# Advanced Manufacturing and Precision Machining
## 2 An Overview of the Published Articles  
In recent years, significant progress has been made in the fields of advanced manufacturing and precision machining, with innovative research focusing on the development of novel tools, the design of micro-structured cutting tools, process optimization, and intelligent control systems. These advancements have further driven the evolution of high-performance, high-precision manufacturing technologies, offering crucial technological support to meet the demands of complex machining. To meet the high-precision machining requirements of hard and brittle materials such as single-crystal silicon, researchers have developed a novel micro-grinding tool based on mechano-chemical action, achieving efficient and low-damage machining results. Simulation and experimental studies revealed that a grinding tool composed of 25% cerium oxide abrasive and calcium oxide additives significantly activated solid-solid phase chemical reactions at the tool interface through the synergistic effects of mechanical force and grinding temperature. The softening reaction products generated by this chemical interaction were removed via mechanical friction between the abrasive particles and the silicon wafer surface, enabling low-damage processing. Machining test results demonstrated that the surface roughness of single-crystal silicon reached Ra 1.332 nm using the mechano-chemical grinding tool, a substantial improvement compared to the Ra 96.363 nm achieved with traditional diamond abrasives. Additionally, the tool exhibited significant advantages in material removal efficiency, greatly enhancing the material removal rate while reducing machining damage. This research offers a promising solution for the high-precision, high-surface-quality manufacturing of semiconductors and optical components, marking a significant advancement in machining technologies for hard and brittle materials [19]. Another innovative study focused on the efficient machining of the nickel-based superalloy GH4169, a material well-known for its machining challenges. Researchers developed micro-structured cutting tools by incorporating surface features such as micro-pits, micro-grooves, and elliptical textures, significantly enhancing cutting performance. Using finite element simulations and experimental analysis, the research systematically evaluated the effects of various micro-structure morphologies on the cutting process, examining their influence on friction, heat distribution, and stress during machining. The findings revealed that, compared to untextured tools, micro-structured tools demonstrated superior cutting performance, with reduced cutting temperatures and significantly lower friction coefficients. Moreover, these micro-structures improved the contact behavior between the tool and the chip, enhancing heat dissipation during machining and further improving machining quality. This research provides valuable technical insights into the machining of complex aerospace components and establishes a clear direction for the development of high-performance cutting tools tailored for difficult-to-cut materials [20]. Researchers have also explored advancements in the precision machining of optical components. To meet the high-precision polishing requirements of optical elements, a robust model predictive control (RMPC) system was developed, utilizing a ring-pendulum double-sided polisher. By optimizing the dynamic performance of the radial-feed system, this approach enhanced the stability and precision of the polishing process. The system's effectiveness was validated through simulations and experiments. The experimental resultsdemonstrated that the RMPC system effectively suppressed disturbances in the radial-feed system, significantly improving machining precision. Specifically, the peak-to-valley (PV) error of the polished optical elements decreased from 1.49 \(\lambda\) PV to 0.99 \(\lambda\) PV, while the root mean square (RMS) error was reduced from 0.257 \(\lambda\) RMS to 0.163 \(\lambda\) RMS.