Junye Li

Straightening rolls used in aluminum strip production are progressively contaminated by metal particles and fine debris that adhere to their surfaces. Periodic polishing is therefore required to restore the surface condition of the rolls while avoiding excessive material removal or surface damage. This study developed an automated polishing machine for aluminum strip straightening rolls using the Theory of Inventive Problem Solving (TRIZ) as a systematic engineering-design framework. Two engineering problems were considered: achieving effective contaminant removal without degrading the roll surface and improving loading and unloading productivity without introducing excessive device complexity. The problems were formalized using TRIZ engineering parameters and a contradiction matrix, after which candidate inventive principles were screened according to the physical requirements of the polishing process. The contradiction matrix was therefore used as a candidate-generation tool and engineering screening as the selection criterion. Based on the resulting design requirements, the machine was configured with an abrasive-belt feed mechanism, a lifting mechanism, and an automated loading and unloading mechanism. A programmable logic controller, servo and stepper drives, and an abrasive-belt system were integrated to coordinate workpiece positioning and polishing. A physical prototype was fabricated and evaluated using a two-group comparative test against manual polishing. Surface roughness was measured at three axial positions using a Surftest SJ-210 portable surface roughness tester. In this workshop-scale comparison of three rolls per method, machine-polished rolls exhibited lower mean Ra values and markedly smaller scatter than manually polished rolls, with 0.075 ± 0.004 μm compared with 0.951 ± 0.179 μm. Workshop records indicate an increase in polishing throughput from approximately 55 to 165 rolls/month. All nine machine-polished measurements satisfied the workshop acceptance criterion of Ra ≤ 0.6 μm, whereas all nine manually polished measurements exceeded it. These observations support the engineering feasibility of the proposed machine architecture under the investigated conditions.

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems.

Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address these challenges and enable precise safflower target recognition in complex environments, this study proposes an improved safflower detection model, WED-YOLO, based on YOLOv8n. Firstly, the original bounding box loss function is replaced with the dynamic non-monotonic focusing mechanism Wise Intersection over Union (WIoU), which enhances the model’s bounding box fitting ability and accelerates network convergence. Then, the upsampling module in the network’s neck is substituted with the more efficient and versatile dynamic upsampling module, DySample, to improve the precision of feature map upsampling. Meanwhile, the EMA attention mechanism is integrated into the C2f module of the backbone network to strengthen the model’s feature extraction capabilities. Finally, a small-target detection layer is incorporated into the detection head, enabling the model to focus on small safflower targets. The model is trained and validated using a custom-built safflower dataset. The experimental results demonstrate that the improved model achieves Precision (P), Recall (R), mean Average Precision (mAP), and F₁ score values of 93.15%, 86.71%, 95.03%, and 89.64%, respectively. These results represent improvements of 2.9%, 6.69%, 4.5%, and 6.22% over the baseline model. Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest mAP value. It outperforms the module mentioned by 13.06%, 4.85%, 4.86%, and 4.82%, respectively. The enhanced model exhibits superior precision and lower miss detection rates in safflower recognition tasks, providing a robust algorithmic foundation for the intelligent harvesting of safflowers.