To improve medical device hole inner wall quality and overcome issues of traditional abrasive flow methods—limited fluidity in small holes causing deformation due to high inner wall pressure, and slow processing with low viscosity abrasives—a new method called shear thickening abrasive flow polishing is suggested. It uses shear thickening fluid as the medium. By leveraging the Preston equation and fluid dynamics theory, this study establishes both an abrasive flow dynamics model and a material removal model for the shear thickening abrasive flow machining of small titanium alloy hole workpieces in medical instruments. Utilizing the COMSOL software, the flow field state of shear thickening fluid within small holes is examined under varying flow behavior indexes and flow velocities. The findings demonstrate that shear thickening fluid yields superior polishing effects compared to Newtonian fluid. Elevating the flow behavior indexes facilitates a higher material removal rate on the inner wall surface; however, excessively large flow behavior indexes diminish the uniformity of material removal, thereby hindering the attainment of a high-quality polished surface. Furthermore, excessively large flow behavior indexes can reduce fluidity and consequently lower the efficiency of the polishing process. Conversely, while maintaining a constant flow behavior index, increasing the flow velocity contributes to an enhanced material removal rate and improved polishing efficiency. Nevertheless, as the flow velocity rises, the uniformity of inner wall surface roughness diminishes, posing challenges in achieving a high-quality polished surface.

Aerostatic bearings are considered crucial components that can improve the measurement accuracy of ground simulation tests of space equipment. A structural optimization design method is proposed to enhance the static performance of aerostatic bearings. A mathematical model which can quickly calculate the aerostatic bearing capacity and gas consumption is established, and the influence of structural parameters on bearing performance is analyzed using simulation software. By comparing the convergence time and convergence results of the algorithm using different initialization methods, the Latin hypercube initialization method is selected instead of the random initialization method. The multi-objective particle swarm optimization algorithm is used to obtain the optimal solution set distributed in the objective space. It is found that the optimized structural parameters meet the requirements of improving the capacity and reducing gas consumption, which verifies the method’s effectiveness in designing the structural parameters of aerostatic bearings.

The multi-dimensional optimization of mechanisms is a typical optimization problem encountered in mechanical design. Herein, the Hybrid strategy improved Beetle Antennae Search (HSBAS) algorithm is proposed to solve the multi-dimensional optimization problems encountered in structural design. To solve the problems of local optimization and low accuracy of the high-dimensional solution of the Beetle Antennae Search (BAS) algorithm, the algorithm adopts the adaptive step strategy, multi-directional exploration strategy, and Lens Opposition-Based Learning strategy, significantly reducing the probability of the algorithm falling into the local optimum and improving its global search capability. Comparative experiments of the improved algorithm are carried out by selecting eleven benchmark test functions. HSBAS can reach 1 × 10⁻²² accuracy from the optimal value when dealing with low-dimensional functions. It can also obtain 1 × 10⁻² accuracy when dealing with high-dimensional functions, significantly improving the algorithm’s capability. According to Friedman’s ranking test result, HSBAS ranks first, which proves that HSBAS is superior to the other three algorithms. The HSBAS algorithm is further used to optimize the design of the altitude compensation module of the gravity compensation device for solar wings, controlling the fluctuation of bearing capacity within 0.25%, which shows that the algorithm can be used as an effective tool for engineering structural optimization problems.