3D bioprinting is an emerging research field developed by the deep cross-fertilization of 3D printing technology with multiple disciplines such as mechanics, materials, and biomedicine. Extrusion 3D bioprinting, the most widely used 3D bioprinting technology, can print biomaterials with different viscosities and has a wide range of material applicability. In this study, we prepared a composite hydrogel with gelatin-oxidized nanocellulose as the matrix and glycerol as a multifunctional co-solvent, and the optimal composition of the hydrogel was determined by material characterization. The microstructure of the hydrogel was visualized by scanning electron microscopy (SEM), and it can be seen that the composite hydrogel material has a three-dimensional porous network structure with microporous pore sizes ranging from 200–300 µm. The infrared spectra also showed that the addition of glycerol did not interact with gelatin-oxidized nanocellulose while improving the hydrogel properties. Meanwhile, the composite hydrogel has obvious shear-thinning properties and good mechanical properties, which are suitable for extrusion-based 3D bioprinting, and the printed area is clear and structurally stable. A series of results indicate that the hydrogel is suitable for extrusion-based 3D bioprinting with good pore structure, mechanical properties, and printable performance. This gelatin-oxidized nanocellulose hydrogel provides a new idea and material for 3D bioprinting and expands the potential uses of the material.

Diamond wire sawing, as the core process for monocrystalline silicon wafering, has gained widespread application in the photovoltaic and microelectronics industries due to its high efficiency and low material loss. This study investigates the cutting mechanism of monocrystalline silicon with (100) crystal orientation under multi-abrasive and multi-scratch conditions using explicit finite element dynamics simulation. It focuses on analyzing the effects of radial spacing and height difference between abrasive grains on surface morphology, cutting force, and residual stress. Based on the Johnson-Holmquist-II (JH-II) constitutive model, a high-precision three-dimensional finite element simulation model was constructed. Simulation results indicate that the spacing and height difference between abrasive grains significantly affect the grain-to-grain coupling, thereby influencing the peak cutting force and the surface damage characteristics of the scratches. To address cutting force and residual stress responses, this study proposes an algorithmic optimization scheme based on a multifactor orthogonal experimental design. The analysis indicates that the optimal parameters—U = 1385 m/min, V = 142°, and W = 6.2 μm—reduce residual stress by 33% and cutting force by 75%.

The swashplate is a critical component in automotive air-conditioning compressors, and its surface defects can directly affect the cooling performance and service life of the compressor. To address the challenge of accurately detecting small surface defects on the swashplate, we propose an improved YOLOv8n-based detection algorithm, named YOLO-PTSW. This algorithm incorporates a P2 small-target detection head to enhance fine-grained feature perception, introduces a 2wayConv downsampling module to improve information retention in shallow features, integrates the parameter-free simple attention module (SimAM) attention mechanism to strengthen feature responses, replaces the localization loss with Wise-PIoU to increase sensitivity to small-target boundary errors and improve localization accuracy, and applies pruning to achieve model lightweighting. Experimental results demonstrate that YOLO-PTSW outperforms the baseline YOLOv8n, with increases of 11.1%, 8.6%, 11.2%, and 17.5% in precision, recall, mAP@0.5, and mAP@0.5:0.95, respectively, while reducing the number of model parameters by 58%. These improvements not only enhance detection performance but also reduce model size, thereby improving deployment flexibility. Compared with other mainstream object detection models, YOLO-PTSW exhibits superior accuracy and efficiency, highlighting its strong potential for industrial applications.