A robotic polishing trajectory planning method for TBCs of aero-engine turbine blade using measured point cloud

IF 1.9 4区 计算机科学 Q3 ENGINEERING, INDUSTRIAL Industrial Robot-The International Journal of Robotics Research and Application Pub Date : 2022-11-02 DOI:10.1108/ir-05-2022-0141
Xufeng Liang, Zhenhua Cai, Chunnian Zeng, Zixin Mu, Zifan Li, Fan Yang, Tingyang Chen, S. Dong, Chunming Deng, S. Niu
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引用次数: 3

Abstract

Purpose The application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud. Design/methodology/approach The authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation. Findings The target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state. Originality/value The research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.
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基于测点云的航空发动机涡轮叶片机械抛光轨迹规划方法
目的热障涂层(tbc)的应用可以使航空发动机叶片在更高的温度下以更高的效率工作。涂层的制备增加了叶片的表面粗糙度,影响了涂层的热循环寿命和隔热性能。为了降低叶片,特别是小尺寸、复杂曲率叶片的表面粗糙度,本文旨在提出一种基于现场测点云的工业机器人抛光轨迹规划方法。提出了一种基于点云处理技术的机器人抛光轨迹综合规划方法。首先,对获取的点云进行预处理,包括滤波和平面分割算法,提取叶片体点云;然后,利用点云切片算法和交点法建立初步接触点集;最后,采用Douglas-Peucker算法和位姿帧估计分别提取刀尖位置和优化刀具接触姿态。通过仿真和实验验证了所得到的轨迹。结果表明:轨迹目标点在叶片表面的分布并不均匀,而是随叶片表面曲率的变化而波动。仿真得到的机器人末端的直线速度和方向速度在98%以上的时间内相对稳定,且静止时间减少20%。经过抛光实验,叶片表面涂层粗糙度从Ra 7 ~ 8µm显著降低到Ra 1.0µm以下。tbc的去除率小于100 mg,明显小于所制备涂层的重量。叶片表面光滑成镜面状。研究航空发动机涡轮叶片tbc的机器人抛光是有价值的。基于测点云的实时轨迹规划可以解决没有标准的计算机辅助绘图模型以及被加工工件几何形状和尺寸不一致的问题。基于点云特征的刀具接触点提取与优化,可以增强机器人运动的平稳性、抛光速度的稳定性和抛光后刀片表面的性能。
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来源期刊
CiteScore
4.50
自引率
16.70%
发文量
86
审稿时长
5.7 months
期刊介绍: Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world. The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to: Automatic assembly Flexible manufacturing Programming optimisation Simulation and offline programming Service robots Autonomous robots Swarm intelligence Humanoid robots Prosthetics and exoskeletons Machine intelligence Military robots Underwater and aerial robots Cooperative robots Flexible grippers and tactile sensing Robot vision Teleoperation Mobile robots Search and rescue robots Robot welding Collision avoidance Robotic machining Surgical robots Call for Papers 2020 AI for Autonomous Unmanned Systems Agricultural Robot Brain-Computer Interfaces for Human-Robot Interaction Cooperative Robots Robots for Environmental Monitoring Rehabilitation Robots Wearable Robotics/Exoskeletons.
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