Scholarlink

<p>Manufacturing and positioning errors, as well as workpiece deformation, often lead to inadequate edge milling accuracy for large and complex curved thin-walled parts. To address this challenge, a novel robot edge milling method driven by measured data is proposed, which introduces two key innovations. First, a laser profile scanner is utilized to sequentially extract the edge profile features of the workpiece near the processing reference line, enabling real-time generation of robot milling trajectories based on the measured data. Second, advanced feature extraction techniques, including Harris corner detection and the gray barycentric method, are employed to accurately identify and process edge features. The milling path is segmented based on arc length, and the robot posture at each segment endpoint is calculated. A curve smoothing algorithm is then applied to generate the final robot edge milling trajectory. Experimental results demonstrate the effectiveness of the proposed method, with the spatial position error of the converted coordinates at the processing points being less than 0.2&#xa0;mm, the contour accuracy of the robot edge milling better than ± 0.3&#xa0;mm, and the normal accuracy within 3°. This method significantly enhances the processing efficiency and accuracy of large curved thin-walled parts, offering a new intelligent manufacturing solution for the aerospace industry.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Robot edge milling method for large, complex curved thin-walled parts driven by measured data

  • 期刊论文

  • 发表日期：

    2025年7月13日

查看全文
  • Xiang Xu,
  • Pengcheng Li,
  • Jiamei Lin,
  • Kehan Zhang,
  • Wei Tian

摘要

Manufacturing and positioning errors, as well as workpiece deformation, often lead to inadequate edge milling accuracy for large and complex curved thin-walled parts. To address this challenge, a novel robot edge milling method driven by measured data is proposed, which introduces two key innovations. First, a laser profile scanner is utilized to sequentially extract the edge profile features of the workpiece near the processing reference line, enabling real-time generation of robot milling trajectories based on the measured data. Second, advanced feature extraction techniques, including Harris corner detection and the gray barycentric method, are employed to accurately identify and process edge features. The milling path is segmented based on arc length, and the robot posture at each segment endpoint is calculated. A curve smoothing algorithm is then applied to generate the final robot edge milling trajectory. Experimental results demonstrate the effectiveness of the proposed method, with the spatial position error of the converted coordinates at the processing points being less than 0.2 mm, the contour accuracy of the robot edge milling better than ± 0.3 mm, and the normal accuracy within 3°. This method significantly enhances the processing efficiency and accuracy of large curved thin-walled parts, offering a new intelligent manufacturing solution for the aerospace industry.