Dry cutting is an effective method to realize the concept of green cutting today. However, in the process of cutting bearing steel, the high temperatures and high pressures produced by the cutting tool and chip under dry friction seriously affect the machining performance of the tool. Therefore, a bionic microstructure tool based on bionics is proposed to improve the cutting performance and reduce friction by changing the size parameters of the microstructure. On the basis of finite element simulation and cutting tests, the cutting force, surface roughness, and chip shape are used to evaluate the cutting performance. It is found that composite bionic micro-textured tools have a significantly reduced cutting force compared with non-micro-textured tools; composite bionic micro-textured tools lead to a reduction in surface roughness of 10–25%; and composite bionic micro-textured tools are more prone to enhancing the curling and breaking of chips. In addition, with the increase in the microstructure area occupancy, the cutting performance of the tool was also significantly improved. Moreover, it was found that the cutting performance of the tool was improved when the area occupancy of the micro-texture on the front face of the tool was increased.

The stability and reliability of the brake system are critically affected by the concentricity error of automotive brake piston components. Traditional contact-based concentricity measurement methods are inefficient. To address the issue of low detection efficiency, a non-contact concentricity measurement method based on the combination of machine vision and image processing technology is proposed in this paper. In this method, an industrial camera is utilized to capture images of the measured part's end face from the top of the spring. Edge contours are extracted through image preprocessing algorithms, the outer circle center is calculated, and the inner circle center is fitted. Finally, the concentricity error is calculated using the coordinates of the two circle centers. Experimental results show that, compared to a coordinate measuring machine(CMM), this method has a maximum error of only 0.0393mm and an average measurement time of just 3.9s. It significantly improves measurement efficiency and meets the industry's demand for automated inspection. The experiments verified the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Additionally, this method can be applied to the concentricity measurement of other complex stepped shaft parts.

Initially, a theoretical model of energy under the influence of micro-weave structure on the front and rear faces of the tool is established, and the feasibility of the tool placed in the micro-weave structure is verified. The finite element analysis method will be used to carry out simulation tests on the tools with different parameters of the micro-weave structure on the front face and rear face of the tool. This will allow for the determination of a reasonable range of micro-weave structure parameters. These parameters will then be combined to make the front and rear faces of the micro-weave structure tool. Finally, simulation tests will be carried out. These tests will evaluate the milling force, tool wear, and workpiece surface roughness. Finally, the milling test design, milling force, tool wear, and workpiece surface roughness are used as a criterion to evaluate the milling performance of the front and rear face weaving tool and the front and rear micro-weaving tool compared to the non-weaving tool. The milling of 304 stainless steel can effectively reduce the milling force by 19.4%, reduce the temperature by 19%, and reduce the surface roughness of the workpiece by 43%. It also has a relatively significant effect on reducing tool wear.

To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back cutting face microstructures is established to verify the feasibility of embedding microstructures. Then, finite element analyses are conducted on cutters with varying microstructure parameters on front and back cutting faces to determine reasonable parameter ranges. Parameter combinations are subsequently used to manufacture front/back microstructured cutters, which undergo FEA validation. Finally, milling experiments are designed with milling forces, tool wear, and workpiece surface roughness as evaluation metrics. The results demonstrate that front/back microstructured cutters reduce milling forces by 19.4%, cutting temperatures by 19%, and workpiece surface roughness (Sa) by 43% compared to non-microstructured cutters, while significantly mitigating tool wear.