Youzhi Fu

Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby improving cutting performance and extending tool life. A discrete element method (DEM) model of drag finishing edge preparation was developed to investigate the effects of processing time, tool rotational speed, and rotation direction on abrasive-mediated tool wear behavior. The model was validated through milling cutter edge preparation experiments. Simulation results show that increasing the processing time causes fluctuating changes in average abrasive velocity and contact forces, while cumulative energy and tool wear increase progressively. Elevating tool rotational speed increases average abrasive velocity, contact forces, cumulative energy, and tool wear. Rotation direction significantly impacts tool wear: after 2 s of clockwise (CW) rotation, wear reached 1.45 × 10⁻⁸ mm; after 1 s of CW followed by 1 s of counterclockwise (CCW) rotation, wear was 1.25 × 10⁻⁸ mm; and after 2 s of CCW rotation, wear decreased to 1.02 × 10⁻⁸ mm. Experiments, designed based on simulation trends, confirm that edge radius increases with time and tool rotational speed. After 30 min of processing at 60, 90, and 120 rpm, average edge radius increased to 22.5 μm, 28 μm, and 30 μm, respectively. CW rotation increased the edge shape factor K, while CCW rotation decreased it. The close agreement between experimental and simulation results confirms the model’s effectiveness in predicting the impact of edge preparation parameters on tool geometry. Rotational speed control optimizes edge preparation efficiency, the predominant tangential cumulative energy reveals abrasive wear as the primary material removal mechanism, and rotation direction modulates the shape factor K, enabling symmetric edge preparation.

Gallium nitride (GaN), as the core material of third-generation semiconductors, has important applications in high-temperature, high-frequency, and high-power devices, but its polishing process faces many challenges. In this work, a multifield synergistic material removal model is established to study the material removal behaviour by ultrasonic magnetorheological chemical compound polishing (UMCP) of gallium nitride wafers, and the polishing processing under different polishing solution compositions and processing conditions is used to examine the effects of the ultrasonic, chemical, and mechanical effects on the material removal rate. The results show that mechanical removal dominates during UMCP, the chemical enhancement is slightly greater than the ultrasonic action, and the synergistic interaction between the range of factors promotes better removal of the GaN materials. The percentage of mechanical removal by abrasives is about 25% to 44.63%, the mechanical removal by magnetorheological effect polishing pads is about 14.66% to 23.94%, the removal due to chemical action is about 15.52% to 23.41%, the removal due to ultrasonic action is about 11.73% to 14.66%, and the percentage of interactive removal is 6.47% to 14.36%. The abrasive composition significantly enhances the mechanical removal effect, and a higher abrasive concentration correlates to a stronger mechanical removal effect. The concentration of hydrogen peroxide has a superior effect on the chemical reaction, and too high or too low a concentration of hydrogen peroxide weakens the chemical action effect. The results of the study can provide a basis for further research on the material removal mechanism of the GaN UMCP process.

Magnetorheological–chemical-polishing tests are carried out on single-crystal silicon carbide (SiC) to study the influence of the process parameters on the polishing effect, predict the polishing results via a back propagation (BP) neural network, and construct a model of the processing parameters to predict the material removal rate (MRR) and surface quality. Magnetorheological–chemical polishing employs mechanical removal coupled with chemical action, and the synergistic effect of both actions can achieve an improved polishing effect. The results show that with increasing abrasive particle size, hydrogen peroxide concentration, workpiece rotational speed, and polishing disc rotational speed, the MRR first increases and then decreases. With an increasing abrasive concentration and carbonyl iron powder concentration, the MRR continues to increase. With an increasing machining gap, the MRR shows a continuous decrease, and the corresponding changes in surface roughness tend to decrease first and then increase. The prediction models of the MRR and surface quality are constructed via a BP neural network, and their average absolute percentage errors are less than 2%, which is important for the online monitoring of processing and process optimisation.