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.

This paper focuses on the fast finite-time and high accuracy control issue of electro-hydraulic systems (EHS). A third-order mathematical dynamic model of EHS with parametric uncertainty and external load disturbance is provided at first. Then, a feedback linearization transversion via Lie derivative for EHS is manipulated to overcome the nonlinear part in the nonlinear EHS model, which reduce the difficulty of controller design process since the nonlinear part of the EHS system can be unconsidered. A kind of disturbance observer is constructed for linearized EHS model via sliding mode disturbance observer to compensate the lumped uncertainty unavoidable in the system, based on which a finite-time sliding mode position tracking control strategy is designed for EHS, which realize the fast finite-time tracking control purpose of EHS. The feasibility of the theoretical results in this paper are verified by simulation and experiment, which illustrate that the feedback linearization transversion via Lie derivative can effectively reduce the negative impact of the nonlinear part of the EHS model, the proposed disturbance observer can effectively estimate the lumped uncertainties and raised the control accuracy and the controller designed in this paper can accelerate the tracking performance of EHS.

The prepreg compression molding process has received increasing attention from industry due to its cost-effectiveness and ability to produce complex structural shapes, and the design of the initial blank geometry is critical for the efficient production of woven composite parts using the automated manufacturing process. To design the optimal blank geometry that meets the structure requirements, and minimize trimming and waste of the edge material after preforming in the prepreg compression molding process, a blank geometry design method was developed based on finite element analysis (FEA) of preforming and a modified non-orthogonal material model. Meanwhile, whether normal vectors of all shell elements of the preformed prepregs pointing to one side of the produced structure was analyzed to automatically detect appearance of wrinkles and overlaps. An optimal blank geometry can be designed by modifying edge elements of the prepreg model through iterations of the preforming simulation. By comparing with the experimental results, the blank span length, appearance and yarn angles predicted by the preforming model were validated, and capability of the modeling-based design method to optimize the prepreg blank geometry for minimum material waste during preforming was verified.

Blade tip timing (BTT) is a non-contact measurement method for rotor blades. Its non-uniform sampling pattern is determined by the physical probe placement and rotational speed. Due to the lack of sampling probes and uneven placement, the anti-aliasing spectrum analysis for non-uniformly sampled signals becomes a hotspot in the BTT field. In this paper, a forward backward spatial smoothing (FBSS) method is used to estimate the autocorrelation matrix more accurately, which enables a better frequency identification ability for the autocorrelation matrix-based methods. Additionally, the relationship between the exponential complex steering vector and real-valued steering vectors is revealed. The peak significance is proposed to measure the quality of the pseudo spectrum. By taking multiple signal classification and minimum variance distortionless response as two example methods, the superiority of FBSS is demonstrated by simulations and experiments.