The reinforced fibers of three-dimensional (3D) composites are interwoven in space and have better inter-laminar properties than two-dimensional (2D) laminates, and have great application prospects in high-tech fields.Flexible-oriented 3D woven technology is an emerging technology for weaving 3D composite preforms, especially suitable for manufacturing large-thickness and complex preform architectures, and the compaction behavior of preforms has an important influence on its performance. In this paper, using this technology, different types of 3D preforms were woven and subjected to systematic compaction experiments, to explore the effects of different fiber hybrid modes and process parameters on the compaction behavior. It is found that, increasing the number of cycles, wetting and sizing the fibers are all beneficial to improve the compaction ability, but reduce the stress relaxation and recovery ratios. The stress relaxation of the hybrid fiber preform is mainly affected by the carbon fiber. Reducing the dispersion of different fiber bundles is beneficial to decrease the recovery ratio. The research results provide experimental and theoretical reference for the rapid and efficient weaving of hybrid fiber composite preforms in the future.
The snake-like robot is a limbless bionic robot widely used in unstructured environments to perform tasks with substantial functional flexibility and environmental adaptability in complex environments. In this paper, the spiral climbing motion of a snake-like robot on the outer surface of a cylindrical object was studied based on the three-dimensional motion of a biological snake, and we carried out the analysis and optimization of the motion-influencing factors. First, the spiral climbing motion of the snake-like robot was implemented by the angle control method, and the target motion was studied and analyzed by combining numerical and environmental simulations. We integrated the influence of kinematics and dynamics factors on the spiral climbing motion. Based on this, we established a multi-objective optimization function that utilized the influence factors to optimize the joint module. In addition, through dynamics simulation analysis, the change of the general clamping force of the snake-like robot’s spiral climbing motion was transformed into the analysis of the contact force between the joint module and the cylinder. On the basis of the results, the effect of the control strategy adopted in this paper on the motion and change rule of the spiral climbing motion was analyzed. This paper presents the analysis of the spiral climbing motion, which is of great theoretical significance and engineering value for the realization of the three-dimensional motion of the snake-like robot.
Fiber hybrid composites can give full play to the performance advantages of different component fibers, and hybrid weaving is one of the effective methods to improve the properties of composite materials. Herein, based on the flexible‐oriented 3D woven technology, fiber hybrid ceramic matrix composites (FHCMCs) with different hybrid ratios and fiber dispersions are designed and fabricated, and their quasistatic compression properties, failure modes, and hybrid effects are studied. The results show that the reasonable hybrid of carbon (C) fiber and silicon carbide (SiC) fiber can make up for the shortcoming of single‐fiber composites and improve the compression properties of FH‐CMCs. The compressive failure modes of the FH‐CMCs are mainly the crushing failure in the C fiber layers and shear failure of the specimen. For the compressive failure deformation, the positive hybrid effect gradually becomes obvious with the increase of fiber dispersion, but the compressive strength shows a negative hybrid effect as a whole. The improved rule of mixture by introducing different parameters has a better prediction effect for the compressive strength of the FH‐CMCs. The research results provide experimental and theoretical reference for the high‐performance manufacturing of hybrid composites in the future.
Accurate real-time classification of fluorescently labelled maize kernels is important for the industrial application of its advanced breeding techniques. Therefore, it is necessary to develop a real-time classification device and recognition algorithm for fluorescently labelled maize kernels. In this study, a machine vision (MV) system capable of identifying fluorescent maize kernels in real time was designed using a fluorescent protein excitation light source and a filter to achieve optimal detection. A high-precision method for identifying fluorescent maize kernels based on a YOLOv5s convolutional neural network (CNN) was developed. The kernel sorting effects of the improved YOLOv5s model, as well as other YOLO models, were analysed and compared. The results show that using a yellow LED light as an excitation light source combined with an industrial camera filter with a central wavelength of 645 nm achieves the best recognition effect for fluorescent maize kernels. Using the improved YOLOv5s algorithm can increase the recognition accuracy of fluorescent maize kernels to 96%. This study provides a feasible technical solution for the high-precision, real-time classification of fluorescent maize kernels and has universal technical value for the efficient identification and classification of various fluorescently labelled plant seeds.
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