Ceramic matrix composites have the advantages of low density, high specific strength, high specific die, high-temperature resistance, wear resistance, chemical corrosion resistance, etc., which are widely used in aerospace, energy, transportation, and other fields. CMCs have become an important choice for engine components and other high-temperature component manufacturing. However, ceramic matrix composite is a kind of multi-phase structure, anisotropy, high hardness material, due to the brittleness of the ceramic matrix, the weak bonding force between fiber and matrix, and the anisotropy of composite material. Burr, delamination, tearing, chips, and other surface damage tend to generate in the machining, resulting in surface quality and strength decline. This paper reviewed the latest abrasive machining technology for SiC ceramic composites. The characteristics and research directions of the main abrasive machining technology, including grinding, laser-assisted grinding, ultrasonic-assisted grinding, and abrasive waterjet machining, are introduced first. Then, the commonly used numerical simulation research for modeling and simulating the machining of ceramic matrix composites is briefly summarized. Finally, the processing difficulties and research hotspots of ceramic matrix composites are summarized.

To benefit tissue removal and postoperative rehabilitation, increased efficiency and accuracy and reduced operating force are strongly required in the osteotomy. A novel elliptical vibration cutting (EVC) has been introduced for bone cutting compared with conventional cutting (CC) in this paper. With the assistance of high-speed microscope imaging and the dynamometer, the material removals of cortical bone and their cutting forces from two cutting regimes were recorded and analysed comprehensively, which clearly demonstrated the chip morphology improvement and the average cutting force reduction in the EVC process. It also revealed that the elliptical vibration of the cutting tool could promote fracture propagation along the shear direction. These new findings will be of important theoretical and practical values to apply the innovative EVC process to the surgical procedures of the osteotomy.

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use.