| 基于点云数据的发动机管路最小间距计算方案 |
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| 引用本文： | 樊晶晶,马骊群,孙安斌,瞿剑苏.基于点云数据的发动机管路最小间距计算方案[J].航空动力学报,2019,34(11):2347-2353. |
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| 作者姓名： | 樊晶晶 马骊群 孙安斌 瞿剑苏 |
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| 作者单位： | 中国航空工业集团有限公司北京长城计量测试技术研究所,北京100095;北京航空航天大学仪器科学与光电工程学院,北京100191;中国航空工业集团有限公司北京长城计量测试技术研究所,北京,100095 |
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| 基金项目： | 工业和信息化部民用飞机专项科研技术研究项目(MJ-2018-J-70) |
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| 摘 要： | 为检测整机发动机管路是否满足最小间距的设计要求,提出了一种基于点云数据的发动机管路最小间距计算方案,方案包含5个步骤:①从点云数据中划分出不同管路的数据;②基于管路点云数据的空间分布构造等间隔栅格,计算栅格中心点作为管路的趋势线数据;③在管路各趋势线数据点位置上构造垂直平面,将管路点云数据投影到最近的垂直平面上,获得各个垂直平面上呈圆弧状分布的投影点数据;④对各垂直平面上的投影点数据进行最小二乘圆拟合,得到拟合圆圆心及其半径值,将拟合圆圆心作为管路中心线数据;⑤采用遍历法计算两条管路中心线数据的最小间距,中心线最小间距分别减去两条管路的半径值则得到两条管路的表面最小间距。通过12条管路验证了方案的准确度。实验结果表明:管路最小间距偏差在-0.35~0.46mm之间,管路半径偏差在-0.08~0.22mm之间。该方案的实施有助于管路间距数字化检测的实现,且方案的计算结果具有较好的鲁棒性。
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| 关 键 词： | 管路最小间距 点云数据 垂直平面 趋势线数据 中心线数据 |
| 收稿时间： | 2019/5/5 0:00:00 |
Strategy of calculating minimal-distance of engine pipelines based on point cloud data |
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| FAN Jingjing,MA Liqun and SUN Anbin.Strategy of calculating minimal-distance of engine pipelines based on point cloud data[J].Journal of Aerospace Power,2019,34(11):2347-2353. |
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| Authors: | FAN Jingjing MA Liqun and SUN Anbin |
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| Affiliation: | 1.Changcheng Institute of Metrology and Measurement,Aviation Industry Corporation of China,Limited,Beijing 100095,China2.School of Instrumentation and Optoelectronic Engineering,Beijing University of Aeronautics and Astronautics,Beijing 100191,China3.Changcheng Institute of Metrology and Measurement,Aviation Industry Corporation of China,Limited,Beijing 100096,China |
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| Abstract: | A strategy of calculating minimal-distance of engine pipelines based on point data was proposed to inspect whether the minimal-distance meets the design requirement. The strategy includes five steps: (1) the points belonging to the same pipeline was picked up and saved in a group. (2) A set of equal-interval grid was built along a coordinate direction in which the pipeline stretched longest. Then the point cloud data were divided into corresponding grid according to the coordinates. The center point of each grid was calculated and the trend-line data of pipeline were constituted. (3) On each trend-line point, a projection plane was built vertically to a straight line connecting the point and its adjacent point. Then point cloud data of pipeline were projected to the nearest projection plane. And a group of projected points in arc distribution were obtained on each projection plane. (4) The projected points on each projection plane were fitted into a circle using least square fitting method. And the center points of all circles constituted the center-line points of a pipeline. And the mean radius of each fitted circle was regarded as pipeline radius. (5) Traversing method was used to calculate minimal-distance between two groups of center-line points. And minimal-distance of two pipeline surfaces was calculated by subtracting radii of two pipelines from the minimal-distance of center-line points. Four groups of pipelines were examined to verify the proposed strategy. The results showed that the deviations of minimal-distance of two pipeline surfaces were within -0.35~0.46mm. And the deviations of pipeline radius were within -0.08~0.22mm. The proposed method is more robust than mostly used method and also meaningful for realizing digital inspection of pipeline distance. |
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| Keywords: | minimal-distance of pipelines point cloud data vertical plane trend-line data center-line data |
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