李鑫,陈松,李雨龙,赵耀耀,李昌龙,解志文.弯管点云面型识别与磁粒研磨试验[J].表面技术,2023,52(5):226-234, 246.
LI Xin,CHEN Song,LI Yu-long,ZHAO Yao-yao,LI Chang-long,XIE Zhi-wen.Experiment on Point Cloud Pattern Recognition and Magnetic Particle Grinding of Elbow[J].Surface Technology,2023,52(5):226-234, 246 |
| 弯管点云面型识别与磁粒研磨试验 |
| Experiment on Point Cloud Pattern Recognition and Magnetic Particle Grinding of Elbow |
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| DOI：10.16490/j.cnki.issn.1001-3660.2023.05.022 |
| 中文关键词: 磁粒研磨 磁场仿真 流体仿真 点云 三维重建 主成分分析 位姿 |
| 英文关键词:magnetic particle grinding magnetic field simulation fluid simulation point cloud 3D reconstruction principal component analysis pose |
| 基金项目:国家自然科学基金(51775258);辽宁省教育厅项目(2020FWDF05，2020FWDF07);辽宁科技大学基金(2018FW05) |
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| Author | Institution |
| LI Xin | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
| CHEN Song | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
| LI Yu-long | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
| ZHAO Yao-yao | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
| LI Chang-long | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
| XIE Zhi-wen | School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan 114051, China |
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| 中文摘要: |
| 目的 解决空间异形弯管折弯处内壁研磨困难，普遍采用的弯管磁粒研磨工艺存在手动采点误差大、机械手坐标精度低及研磨间隙差异大等问题。方法 首先对弯管内壁的研磨原理进行分析，对管内流体和磁极排布进行仿真模拟，分析不同研磨间隙下的磁感应强度变化及管件压力和流速的变化，利用三维光学扫描仪扫描点云数据，对扫描的数据进行三维重建，截取折弯处，提取特征点，通过主成分分析法构建点云坐标系，最后将提取到的特征点进行坐标转换，利用处理后的点云数据进行磁粒研磨弯管内壁，与手动采点试验后的研磨效果进行对比，证明其可行性。结果 采用点云识别获取弯管中线轨迹更平滑，在相同条件下，经过手动采点研磨弯管使其表面粗糙度降至0.18 μm，点云面型特征识别弯管将表面粗糙度降至0.10 μm，同时其表面形貌效果最佳，表面凹坑、划痕完全被去除，研磨痕迹较浅。结论 点云面型的识别方法能够快速获取弯管研磨轨迹，并且经过点云数据处理，提高了中线采取的准确性，同时保证了研磨过程的稳定性，克服了因研磨间隙变化产生的研磨效果不均匀问题。 |
| 英文摘要: |
| The robot grinding system is used to process the elbow. The manual sampling point has random gap fluctuations. The centerline point cannot be effectively controlled. In this paper, a method for identifying point cloud surface features of elbows was proposed. The point cloud data of the elbows were scanned by a 3D laser scanner. The grinding trajectory of the center line was accurately planned through point cloud data processing, and the point cloud space coordinates were converted into robot recognizable codes. The grinding gap between the yoke and the elbow was effectively controlled, and the robot grinding and elbow system was optimized. The CFD simulation of the fluid in the tube was carried out to analyze the erosion damage of the inner wall of the tube under different flow rates and pressures, so that the inner wall of the tube could be ground in a targeted manner by changing the grinding gap during grinding. The effect of different magnetic pole arrangements on the grinding area was explored. The gap between the magnetic pole and the workpiece was adjusted according to the direction of the red arrow. And the change trend of the magnetic induction of the pipe was analyzed with a diameter of 30 mm by magnetic field simulation. The grinding gap reasonably controlled the difference of the magnetic induction intensity and the variation range of the magnetic induction intensity to ensure the uniform grinding of the inner wall of the workpiece. The pipe point cloud data was scanned with a 3D laser scanner. The 3D model was reconstructed on the basis of the original point cloud. The point cloud data was segmented. The down-sampling processing was conducted on the intercepted bends. The the principal component analysis method was used to determine the point cloud coordinate system. The point cloud coordinate system was taken as the workpiece coordinate system. The robot's running pose coordinates were solved through coordinate transformation. The robot flange was connected to the magnetic yoke to control the processing feed speed. The servo motor controlled the magnetic pole speed of the rotating magnetic field. The elbow passed through the rotating magnetic yoke. The internal abrasive particles were adsorbed on the auxiliary magnetic pole. In cooperation with the external rotating magnetic field, a closed magnetic induction line loop was formed. Based on the robot grinding pose generated by coordinate transformation, initial processing parameters were set, and reciprocating processing was performed on the bend of the elbow. A comparative test was carried out on manual point sampling and point cloud surface type recognition. The elbow was ground under the processing parameters of fixed magnetic pole speed of 800 r/min, feed speed of 1 mm/s, machining gap of 5 mm, grinding fluid of 50 mL, and abrasive of 20 g. After 50 minutes, the surface roughness Ra of manual sampling points decreased from 0.41 μm to 0.25 μm, and the surface roughness Ra of point cloud surface recognition decreased from 0.49 μm to 0.19 μm. After 80 minutes, the surface roughness Ra of manual sampling points decreased to 0.18 μm, and the surface roughness Ra of the point cloud surface type identification dropped to 0.10 μm. At the same time, the micro-cracks on the surface morphology of the manually collected points were obviously removed, and there were still groove marks, and the point cloud surface identifying the surface topography as the centerline trajectory was planned, the operating points were guaranteed to be dense and regular, so that the original defects were basically removed, and the surface grinding marks were fine and uniform. Compared with the improvement rate of surface roughness, the manual point collection was 54.4%, and the point cloud surface recognition was 78.4%. Therefore, the point cloud surface recognition saves the point collection time, the surface roughness decreases rapidly, and the surface removal effect is better. There are certain advantages over manual sampling. |
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