This study investigates the impact of asynchronous rolling on the microstructure, tensile properties, and electrical conductivity of pure copper. Pure copper sheets were subjected to asynchronous rolling with asynchronous ratios of 1.0 and 1.2, followed by microstructural analysis using electron backscatter diffraction, tensile testing, and resistivity measurements. Notable increases in ultimate tensile strength and yield strength were observed, with the sample processed at an asynchronous ratio of 1.2 showing improvements of 25.8% in tensile strength and 3.42% in yield strength compared to the sample processed at an asynchronous ratio of 1. However, an increase in strain localization and a decrease in elongation were noted, indicating a trade-off between strength and ductility. Furthermore, conductivity measurements revealed that the increase in asynchronous ratio led to a rise in defect density, resulting in a 19.96% decrease in conductivity. These findings provide valuable insights into optimizing asynchronous rolling parameters to tailor the performance of pure copper in various industrial applications.

Sophisticated composite materials have a range of outstanding broad merits such as high particular strength, high specific stiffness, powerful designability, excellent tiredness resistance, excellent durability, are widely used in transportation, medical, electronics and other area. The assembly process of the composite surface components has large assembly stress, which affects the assembly accuracy. At the same time, the manual assembly cycle is elongated, the efficiency is low, and the quality stability is impoverished, which constraints the assembly mass and assembly power of the complex curved composite components. To improve the assembly power and carry out automatic assembly, this paper designs the automatic assembly technology scheme of the complex curved composite components, designs the automatic assembly scheme verification device, and conducts the automatic assembly test to verify the feasibility of the scheme.

Reliable extraction of local trajectory geometry from line-laser images is essential for robotic adhesive-dispensing perception, particularly in multi-target scenes affected by reflection, stripe discontinuity, and weak responses. This study presents a two-stage framework that separates full-image target localization from ROI-level keypoint estimation. YOLO26n first detects local trajectory regions, after which an orientation-aware lightweight laser keypoint network (OLKNet) predicts one center control point and two directional reference points within each ROI. OLKNet combines a FastViT-T8 backbone, a lightweight top-down feature pyramid, center-guided rotated anisotropic heatmaps, and channel-dependent DARK decoding. A dataset comprising 4460 images and 5656 target instances was constructed. On the test subset, YOLO26n achieved a precision of 0.9878, a recall of 1.0000, and an mAP_50:95 of 0.8737. On 567 test instances matched to predicted ROIs, OLKNet achieved an AP of 0.9120, a center-point mean Euclidean error of 3.406 pixels, a center-point root mean square error of 4.316 pixels, and an angular error of 2.819^∘. Under a five-frame scheduling strategy, the GPU vision-software pipeline achieved 73.93 FPS for single-target images and 47.17 FPS for multi-target images. Here, real-time performance refers to the vision-software pipeline rather than robotic closed-loop dispensing.