# Advanced Manufacturing Processes
## 2 Overview of Published Articles
Thus, these authors developed surface quality evolution models for assessing surface roughness and glossiness based on an Elman neural network, which builds regressions between process parameters, surface quality indices of the previous grinding pass, and surface quality indices of the current grinding pass. These authors also propose a consistency control method for the surface quality that involves adjusting the actual grinding depth within the dimensional accuracy tolerance range at the rough grinding stage. Their study shows that this proposed consistency control method can guarantee a consistent surface quality, reduce the grinding passes, and increase the grinding efficiency. At a lower scale than in the previous works on subtractive manufacturing, the paper published by [PERSON] et al. (Contribution 8) addresses micro-milling, another particular process within engineering that presents many applications such as terahertz slow-wave structures, microfluidic chips, and micro-molds. These authors indicate that even with this process consuming more energy than traditional milling, research on micro-milling is usually focused on parameter optimization, with limited attention given to energy efficiency. Citing the importance of developing or improving such processes to be more sustainable, the authors propose a micro-milling parameter-based power consumption model for use with evolutionary algorithms to optimize micro-milling parameters. This model allowed them to achieve comprehensive enhancements in both machinability and sustainability, improving surface quality, dimensional accuracy, the material removal rate, and specific energy consumption during the micro-milling process for thin-walled micro-structures. [PERSON] et al. (Contribution 9) also worked on machining, but with a different focus, specifically the use of double-sided collaborative machining. In fact, these authors investigated the tool path in double-sided collaborative machining of a propeller blade. The concomitant vibration and deformation produced by propeller blades in single-sided machining significantly affect the surface machining precision. These authors indicate that double-sided symmetrical machining can improve system rigidity through mutual shoring on both sides, but it cannot be applied to blade machining due to the complexity of a blade's shape. Thus, they investigated a tool-path planning algorithm, showing that the algorithm can achieve smooth fitting and correspondence of bilateral cutter position points through double-curve interpolation and position data alignment. The authors also verified the feasibility and superiority of double-sided collaborative machining through machining experiments. The multidisciplinary nature of the present Special Issue also allowed the inclusion of the work conducted by [PERSON] et al. (Contribution 10). These authors investigated the variations in surface shape during gas jet forming for optical aspherical mirror blanks. The inference was verified using numerical simulation and compared to the experimental results. The authors found that the average prediction deviation for the diameter was 1.07 mm, while the average prediction deviation for the principal curvature was 0.03665 mm\({}^{-1}\), which is challenging to correct in simulation. Therefore, they developed a dimensionless prediction model for forecasting the surface curvature and surface diameter of mirror blanks by considering the jet parameters using experimental data. The model's average prediction error for the surface diameter of the formed surface was found to be 0.3192 mm, and the average prediction error of the principal curvature for the formed surface was 0.00269 mm\({}^{-1}\). The assembly stage of component manufacturing is also addressed in detail in this Special Issue. [PERSON] et al. (Contribution 11), taking the core machine of a micro gas turbine as the research object, conducted a study with the aim of constructing a simulation model for assembly-deviation prediction based on the design tolerance and actual measurement data.