| 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],52(5):226-234, 246 |
| 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 |
| KeyWord:magnetic particle grinding magnetic field simulation fluid simulation point cloud 3D reconstruction principal component analysis pose |
| Author | Institution |
| LI Xin |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
| CHEN Song |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
| LI Yu-long |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
| ZHAO Yao-yao |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
| LI Chang-long |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
| XIE Zhi-wen |
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Liaoning Anshan , China |
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| Abstract: |
| 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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