Zhou Yuhui, Wei Bin, Yang Guang, Li Jie, Chang Hongjie
Faculty of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
Hebei Provincial Collaborative Innovation Center for General Aviation Additive Manufacturing, Shijiazhuang, 050018, China.
Sci Rep. 2025 Mar 18;15(1):9352. doi: 10.1038/s41598-025-94164-1.
Aiming at the current high-speed real-time inspection demand faced in the production of filter rods with cores and the limitations of traditional quality control methods, this study proposes a high-speed online inspection method based on machine vision. As the production speed of filter rods increases, the traditional inspection methods cannot meet the real-time and accuracy requirements in a high-speed environment, while the restricted production environment and more disturbing factors lead to an increase in the detection error. Therefore, this paper proposes an efficient visual inspection method, which combines the ellipse fitting algorithm to accurately obtain the coordinates of the filter rods and their mandrel centre points, and evaluates the mandrel deviation through the Euclidean distance calculation to judge the product qualification. The experimental results show that the system is capable of inspecting at a rate of 4200 pcs per minute in a complex environment with a defect detection rate of more than 95%. The technology significantly improves the inspection efficiency and product quality of the filter rod production line, providing a reliable intelligent inspection solution for the filter rod manufacturing industry.
针对目前有芯滤棒生产中面临的高速实时检测需求以及传统质量控制方法的局限性，本研究提出了一种基于机器视觉的高速在线检测方法。随着滤棒生产速度的提高，传统检测方法无法满足高速环境下的实时性和准确性要求，同时受限的生产环境和更多干扰因素导致检测误差增加。因此，本文提出一种高效的视觉检测方法，该方法结合椭圆拟合算法精确获取滤棒及其芯轴中心点的坐标，并通过欧几里得距离计算评估芯轴偏差以判断产品是否合格。实验结果表明，该系统能够在复杂环境下以每分钟4200支的速度进行检测，缺陷检测率超过95%。该技术显著提高了滤棒生产线的检测效率和产品质量，为滤棒制造行业提供了可靠的智能检测解决方案。
