recognition capability to harvest stepping energy during human movement by placing it in the sole. In this device, two modules are designed to achieve different functions. The piezoelectric sensing module consists of rectangular piezoelectric sheets combined with the connector. Monitoring of motion is achieved by the longitudinal displacement stretching of four rectangular piezoelectric sheets. The power generation module is composed of circular piezoelectric sheets and contact elements, and the circular piezoelectric sheets are directly excited by the designed contact blocks to maximize the voltage output of the device. After the experimental test, it was found that when the prototype is at the installation angle of 45° and the contact block is the surface contact, the output performance and sensing performance of the prototype are the best. The maximum output power of the prototype is 264.1mW, and it has high sensitivity as a sensor. With the help of the MATLAB neural network module combined with the output signal of the piezoelectric module, the function of motion recognition is realized, and the accuracy is 87.5%， providing innovative ideas for the development of wearable devices in the field of motion recognition.

Aluminum-based silicon carbide (SiCp/Al) is a hard-to-process material. SiC particles are randomly distributed and have a unique structure, thus posing challenges during processing. These distinctions considerably affect the overall quality of machining. As the volume fraction increases, the machinability continues to decline. Understanding the removal mechanism of SiCp/Al composites is essential for improving their machined surface quality. This study explores the influence of vibration frequency on the removal mechanism and plastic deformation in high-volume fraction SiCp/Al composites using non-resonant vibration-assisted scratching (NVAS) experiments combined with molecular dynamics (MD) simulations. The experimental results show that compared with conventional scraping (CS), increasing the vibration frequency in the NVAS process significantly expands the plastic removal area and reduces the scraping force. The simulation results indicate that as vibration frequency rises, the smoothness of the scratched groove improves, leading to a more uniform distribution of dislocations and a significant reduction in dislocation loops and HCP structures, promoting plastic deformation of the material. The simulation explains and clarifies the occurrence of plastic deformation observed during the scratching experiments. This study can provide a potential understanding of non-resonant vibration-assisted high-volume SiCp/Al composites machining.

A rotary vibration-assisted polishing device (RVAPD) is designed to enhance polishing force by converting PZT’s linear motion into the rotary motion of a central platform via a flexible mechanism, improving material surface quality. The RVAPD is optimized, simulated, and tested to meet high-frequency and large-amplitude non-resonant vibration polishing requirements. Its structure, designed using theoretical models and finite element software, offers a wide range of polishing parameters. Performance parameters are validated through open-loop tests, confirming effectiveness in polishing experiments. The lever mechanism and Hoeckens connection enhance vibration parameters and motion efficiency, reducing surface flaws in SiC and improving uniformity. Adjusting the RVAPD structure and using the proposed method significantly improve SiC surface quality.

As an essential link in ultra-precision machining technology, various new surface polishing technologies and processes have always attracted continuous in-depth research and exploration by researchers. As a new research direction of ultra-precision machining technology, magnetorheological polishing technology has become an important part. The polishing materials and magnetorheological fluids involved in the process of magnetorheological polishing are reviewed. The polishing principle, equipment development, theoretical research and process research of magnetorheological polishing technologies, such as the wheel-type, cluster-type, ball-type, disc-type and other types, derived from the magnetorheological polishing process, are reviewed. The above magnetorheological polishing technologies are analyzed and compared from the perspective of processing accuracy, processing efficiency and application range. The curvature adaptive magnetorheological polishing technology with a circulatory system is proposed to achieve high efficiency and high-quality polishing.