Additive seam tracking technology based on laser vision

Author(s):  
Zhuang Zhao ◽  
Jun Luo ◽  
Yeyu Wang ◽  
Lianfa Bai ◽  
Jing Han
Author(s):  
Taewook Kim ◽  
Seungbeom Lee ◽  
Seunghwan Baek ◽  
Kwangsuck Boo

Author(s):  
Chao Liu ◽  
Hui Wang ◽  
Yu Huang ◽  
Youmin Rong ◽  
Jie Meng ◽  
...  

Abstract Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.


Author(s):  

Laser sensors with various technologies used to track weld seams during welding operations are discussed in detail Laser vision sensors provide full automation of welding robotic systems and real-time process monitoring. Reasonable selection of the control system for a robotic welding system with laser vision is represented. Based on the analysis of the advantages and disadvantages, the practical application of laser vision sensors in the process of automatic welding is predicted. Keywords weld seam tracking; laser vision sensor; robotic welding; seam recognition; pre-processing of images; structure of the control system


2014 ◽  
Vol 511-512 ◽  
pp. 514-517
Author(s):  
Zhao Yu Li ◽  
Xiang Dong Gao

Seam tracking technology is an important area of research automatic arc welding, precise seam tracking is crucial to achieve high quality welds. In order to achieve precise seam tracking, seam deviation ( the weld center arc deviation) detection is a key. Unlike the conventional method by image processing techniques to obtain the seam deviation information directly, but selected image processing area (including the distal end portion of the molten pool welds and the front end of the pool), and analyzed as a pool image centroid characteristic parameters of the weld deviation. Study these parameters to create a new method for visual weld deviation measurement model, establish the linear regression model between pool image centroid deviation and the weld based on regression analysis theory.


2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Yanbiao Zou ◽  
Mingquan Zhu ◽  
Xiangzhi Chen

Abstract Accurate locating of the weld seam under strong noise is the biggest challenge for automated welding. In this paper, we construct a robust seam detector on the framework of deep learning object detection algorithm. The representative object algorithm, a single shot multibox detector (SSD), is studied to establish the seam detector framework. The improved SSD is applied to seam detection. Under the SSD object detection framework, combined with the characteristics of the seam detection task, the multifeature combination network (MFCN) is proposed. The network comprehensively utilizes the local information and global information carried by the multilayer features to detect a weld seam and realizes the rapid and accurate detection of the weld seam. To solve the problem of single-frame seam image detection algorithm failure under continuous super-strong noise, the sequence image multifeature combination network (SMFCN) is proposed based on the MFCN detector. The recurrent neural network (RNN) is used to learn the temporal context information of convolutional features to accurately detect the seam under continuous super-noise. Experimental results show that the proposed seam detectors are extremely robust. The SMFCN can maintain extremely high detection accuracy under continuous super-strong noise. The welding results show that the laser vision seam tracking system using the SMFCN can ensure that the welding precision meets industrial requirements under a welding current of 150 A.


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