Titanium alloy material has physical properties such as low thermal conductivity, high hardness, and surface resilience, which are prone to problems such as large milling force, low machining efficiency, and poor surface quality in processed products during dry milling. This document details our process of isolating micro-textures from biological structures, applying them to cutting tool surfaces to create micro-texture milling cutters, and employing this micro-texture technique to reduce friction and prevent wear on these cutters. According to the milling dosage and the installation position between the tool and the workpiece, the effective working area of the cutting edge of the ball-end milling cutter is calculated. At the same time, a self-lubricating cutter was constructed by using a laser to process micro-textures and filling solid lubricant inside the micro-textures. An analysis was conducted to compare the milling efficiency of bionic microtextured cutters in both dry and micro-lubricated environments. It was found that the self-lubricating tool promoted a 3% to 5% decrease in milling force, a reduction in the coefficient of friction, a high surface finish of the machined workpiece, and an alleviation of chip sticking at the edge area.

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.

Aiming at addressing the problem of the online detection of automobile brake piston components, a non-contact measurement method based on the combination of machine vision and image processing technology is proposed. Firstly, an industrial camera is used to capture an image, and a series of image preprocessing algorithms is used to extract a clear contour of a test piece with a unit pixel width. Secondly, based on the structural characteristics of automobile brake piston components, the region of interest is extracted, and the test piece is segmented into spring region and cylinder region. Then, based on mathematical morphology techniques, the edges of the image are optimized. We extract geometric feature points by comparing the heights of adjacent pixel points on both sides of the pixel points, so as to calculate the variation of the spring axis relative to the reference axis (centerline of the cylinder). Then, we extract the maximum variation from all images, and calculate the coaxiality error value using this maximum variation. Finally, we validate the feasibility of the proposed method and the stability of extracting geometric feature points through experiments. The experiments demonstrate the feasibility of the method in engineering practice, with the stability in extracting geometric feature points reaching 99.25%. Additionally, this method offers a new approach and perspective for coaxiality measurement of stepped shaft parts.

The success of the simulator’s motion platform hinges on the washout algorithm; incorrect choice of its filter parameters can lead to issues like phase delay and reduced space consumption. Consequently, the study focuses on enhancing the sparrow search algorithm (Tent Golden Sparrow Search Algorithm (TGSSA)) to fine-tune the filter settings of the washout algorithm. Initially, Tent mapping is implemented to start the population, enhancing its diversity, hastening the algorithm’s convergence, and boosting convergence precision; secondly, to address the issue where altering each sparrow discoverer and follower results in a halt in the local population’s quest for the best solution towards the algorithm’s end, Cauchy-Gaussian variance factors are incorporated during the follower position update, enhancing the algorithm’s overall search efficiency. An optimization model is established to compare the filter parameters optimized by TGSSA with those optimized by other intelligent optimization algorithms. The results show that the new parameters obtained by the improved sparrow search algorithm enable the simulator motion platform to recover to the neutral position in a shorter time, improve the utilization of the motion platform, and reduce the phase delay.