BK7 glass, with its outstanding mechanical strength and optical performance, plays a crucial role in many cutting-edge technological fields and has become an indispensable and important material. These fields have extremely high requirements for the surface quality of BK7 glass, and any small defects or losses may affect its optical performance and stability. However, as a hard and brittle material, the processing of BK7 glass is extremely challenging, requiring precise control of machining parameters to avoid material fracture or excessive defects. Therefore, how to obtain the required surface quality with lower cost machining techniques has always been the focus of researchers. This article introduces the properties, application background, machining methods, material removal mechanism, and surface and subsurface damage of optical glass BK7 material. Finally, scientific predictions and prospects are made for future development trends and directions for improvement of BK7 glass machining.

Powder metallurgy (PM) technology is extensively employed in the manufacturing sector, yet its processing presents numerous challenges. To alleviate these difficulties, green machining of PM green compacts has emerged as an effective approach. The aim of this research is to explore the deformation features of green compacts and assess the impact of various machining parameters on the force of cutting. The cutting variables for compacts of PM green were modeled, and the cutting process was analyzed using Abaqus (2022) software. Subsequently, the orthogonal test ANOVA method was utilized to evaluate the significance of each parameter for the cutting force. Optimization of the machining parameters was then achieved through a genetic algorithm for neural network optimization. The investigation revealed that PM green compacts, which are brittle, undergo a plastic deformation stage during cutting and deviate from the traditional model for brittle materials. The findings indicate that cutting thickness exerts the most substantial influence on the cutting force, whereas the speed of cutting, the tool rake angle, and the radius of the rounded edge exert minimal influence. The optimal parameter combination for the cutting of PM green compacts was determined via a genetic algorithm for neural network optimization, yielding a cutting force of 174.998 N at a cutting thickness of 0.15 mm, a cutting speed of 20 m/min, a tool rake angle of 10°, and a radius of the rounded edge of 25 μm, with a discrepancy of 4.05% from the actual measurement.

Microtextured cutting tools are widely used because of their excellent performance in cutting difficult-to-machine materials. The cutting performance of cutting tools largely depends on the size parameters of the microtextures used. This study focuses on the machining of titanium alloy Ti-6Al4 V using microgrooved cutting tools under dry-cutting conditions. Special emphasis is placed on exploring cutting performance under specific combinations of microgroove parameters. To determine the optimal parameter combination for cutting, the effects of different microgroove parameters (including the diameter, depth, spacing, and spacing between grooves and cutting edges) on cutting force, tool wear, and chip morphology were investigated. In this study, femtosecond laser technology was used to prepare microgroove-textured cutting tools with different parameters, and the cutting performance of these tools was analyzed. The results show that, when the groove diameter is 80 μm, the depth is 60 μm, the spacing is 80 μm, and the distance between the groove and the tool tip is 120 μm, the cutting performance of the tool is optimal: the cutting force is reduced by 13.9%, the degree of tool wear is minimized, and the degree of chip curling is more uniform. The research results can be applied to the actual processing of Ti-6Al4 V, which can help tool design, selection, and microtexture parameter optimization.