李耀明, 陈淑琴, 张煌. 基于激光谐波调制的深孔内壁三维面型分析系统[J]. 红外与激光工程, 2022, 51(3): 20210862. DOI: 10.3788/IRLA20210862
引用本文: 李耀明, 陈淑琴, 张煌. 基于激光谐波调制的深孔内壁三维面型分析系统[J]. 红外与激光工程, 2022, 51(3): 20210862. DOI: 10.3788/IRLA20210862
Li Yaoming, Chen Shuqin, Zhang Huang. Three-dimensional surface profile analysis system of deep hole inner wall based on laser harmonic modulation[J]. Infrared and Laser Engineering, 2022, 51(3): 20210862. DOI: 10.3788/IRLA20210862
Citation: Li Yaoming, Chen Shuqin, Zhang Huang. Three-dimensional surface profile analysis system of deep hole inner wall based on laser harmonic modulation[J]. Infrared and Laser Engineering, 2022, 51(3): 20210862. DOI: 10.3788/IRLA20210862

基于激光谐波调制的深孔内壁三维面型分析系统

Three-dimensional surface profile analysis system of deep hole inner wall based on laser harmonic modulation

  • 摘要: 为了快速精确地获取深孔结构内壁三维面型,从而分析深孔加工质量,提出了一种基于激光谐波调制的线型扫描系统,设计了可深入深孔结构的反射式光学系统。研究了通过时间窗滤波的谐波匹配点云优化算法,该算法利用谐波调制相位范围对近轴线扫描区域进行阈值分离,从而完成点云数据的滤波。实验针对三种不同类型的深孔进行了测试,并采用Handyscan三维成像仪进行了点云数据对比。文中对5 cm×5 cm的内壁区域进行了量化分析, 对比了优化前后的三维点云图像。优化前的点云中明显包含很多杂散点,综合平均偏差为0.53 mm,而采用优化后,噪声被有效抑制,综合平均偏差降为0.12 mm。在x轴方向上,系统位置偏差均值为0.240 mm,在y轴方向上,系统位置偏差均值为0.228 mm。由于优化后降低了需要计算的点云总量,故其收敛速度也有一定的改善,在3000点以上趋于稳定,约为优化前用时的65.8%。可见该系统适用于深孔内壁三维面型检测,为深孔测试与数据降噪提供了新的思路。

     

    Abstract: In order to quickly and accurately obtain the three-dimensional surface profile of the inner wall of the deep hole structure and analyze the quality of the deep hole processing, a line scanning system based on laser harmonic modulation was proposed, and a reflective optical system that can penetrate deep into the deep hole structure was designed. The point cloud optimization algorithm for harmonic matching through time window filtering was studied. The algorithm used the harmonic modulation phase range to threshold the near-axis scanning area, thereby completing the point cloud data filtering. Experiments were conducted on three different types of deep holes, and the point cloud data was compared with the Handyscan three-dimensional imager. In this paper, the inner wall area of 5 cm×5 cm was quantitatively analyzed, and the three-dimensional point cloud images before and after optimization were compared. The point cloud before optimization obviously contained many stray points, and the comprehensive average deviation was 0.53 mm. After optimization, the noise was effectively suppressed, and the comprehensive average deviation was reduced to 0.12 mm. In the x-axis direction, the average value of the system position deviation was 0.240 mm, and in the y-axis direction, the average value of the system position deviation was 0.228 mm. Since the total amount of point cloud that needs to be calculated was reduced after optimization, the convergence speed had also been improved to a certain extent, and it stabilized above 3000 points, which was about 65.8% of the time before optimization. It can be seen that the system is suitable for the three-dimensional surface inspection of the inner wall of deep holes, and it provided a new idea for deep hole testing and data noise reduction.

     

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