留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

航空叶片形貌高精度结构光扫描视点规划

李茂月 蔡东辰 赵伟翔 肖桂风

李茂月, 蔡东辰, 赵伟翔, 肖桂风. 航空叶片形貌高精度结构光扫描视点规划[J]. 中国光学(中英文), 2023, 16(4): 802-815. doi: 10.37188/CO.2022-0221
引用本文: 李茂月, 蔡东辰, 赵伟翔, 肖桂风. 航空叶片形貌高精度结构光扫描视点规划[J]. 中国光学(中英文), 2023, 16(4): 802-815. doi: 10.37188/CO.2022-0221
LI Mao-yue, CAI Dong-chen, ZHAO Wei-xiang, XIAO Gui-feng. High precision structural light scanning viewpoint planning for aircraft blade morphology[J]. Chinese Optics, 2023, 16(4): 802-815. doi: 10.37188/CO.2022-0221
Citation: LI Mao-yue, CAI Dong-chen, ZHAO Wei-xiang, XIAO Gui-feng. High precision structural light scanning viewpoint planning for aircraft blade morphology[J]. Chinese Optics, 2023, 16(4): 802-815. doi: 10.37188/CO.2022-0221

航空叶片形貌高精度结构光扫描视点规划

doi: 10.37188/CO.2022-0221
基金项目: 国家自然科学基金(No. 51975169);黑龙江省自然科学基金(No. LH2022E085)
详细信息
    作者简介:

    李茂月(1981—),男,山东青岛人,博士,教授,博士生导师,2004 年于南京林业大学获得学士学位,2007 年于长安大学获得硕士学位,2012 年于哈尔滨工业大学获得博士学位,主要从事智能加工与光学检测技术方面的研究。E-mail:lmy0500@163.com

  • 中图分类号: TH741

High precision structural light scanning viewpoint planning for aircraft blade morphology

Funds: Supported by National Natural Science Foundation of China (No. 51975169); Natural Science Foundation of Heilongjiang Province of China (No. LH2022E085)
More Information
  • 摘要:

    航空发动机叶片的加工质量与检测精度对于叶片的使用寿命有着十分重要的影响。本文提出一种基于结构光的高精度扫描视点规划方法以提高叶片检测精度。首先,对叶片整体尺寸进行粗扫描,获取粗模型数据,并根据相机分辨率与采集精度确定视野范围。其次,利用改进Angle Criterion算法进行边界提取,根据边界坐标与视野范围完成边界分割点的确定,利用曲面的截面线法对粗模型进行切片,根据切片结果确定内部分割点,从而完成点云均匀分割。然后,对分割后的点云数据建立有向包围盒获取中心点坐标,并对其法向量进行统计,确定主法线方向,从而生成高精度扫描的视点坐标。最后,对叶片进行表面形貌检测验证,实验结果表明,与超体素分割的视点采集结果相比,本文方法的平均标准差降低了0.0054 mm,且采集视点减少了1/3。提出的视点规划方法在薄壁叶片在机加工检测领域具有良好的应用前景。

     

  • 图 1  单目面结构光采集系统示意图

    Figure 1.  Schematic diagram of monocular structured light acquisition system

    图 2  改进AC算法流程

    Figure 2.  Flow of improved AC algorithm

    图 3  插值后点云示意图

    Figure 3.  Schematic diagram of point cloud after interpolation

    图 4  角点检测图

    Figure 4.  Corner point detection diagram

    图 5  点云旋转平移示意图

    Figure 5.  Schematic diagram of point cloud rotation translation

    图 6  边界提取结果

    Figure 6.  Boundary extraction results

    图 7  边界分割点图

    Figure 7.  Boundary segmentation point diagram

    图 8  叶片点云分割点示意图

    Figure 8.  Schematic diagram of blade point cloud division points

    图 9  叶片点云粗模型分割图

    Figure 9.  Segmentation diagram of blade point cloud coarse model

    图 10  相机拍摄区域示意图

    Figure 10.  Schematic diagram of the camera shooting area

    图 11  点云有向包围盒示意图

    Figure 11.  Schematic diagram of point cloud OBB

    图 12  叶片点云块包围盒示意图

    Figure 12.  Enveloping box diagram of blade point cloud block

    图 13  点云法向量计算与统计图

    Figure 13.  Calculation and statistical graphs of normal vector of point cloud

    图 14  叶片在160 mm×128 mm视野下的采集图

    Figure 14.  Acquisition diagrams of blade in 160 mm×128 mm vision field

    图 15  叶片点云分块图

    Figure 15.  Block diagram of blade point cloud

    图 16  本文方法视点采集结果

    Figure 16.  Viewpoint acquisition results of the proposed method

    图 17  超体素分割方法视点采集结果

    Figure 17.  Viewpoint acquisition results of supervoxel segmentation method

    表  1  MER-130-30UM-L型相机主要参数

    Table  1.   Main parameters of MER-130-30UM-L camera

    性能参数参数值
    分辨率1280(H)×1024(W)
    帧率(frame·s-1)30
    传感器类型CMOS
    靶面尺寸(mm)7.2 x 5.3
    像素尺寸(μm)5.2 x 5.2
    下载: 导出CSV

    表  2  包围盒中心点坐标值

    Table  2.   Coordinate values of bounding box center point (Unit: mm)

    视点位置X坐标Y坐标Z坐标
    点云块125.22534.222376.931
    点云块224.61757.156373.603
    点云块314.81133.602378.520
    点云块415.11957.602376.472
    点云块554.54933.045379.674
    点云块654.70657.098379.530
    下载: 导出CSV

    表  3  实验数据采集结果偏差分析表

    Table  3.   Deviation analysis table of experimental data acquisition results

    视点点云偏差分析(mm) 偏差数据(mm)
    叶片
    整体
    视野
    尺寸
    采集
    本文分割方法采集结果偏差分析超体素分割方法采集结果偏差分析
    本文方法视点采集结果点云偏差分析(mm)偏差数据(mm)超体素方法视点采集结果点云偏差分析(mm)偏差数据(mm)


    1


    1


    2


    2


    3


    3


    4


    4


    5


    5


    6


    7


    6


    8


    9
    下载: 导出CSV
  • [1] 冯萍, 刘震. 舵面角度测量中结构光光条图像自动定位方法[J]. 中国光学,2014,7(6):911-916.

    FENG P, LIU ZH. Automatic localization method of the multi-planar strip in rudder angle measurement[J]. Chinese Optics, 2014, 7(6): 911-916. (in Chinese)
    [2] 秦训鹏, 丁吉祥, 董寰宇, 等. 基于直线空间旋转的十字结构光标定[J]. 光学 精密工程,2021,29(6):1430-1439. doi: 10.37188/OPE.20212906.1430

    QIN X P, DING J X, DONG H Y, et al. Calibration of cross structured light based on linear space rotation[J]. Optics and Precision Engineering, 2021, 29(6): 1430-1439. (in Chinese) doi: 10.37188/OPE.20212906.1430
    [3] GOSPODNETIĆ P, MOSBACH D, RAUHUT M, et al. Viewpoint placement for inspection planning[J]. Machine Vision and Applications, 2022, 33(1): 1-21. doi: 10.1007/s00138-021-01252-z
    [4] VASQUEZ-GOMEZ J I, SUCAR L E, MURRIETA-CID R. View/state planning for three-dimensional object reconstruction under uncertainty[J]. Autonomous Robots, 2017, 41(1): 89-109. doi: 10.1007/s10514-015-9531-3
    [5] VASQUEZ-GOMEZ J I, SUCAR L E, MURRIETA-CID R, et al. Volumetric next-best-view planning for 3D object reconstruction with positioning error[J]. International Journal of Advanced Robotic Systems, 2014, 11(10): 159. doi: 10.5772/58759
    [6] PENG W X, WANG Y N, MIAO ZH Q, et al. Viewpoints planning for active 3-D reconstruction of profiled blades using estimated occupancy probabilities (EOP)[J]. IEEE Transactions on Industrial Electronics, 2021, 68(5): 4109-4119. doi: 10.1109/TIE.2020.2987286
    [7] 苏成志, 金俊杰, 毛英坤, 等. 面向未知复杂曲面的视点自主规划方法[J]. 机床与液压,2022,50(9):103-111.

    SU CH ZH, JIN J J, MAO Y K, et al. A method for automatic view planning of unknown complex surfaces[J]. Machine Tool &Hydraulics, 2022, 50(9): 103-111. (in Chinese)
    [8] MAVRINAC A, CHEN X, ALARCON-HERRERA J L. Semiautomatic model-based view planning for active triangulation 3-D inspection systems[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(2): 799-811. doi: 10.1109/TMECH.2014.2318729
    [9] LEI Z K, CHEN X, CHEN X, et al. Radial coverage strength for optimization of multi-camera deployment[J]. arXiv:, 2004, 00787: 2020.
    [10] 朱超, 苗腾, 许童羽, 等. 基于骨架和最优传输距离的玉米点云茎叶分割和表型提取[J]. 农业工程学报,2021,37(4):188-198.

    ZHU CH, MIAO T, XU T Y, et al. Segmentation and phenotypic trait extraction of maize point cloud stem-leaf based on skeleton and optimal transportation distances[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(4): 188-198. (in Chinese)
    [11] VO A V, TRUONG-HONG L, LAEFER D F, et al. Octree-based region growing for point cloud segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 104: 88-100. doi: 10.1016/j.isprsjprs.2015.01.011
    [12] 王玮婕, 薛河儒, 武晓东, 等. 基于法线估计的鼢鼠头骨三维点云分割算法[J]. 应用激光,2022,42(5):141-150.

    WANG W J, XUE H R, WU X D, et al. Three-dimensional point cloud segmentation algorithm of Zokor skull based on normal estimation[J]. Applied Laser, 2022, 42(5): 141-150. (in Chinese)
    [13] 张兴岩, 李琦, 梁栋, 等. 一种邻接区域平面元融合的桥面分割方法[J]. 大地测量与地球动力学,2022,42(8):863-869.

    ZHANG X Y, LI Q, LIANG D, et al. A bridge deck segmentation method based on fusion of plane elements in adjacent regions[J]. Journal of Geodesy and Geodynamics, 2022, 42(8): 863-869. (in Chinese)
    [14] 李茂月, 刘泽隆, 赵伟翔, 等. 面结构光在机检测的叶片反光抑制技术[J]. 中国光学,2022,15(3):464-475. doi: 10.37188/CO.2021-0194

    LI M Y, LIU Z L, ZHAO W X, et al. Blade reflection suppression technology based on surface structured light on-machine detection[J]. Chinese Optics, 2022, 15(3): 464-475. (in Chinese) doi: 10.37188/CO.2021-0194
    [15] ARGÜELLES-FRAGA R, ORDÓÑEZ C, GARCÍA-CORTÉS S, et al. Measurement planning for circular cross-section tunnels using terrestrial laser scanning[J]. Automation in Construction, 2013, 31: 1-9. doi: 10.1016/j.autcon.2012.11.023
    [16] GRONLE M, OSTEN W. View and sensor planning for multi-sensor surface inspection[J]. Surface Topography:Metrology and Properties, 2016, 4(2): 024009. doi: 10.1088/2051-672X/4/2/024009
    [17] GALLARDO-GUTIÉRREZ E A, PARTINGTON J R. Supercyclic vectors and the angle criterion[J]. Studia Mathematica, 2005, 166(1): 93-99. doi: 10.4064/sm166-1-7
    [18] 郑鹏飞, 邹培玲, 赵菊娣, 等. 点云曲面空间网格化加密求交算法[J]. 浙江大学学报(工学版),2018,52(3):605-612.

    ZHENG P F, ZOU P L, ZHAO J D, et al. Intersection algorithm of point cloud surface by spatial mesh and refinement[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(3): 605-612. (in Chinese)
    [19] 陈岳坪, 靳龙, 卢海燕, 等. 基于三角网格模型的复杂曲面测点规划[J]. 机床与液压,2015,43(23):42-45,53.

    CHEN Y P, JIN L, LU H Y, et al. Measured point planning of complex surfaces based on triangular mesh models[J]. Machine Tool &Hydraulics, 2015, 43(23): 42-45,53. (in Chinese)
    [20] 王张飞, 刘春阳, 隋新, 等. 基于深度投影的三维点云目标分割和碰撞检测[J]. 光学 精密工程,2020,28(7):1600-1608. doi: 10.37188/OPE.20202807.1600

    WANG ZH F, LIU CH Y, SUI X, et al. Three-dimensional point cloud object segmentation and collision detection based on depth projection[J]. Optics and Precision Engineering, 2020, 28(7): 1600-1608. (in Chinese) doi: 10.37188/OPE.20202807.1600
  • 加载中
图(17) / 表(3)
计量
  • 文章访问数:  322
  • HTML全文浏览量:  161
  • PDF下载量:  260
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-10-25
  • 修回日期:  2022-12-12
  • 网络出版日期:  2023-01-13

目录

    /

    返回文章
    返回

    重要通知

    2024年2月16日科睿唯安通过Blog宣布,2024年将要发布的JCR2023中,229个自然科学和社会科学学科将SCI/SSCI和ESCI期刊一起进行排名!《中国光学(中英文)》作为ESCI期刊将与全球SCI期刊共同排名!