Single-crystal sapphire specimen (α-Al₂O₃) have been widely applied in the semiconductor industry, microelectronics, and so on. In order to shorten the production time and improve the processing efficiency of sapphire processing, an integrated fixed-abrasive tool (FAT) based on solid-phase reactions is proposed in this article. The optimal FAT composition is determined using a preliminary experiment and orthogonal experiments. The mass fraction of the abrasives is chosen as 55 wt%, and the mass ratio of SiO₂/Cr₂O₃ is 2. Surface roughness R_a decreased from 580.4 ± 52.7 nm to 8.1 ± 0.7 nm after 150 min, and the average material removal rate was 14.3 ± 1.2 nm/min using the prepared FAT. Furthermore, FAT processing combined with chemical mechanical polishing (CMP) was shortened by 1.5 h compared to the traditional sapphire production process in obtaining undamaged sapphire surfaces with a roughness of R_a < 0.4 nm, which may have the potential to take the place of the fine lapping and rough polishing process.

Overcoming complex background noise and poor small-target detection in industrial settings, this paper introduces YOLOv8-BBP2, an enhanced YOLOv8 model. To better extract dynamic features, the backbone integrates a BiFormer dual-level routing attention mechanism. Moreover, a learnable Bi-directional Feature Pyramid Network (BiFPN) replaces the standard module, optimizing multi-scale feature integration. A P2 detection head is also added to accurately identify tiny objects, such as early flames and thin smoke. Tested on a custom factory fire dataset, YOLOv8-BBP2 yields 95.231% precision, 94.612% recall, and 89.677% mean average precision (mAP@0.5). These metrics represent respective gains of 3.31%, 4.934%, and 7.451% over the baseline YOLOv8s. Ultimately, with an inference speed of 20 ms per frame, the proposed network ensures highly robust, real-time performance.

The sunflower oil that has rich nutritional value is high-quality nutrition oil. The quality of oil products was impacted seriously by a lot of harmful substances that contained in sunflower seed shell. The characteristic of sunflower seeds are the kernel is smaller and the shell density is large. The shelling operation is quite difficult with the characteristic. The shelling devices for the sunflower seeds based on optimization design method and dynamics analysis is introduced in the paper. The shelling devices for the sunflower seeds can be used in the place of processing factory and individual workshop. The structure of shelling devices was designed with the 3d modeling software. In the research, the virtual prototype technology was used to simulate the kinematics and dynamics characteristics of the mechanical system. The feasibility of the design scheme of mechanical system was proved with the simulation analysis results. The equipment has advantages of the structure is compact, safe and reliable, easy to use. The research of the shelling devices for sunflower seeds can be regarded as the reference of the related research.