Open AccessA Weld Line Detection Method Based on 3D Point Cloud for Automatic NDT
Author(s) -
Zhaoxuan Dong,
Jianchang Huang,
Shiqi Yin,
Fei Yue-g
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/2/022003
Subject(s) - nondestructive testing , computer science , point cloud , computer vision , robustness (evolution) , robot welding , welding , artificial intelligence , ultrasonic sensor , robot , point (geometry) , machine vision , engineering , acoustics , mechanical engineering , medicine , biochemistry , chemistry , physics , geometry , mathematics , radiology , gene
The quality of welding is often checked by ultrasonic waves. Manual testing is costly and inefficient, and manual testing is not possible in some extreme environments. Automatic non-destructive testing (NDT) technology uses robots to carry ultrasonic devices for automatic detection. Machine vision is one of the important methods to achieve navigation, that is, capturing the weld line through the camera, planning the optimal path through visual analysis and processing, and also based on structured light. Although the navigation method can solve the problem of rust and stain to a large extent, it is less robust in dealing with problems such as rust, light interference and stain. This paper proposes a navigation method based on 3D point cloud, which can effectively improve its robustness.