Silicon carbide (SiC) ceramics have been widely used in industry due to their superior properties and excellent performance. Grinding is the key method to manufacture SiC to the desired shape, dimension, and surface quality. Grinding defects such as pits and cracks are easy to cause SiC strength degradation. In this paper, the advanced grinding technologies for SiC ceramics, including high-speed grinding, ultrasonic vibration-assisted grinding, laser-assisted grinding, and electrolytic in-process dressing grinding, are reviewed. The characteristics and machine tool setups of each advanced grinding technology are analyzed and compared with traditional grinding technology to reveal the advantages. The latest research on surface integrity, material removal mechanism, simulation, and other related studies are reviewed to express the fundamental theory in each advanced grinding technology. Finally, the key innovation and future research areas for all advanced grinding technologies are drawn in the conclusion.

Micro-milling as a precision manufacturing process has received increasing attention for its unique advantages in machining tiny parts. The surface quality of parts is an important factor that affects the performance and life of micro parts. Therefore, the study of the surface quality of parts after micro-milling has become an important topic of great interest. In this paper, the surface roughness generation models are reviewed to provide a comprehensive understanding of the various factors affecting surface quality. Based on these models, the key factors affecting surface roughness and burr generation are summarized. Furthermore, the challenges and opportunities for achieving high surface quality parts during micro-milling are summarized and some suggestions for future research are expected.

Bamboo strips, as assembling parts of sleeping mats, cushions and other decorative components, play an important role in humans’ everyday life and social economy now. Therefore, quality control for bamboo strips production is very critical. Traditional manual sorting technology owns many disadvantages such as high production cost and low sorting accuracy. This work deals with an automatic sorting system for comprehensive defect detection of bamboo strips based on machine vision. Differing from the present feature extraction methods of the bamboo strip in image processing, contour features considering area and geometrical symmetry and texture feature considering average gradient are newly introduced. An experimental automatic sorting system is designed to verify the feasibility and superiority of the proposed comprehensive defect detection method. Experimental results show that total defect detection accuracy, contour defect detection accuracy, surface texture defect detection accuracy and sorting accuracy reach 99.1%, 98.33%, 95.2% and 95.125%, respectively. The designed sorting system finishes one time sorting in 197 ms with a comparable low-speed computation processor in laboratory and it can be utilized instead of three skilled workers in practice.