肖苏华,乔明娟,赖南英,罗文斌,刘普京,曹应斌,王志勇.基于 3D 视觉的风电塔筒焊缝检测系统设计[J].电子测量与仪器学报,2022,36(2):122-130
基于 3D 视觉的风电塔筒焊缝检测系统设计
Design of wind turbine tower weld detection system based on 3D vision
  
DOI:
中文关键词:  机器视觉  风电塔筒  焊缝检测  高精密运动  点云处理
英文关键词:machine vision  wind power tower  weld detection  high precision movement  point cloud processing
基金项目:2021年广东省科技创新战略专项资金(pdjh2021a0284)、衡阳市重点研发计划(202002022385)、广东省教育厅特色创新项目(2019KTSCX086)资助
作者单位
肖苏华 1. 广东技术师范大学机电学院 
乔明娟 1. 广东技术师范大学机电学院 
赖南英 2. 湖南恒岳重钢钢结构工程有限公司 
罗文斌 2. 湖南恒岳重钢钢结构工程有限公司 
刘普京 1. 广东技术师范大学机电学院 
曹应斌 2. 湖南恒岳重钢钢结构工程有限公司 
王志勇 1. 广东技术师范大学机电学院 
AuthorInstitution
Xiao Suhua 1. College of Electromechanical Engineering, Guangdong Polytechnic Normal University 
Qiao Mingjuan 1. College of Electromechanical Engineering, Guangdong Polytechnic Normal University 
Lai Nanying 2. Zhong-gang Steel Structure Engineering Co. , Ltd. of Hunan Heng-yue 
Luo Wenbin 2. Zhong-gang Steel Structure Engineering Co. , Ltd. of Hunan Heng-yue 
Liu Pujing 1. College of Electromechanical Engineering, Guangdong Polytechnic Normal University 
Cao Yingbin 2. Zhong-gang Steel Structure Engineering Co. , Ltd. of Hunan Heng-yue 
Wang Zhiyong 1. College of Electromechanical Engineering, Guangdong Polytechnic Normal University 
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中文摘要:
      为满足风电机组塔筒行业对焊缝外观质量缺陷检测高效性、精确性的需求,采用 3D 机器视觉技术研制了基于风电塔 筒焊缝外观质量缺陷检测系统。 首先,通过点云滤波、点云分割、点云精简对采集的点云数据进行预处理,确保后期缺陷评判的 准确性;其次,对三维数据进行轮廓切片化处理以及断点拟合处理,得到轮廓特性;再次,采用递归粗提取改进算法提取特征点, 进行缺陷评判,获得焊缝外观缺陷检测结果;最后,根据系统焊缝缺陷评判流程及标准,选取典型的焊缝样板进行焊缝宽度、焊 缝错边以及焊缝直线度测试,焊缝检测精度可达 0. 001 mm,速度为当前人工检测速度的 3 倍,检测结果表明,系统具备高准确 性、高速度和高精度特性,能代替人工检测,具有良好的应用前景。
英文摘要:
      In order to meet the demand of high efficiency and accuracy of weld appearance quality defect detection in wind turbine tower cylinder industry, the 3D machine vision technology was used to develop the weld appearance quality defect detection system based on wind turbine tower cylinder. First, the point cloud data was preprocessed by point cloud filtering, point cloud segmentation and point cloud simplification to ensure the accuracy of defect evaluation in the later stage. Secondly, the contour characteristics of 3D data were obtained by slice processing and breakpoint fitting. Thirdly, the improved recursive rough extraction algorithm was used to extract the feature points, and the defect evaluation was carried out to obtain the detection results of weld appearance defects. Finally, according to the evaluation process and standard of weld defects in the system, a typical weld sample is selected to test the weld width, weld dislocation and weld straightness. The weld detection accuracy can reach 0. 001 mm, and the speed is 3 times of the current manual detection speed. The detection results show that the system has the characteristics of high accuracy, high speed and high precision, which can replace manual detection, and has a good application prospect.
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