1.6 μm左右)。提出了一种基于点云数据处理的样条路径曲线生成及特征点采样方法,可用于航空发动机小型涡轮叶片表面热障涂层的机器人自动磨抛作业。该方法采用三维视觉传感器实时扫描叶片表面并生成点云数据,然后经过点云处理与B 样条曲线拟合算法,生成航空发动机叶片表面高温涂层的全覆盖磨抛路径。经过试验验证,该方法在保持涂层有效厚度的前提下可将涂层表面粗糙度降低至 R
a
0.7 μm以下,实现了航空发动机叶片表面热障涂层磨抛精加工作业。
Abstract
Thermal barrier coating (TBC) is a critical high-temperature protection technology applied to hot-section components of military and civilian aero-engines. Composed of a ceramic oxide top layer and a metallic bond coat
it significantly reduces the substrate operating temperature and offers technical advantages such as high hardness
excellent stability
resistance to high-temperature corrosion
reduces fuel consumption
and improves engine efficiency and service life. After initial preparation via atmospheric plasma spraying
the surface roughness of the coating is relatively high (approximately R
a
10 μm). In production
grinding and polishing post-processing are commonly employed to reduce it to the required range (around R
a
1.6 μm). This study proposes a spline path curve generation and feature point sampling method based on the RANSAC segmentation principle
applicable to robotic automated grinding and polishing of TBC on small turbine blades in aero-engines. The method utilizes a 3D vision sensor to scan the blade surface in real time
generating point cloud data. Through point cloud processing and B-spline curve fitting algorithms
a full-coverage grinding and polishing path for the high-temperature coating on the aero-engine blade surface is generated. Experimental verification demonstrates that this method reduces the coating surface roughness to below R
a
0.7 μm while maintaining effective coating thickness
achieving precision grinding and polishing of TBCs on aero-engine blade surfaces.