Title
Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW
Abstract
Seam feature point acquisition is the premise of the intelligent welding process such as initial point guiding and seam tracking. However, conventional seam feature point acquisition methods based on geometric feature have shortcomings of poor flexibility and robustness. In this article, a seam feature point acquisition method based on efficient convolution operator (ECO) and particle filter (PF) is proposed, which could be applied to different weld types and could achieve fast and accurate seam feature point acquisition even under the interference of welding arc light and spatter noises. First, a structured light vision sensor is developed to acquire welding image. Second, the ECO algorithm is adopted to track the seam region and acquire seam feature point during gas metal arc welding process. Third, the state and measurement equations of the weld seam position are established, and PF is applied to improve seam feature point acquisition accuracy. Finally, a welding experiment system is built and a series of seam feature point acquisition experiments of butt joint, lap joint, and fillet joint are carried out to validate the performance of the proposed method. The experiment results demonstrate that the processing speed of the proposed method could reach up 35 Hz, and the seam feature point acquisition errors are smaller than 0.15 mm, which could meet the real-time and accuracy requirement for subsequent initial point guiding and seam tracking.
Year
DOI
Venue
2021
10.1109/TII.2020.2977121
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Efficient convolution operator (ECO),particle filter (PF),robot intelligent welding,seam feature acquisition,structured light vision
Journal
17
Issue
ISSN
Citations 
2
1551-3203
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Junfeng Fan133429.09
Sai Deng210.69
Yunkai Ma300.34
Chao Zhou48213.49
Fengshui Jing563.36
Min Tan62342201.12