In order to solve the problem of poor stability of hard brittle material in cutting process, a 45° groove micro-textured composite bionic tool was proposed. Under the same cutting parameters and conditions, the temperature rise and cutting force can be suppressed, and the tool stability can be reduced. Firstly, the design tool was modeled in three dimensions and the optimal bionic tool structure was optimized by finite element simulation technology. Secondly, four microstructures were constructed on the basis of biomimetic tools, and the optimum microstructures were selected based on shear force, stress distribution and chip discharge capacity. The smallest microstructure size parameters were obtained by changing the spacing of microstructures. Finally, on the basis of optimizing the micro-textured biomimetic tool, the surface of the tool was coated, and the influence of coating material on tool performance was discussed. The results showed that the cutting fracture rate of the material increased and the cutting accumulation phenomenon was alleviated obviously when the 45° chute micro-textured bionic tool with composite coating was used for machining. The cutting stability of the tool was enhanced and chip adhesion was reduced when cutting forces and cutting temperatures were reduced. At the same time, compared with conventional tools, the main cutting force and radial force decreased by 35% and 62%, and the cutting temperature decreased by 3.5%. With the decrease in cutting force and cutting temperature, the stability of cutting was enhanced and the phenomenon of chip adhesion on the tool surface was reduced.

Titanium alloys are widely used in various fields, but milling titanium alloy materials often leads to problems such as high milling forces, increased milling temperatures, and chip adhesion. Thus, the machinability of titanium alloys faces challenges. To improve the milling performance of titanium alloy materials, this study analyzes the effective working area on the surface of the milling cutter through mathematical calculations. We design micro-grooves in this area to utilize their friction-reducing and wear-resisting properties to alleviate the aforementioned issues. The effective working area of the ball end milling cutter’s cutting edge is calculated based on the amount of milling and the installation position between the milling cutter and the workpiece. By observing the surface structure of seashells, micro-grooves are proposed and designed to be applied in the working area of the milling cutter surface. The impact of the micro-groove area on the milling cutter surface and spindle speed on milling performance is discussed based on milling simulation and experimental tests. Experimental results show that the cutting force, milling temperature, and chip resistance to adhesion produced by micro-groove milling cutters are superior to conventional milling cutters. Milling cutters with three micro-grooves perform best at different spindle speeds. This is because the presence of micro-grooves on the surface of the milling cutter improves the friction state, promoting a reduction in milling force, while the micro-grooves also serve as storage containers for chips, alleviating the phenomenon of chip softening and adhesion to the cutter. When conducting cutting tests with a milling cutter that has three micro-grooves, the milling force is reduced by 10% to 30%, the milling temperature drops by 10% to 20%, and the surface roughness decreases by 8% to 12%.

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.

Aluminum alloy materials are very difficult to break in the processing process; chip accumulation leads to poor processing stability. In this paper, a coated composite micro–textured tool is presented. The influence of coated composite micro–textured tool on chip shape and cutting force under the same cutting parameters is studied. Firstly, the combination of cutting force and cutting temperature is optimized based on cutting test. Secondly, combined with the finite element simulation technology, three kinds of composite micro–texture tools are tested to determine the same cutting parameters, tools and workpiece materials, and optimize the composite micro–texture combination from the angle of chip shape, cutting temperature and cutting force. Finally, on the basis of optimizing the microstructure combination, the optimization of the cutting performance of aluminum alloy by coating material is studied. It was found that the chip was more easily broken in the composite microstructure when two tools were used. The cutting force decreased by 25% and the cutting temperature decreased by 9.09% compared with the non–micro–texture tools. The cutting temperature and cutting force decreased by 3% and 4.99% compared with those of uncoated composite microstructure.

Aiming at the problems of the Tyrannosaurus optimization algorithm, of poor search accuracy, insufficient global search capability, and ease of falling into local optimality, a dynamic hierarchical improved Tyrannosaurus optimization algorithm (DHTROA) with hybrid topology structure is proposed. Initially, a chaotic opposition-based learning approach is selected to start the population, ensuring a more uniform distribution of prey across the solution area and boosting population diversity; later, a dynamic hybrid bi-population strategy is introduced to divide the initial population into an ‘advantaged group’ and a ‘disadvantaged group’ to improve the efficiency of individual information exchange. Finally, the ‘advantaged group’ and ‘disadvantaged group’ are hunted synchronously; for the ‘advantaged group’, the position update is carried out using the cellular ring topology strategy, and for the ‘disadvantaged group’, the original algorithm is run in accordance with the main loop process. For the problem of the constant running rate of the Tyrannosaurus in the original algorithm, an adaptive running rate strategy is proposed, which enhances the ability of global optimization, and at the same time, the shortcomings of the original algorithm’s ‘failure’ strategy are improved in order to enhance the original algorithm to jump out of extrema. DHTROA was tested for performance with nine optimization algorithms in different dimensions of the CEC2017 test function. The efficiency of these enhancements was confirmed through the Wilcoxon rank sum test and Friedman test, while DHTROA was utilized for six engineering optimization challenges of differing complexities. The experimental results show that DHTROA has improved greatly in convergence speed, optimality search accuracy, global search ability, and stability, and the excellent engineering optimization performance also proves the excellent robustness of DHTROA.

The concentricity error of automotive brake piston components critically affects the stability and reliability of the brake system. Traditional contact-based concentricity measurement methods are inefficient. In order to address the issue of low detection efficiency, this paper proposes a non-contact concentricity measurement method based on the combination of machine vision and image processing technology. In this approach, an industrial camera is employed to capture images of the measured workpiece’s end face from the top of the spring. The edge contours are extracted through the implementation of image preprocessing algorithms, which are then followed by the calculation of the outer circle center and the fitting of the inner circle center. Finally, the concentricity error is calculated based on the coordinates of the inner and outer circle centers. The experimental results demonstrate that, in comparison to a coordinate measuring machine (CMM), this method exhibits a maximum error of only 0.0393 mm and an average measurement time of 3.9 s. This technology markedly enhances the efficiency of measurement and fulfills the industry’s requirement for automated inspection. The experiments confirmed the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Moreover, this methodology can be extended to assess concentricity in other complex stepped shaft parts.