基于点云的发动机叶片损伤体积测量方法

Point cloud based measurement of engine blade damage volume

  • 摘要: 针对当前发动机叶片损伤体积计算困难、误差较大的问题,提出一种基于点云的压气机叶片的损伤体积测量方法。首先,通过结构光扫描仪获取完整点云模型和损伤点云模型,配准分割得到缺损点云。其次,缺损点云经过姿态转换后与主成分轴对比分析、分层、切片、投影得到二维点云轮廓。最后,提出单向双次最近邻点搜索算法对二维点云的轮廓进行有序提取,使用坐标解析法求解投影面的面积,累加各层面积与切片间隔的乘积得到最终的体积。试验结果表明,提出的第一主成分轴方向切片体积计算效果更好,且轮廓提取算法对比凸包提取法、双向最近邻搜索和改进最近邻搜索算法(improved nearest point search, INPS)算法更准确,效率更高,与Geomagic软件结果相比平均相对误差不超过0.3%,证明了算法的高效性和有效性。

     

    Abstract: Aiming at the current problem of difficult calculation of damage volume of engine blade and large error, a damage volume measurement method of compressor blade based on point cloud is proposed. Firstly, a complete point cloud model and a damage point cloud model are acquired by a structured light scanner, and the defective point cloud is obtained by alignment segmentation. Secondly, the defective point cloud is compared and analyzed with the principal component axes after attitude conversion, layered, sliced and projected to obtain the 2D point cloud contour. Finally, a one-way bi-nearest neighbor search algorithm is proposed to extract the contour of the 2D point cloud in an orderly manner, and the area of the projected surfaces is solved using the coordinate resolution method, and the final volume is obtained by accumulating the product of the area of each layer and the slicing interval. The experimental results show that the first principal component axis-direction slice volume proposed in the article is calculated better, and the contour extraction algorithm is more accurate and efficient than the convex packet extraction method, bidirectional nearest-neighbor search and INPS algorithms, and the average relative error is no more than 0.3% compared with the Geomagic software results, which proves the efficiency and effectiveness of the algorithm.

     

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