基于混合视觉的封严蜂窝复杂磨痕量化评估方法

张鹏飞; 徐茂程; 赵罡

doi:10.13224/j.cnki.jasp.20240565

基于混合视觉的封严蜂窝复杂磨痕量化评估方法

doi: 10.13224/j.cnki.jasp.20240565

1.
北京航空航天大学航空发动机研究院，北京 102206
2.
北京航空航天大学机械工程及自动化学院，北京 102206

基金项目: 国家科技重大专项（J2022-Ⅶ-0001-0043）

详细信息

作者简介:
张鹏飞（1990-），男，助理研究员，博士，主要从事智能装配与机器视觉技术方面的研究。E-mail：ftd423@buaa.edu.cn

中图分类号: V264.3
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- 被引次数: 0
出版历程
- 收稿日期: 2024-08-14
- 网络出版日期: 2025-05-28

Hybrid vision-based quantitative evaluation method of complex wear scratches on honeycomb seals

1.
Research Institute of Aero-Engine，Beihang University，Beijing 102206，China
2.
School of Mechanical Engineering and Automation，Beihang University，Beijing 102206，China

摘要

摘要:
提出了一种基于混合视觉的封严蜂窝磨痕测量数据自适应处理与量化评估方法，考虑蜂窝磨痕的边缘模糊及截面复杂的特性，通过点云和图像的融合分析实现了蜂窝磨痕的自适应识别、特征提取与量化评估。在点云数据中，根据蜂窝结构的先验几何特征，对实测点云进行重新定位与主平面提取，实现测量坐标系与理论坐标系的位姿对齐；在图像数据中，通过图像形态学算法解决蜂窝空洞较多、深孔等干扰下的蜂窝区域精准提取问题，实现划痕几何的量化计算。对多组磨损烧蚀后的蜂窝实测数据测试，能够实现所有划痕的精准辨识，在划痕宽度与深度上与人工测量结果偏差小于5%，识别速度提升7倍以上；结果表明所提出的方法能够利用表面形貌点云数据对碰磨及烧蚀的蜂窝磨痕进行自适应识别与量化评估。
- 封严蜂窝 /
- 混合视觉 /
- 智能识别 /
- 三维测量 /
- 点云变换 /
- 图像处理
Abstract:
A hybrid vision-based method for adaptive processing and quantitative evaluation of sealing honeycomb scratch measurement data was proposed. Taking into account the characteristics of edge blurring and complex cross-sections in honeycomb scratches, the method integrated point cloud and image data through fusion analysis to achieve adaptive scratch identification, feature extraction, and quantitative evaluation. In point cloud data analysis, the measured point cloud was realigned, and the primary plane was extracted based on the prior geometric features of the honeycomb structure, enabling alignment of the measurement coordinate system with the theoretical coordinate system. In image data analysis, image morphology algorithms were employed to address challenges such as the abundance of honeycomb cavities and deep holes, ensuring precise extraction of the honeycomb regions and quantitative computation of the scratch geometry. Testing on multiple sets of measured data from worn and ablated honeycomb surfaces demonstrated the ability of the proposed method to accurately identify all scratches, with deviations in scratch width and depth measurements being less than 5% compared with manual measurements. Moreover, the identification speed was improved by more than sevenfold. The results indicated that the proposed method effectively utilized surface morphology point cloud data for the adaptive identification and quantitative evaluation of honeycomb scratches caused by wear and ablation.
- honeycomb seal /
- hybrid vision /
- intelligent recognition /
- three-dimensional measurement /
- point cloud transformation /
- image processing

HTML

图 1 发动机蜂窝封严结构^[3]（单位：mm）

Figure 1. Engine honeycomb sealing structure^[3] （unit：mm）

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图 2 蜂窝封严点云测量示意图

Figure 2. Schematic diagram of honeycomb sealed point cloud measurement

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图 3 蜂窝点云数据特点

Figure 3. Honeycomb point cloud data characteristics

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图 4 点云Z距离聚类分割（颜色随机赋值）

Figure 4. Point cloud Z distance clustering segmentation （color random assignment）

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图 5 蜂窝区域提取结果

Figure 5. Honeycomb region extraction results

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图 6 校准前点云与XYZ包围盒

Figure 6. Point cloud and XYZ bounding box before calibration

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图 7 校准前点云与XOY平面

Figure 7. Point cloud and and XOY plane before calibration

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图 8 点云坐标校正与平面提取流程

Figure 8. Coordinate correction and plane extraction process of point cloud

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图 9 点云平面提取结果

Figure 9. Extraction results of point cloud plane

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图 10 校准后点云与XOY平面

Figure 10. Point cloud and XOY plane after calibration

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图 11 点云投影后生成的灰度图像

Figure 11. Gray image generated after the point cloud projection

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图 12 划痕区域提取

Figure 12. Scratch area extraction

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图 13 蜂窝骨架提取

Figure 13. Honeycomb skeleton extraction

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图 14 周向数据平滑

Figure 14. Circumferential data smoothing

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图 15 划痕深度分布情况

Figure 15. Depth distribution of scratches

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表 1 蜂窝封严识别对比（自动/人工）

Table 1. Honeycomb seal recognition and comparison （automatic/manual）

测量项目	数据1	数据2	数据3
划痕数量	4/4	4/4	4/4
处理时间/s	40/300	40/300	40/300
划痕1宽度/mm	1.90/1.89	2.50/2.44	2.56/2.61
划痕1深度/mm	0.367/0.344	0.309/0.314	0.405/0.411
划痕2宽度/mm	1.84/1.91	2.50/2.33	2.60/2.72
划痕2深度/mm	0.376/0.353	0.287/0.284	0.360/0.351
划痕3宽度/mm	1.94/1.96	2.26/2.29	2.62/2.55
划痕3深度/mm	0.366/0.366	0.288/0.279	0.356/0.361
划痕4宽度/mm	1.82/1.79	2.36/2.37	2.60/2.58
划痕4深度/mm	0.35/0.362	0.281/0.257	0.355/0.312

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