Research and development of weld tracking system based on laser vision

Author:

Li Dongjie12,Wang Mingrui1ORCID,Wang Shiwei1,Zhao Hongyue1

Affiliation:

1. Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China

2. Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education, Harbin University of Science and Technology, Harbin, China

Abstract

Aiming at the shortcomings of low real-time, low applicability, and low welding precision of automatic welding system, a seam tracking system based on laser vision is designed. Use the laser vision sensor to collect the weld image and transmits it to the industrial control computer for processing. Using a median filter to eliminate noise impacts such as arc and splash. Then, this paper focuses on the combination of an improved image threshold segmentation algorithm is used to solve the optimal threshold to obtain the binary image. And the information of laser stripe and the background are separated, overcomes the problems that the researchers have encountered before, such as the unrecognized global optimal solution, and the inaccuracy of the segmentation caused by system jitter. Finally, combined with the improved upper and lower average method, least square method, and Hough transform, the weld feature points are identified and more ideal real-time weld tracking is realized. The experimental results show that the method can accurately track the weld feature points, and improve the detection speed.

Funder

Fundamental Research Foundation for Universities of Heilongjiang Province

National Natural Science Foundation of China

Reserve Leader Funding for Leading Talent Echelon of Heilongjiang Province

Publisher

SAGE Publications

Subject

Applied Mathematics,Control and Optimization,Instrumentation

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An automatic measurement method for hull weld seam dimensions based on 3D laser scanning;Ocean Engineering;2024-11

2. Design of a directional feature-driven weld target tracking algorithm;2023 6th International Conference on Signal Processing and Machine Learning (SPML);2023-07-14

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