摘要
列车轮对作为转向架的关键零部件,其检测手段仍以人工检测为主,现有的自动检测方案,大多针对车轮某一断面的参数尺寸进行测量,难以真实反映车轮轮缘踏面的损伤情况。为此,该文提出一种列车车轮三维结构光检测中的点云处理方案。首先,利用三维结构光测量仪器采集列车车轮的三维点云数据;其次,根据列车车轮三维点云的特点,确定包括离群点去除、点云配准、点云平滑处理以及孔洞修补在内的点云处理方案,并对各处理步骤的最优参数进行分析;最后,利用贪婪投影三角化算法,进行列车车轮三维点云数据的曲面重建,使用拉普拉斯平滑算法对重建后的曲面进行平滑处理。结果表明,该文所提出的列车车轮点云处理方案能够实现对三维点云数据的处理,最终得到的列车车轮的三维曲面模型与基准模型的标准偏差为1.768 mm,实现对于列车车轮的三维检测。
As an important component of the bogie,the main inspection method of the train wheelset is still the manual inspection.Current automatic inspection methods mostly measure the parameters of a certain section of the wheel,which cannot truly reflect the damage of the wheel flange and tread.For this reason,this paper proposes a point cloud processing scheme in the three-dimensional(3D)structured light inspection of train wheels.First,the 3D point cloud data of the train wheels were acquired using a 3D structured light measuring instrument.Secondly,based on the characteristics of the 3D point cloud of the train wheels,the point cloud processing scheme was studied including outlier removal,point cloud registration,point cloud smoothing and hole repair.At the same time,the optimal parameters of each processing step were analyzed.Finally,with the greedy projection triangulation algorithm,the surface reconstruction of the 3D point cloud data of the train wheels was implemented,and the reconstructed surface was smoothed by the Laplacian smoothing algorithm.The results show that the train wheel point cloud processing scheme proposed in this paper can realize the processing of the 3D point cloud data.The standard deviation of the final 3D surface model of the train wheels and the reference model is 1.768 mm,which realizes the 3D inspection of the train wheels.
作者
庄仁诚
陈鹏
傅瑶
黄运华
ZHUANG Rencheng;CHEN Peng;FU Yao;HUANG Yunhua(College of Mechanical Engineering,Southwest Jiaotong University,Chengdu 611756,China;School of Mechatronics Engineering,Harbin Institute of Technology,Harbin 150080,China)
出处
《中国测试》
CAS
北大核心
2021年第2期19-25,共7页
China Measurement & Test
关键词
列车车轮
三维检测
结构光
点云处理
train wheels
three-dimensional inspection
structured light
point cloud processing
