Application of soft sensor in welding seam tracking prediction based on LSSVM and PSO with compression factor

Author(s):  
Jianhui Wang ◽  
Chao Wang ◽  
Xuefeng Zhu ◽  
Xiaoke Fang
2012 ◽  
Vol 17 (2) ◽  
pp. 155-161 ◽  
Author(s):  
J S Chen ◽  
G D Su ◽  
S B Xiang
Keyword(s):  

2008 ◽  
Vol 41 (2) ◽  
pp. 9186-9191 ◽  
Author(s):  
Haiyong Chen ◽  
De Xu ◽  
Hong Wang

2011 ◽  
Vol 314-316 ◽  
pp. 1005-1008
Author(s):  
Hong Tang Chen ◽  
Hai Chao Li ◽  
Hong Ming Gao ◽  
Lin Wu

Welding seam tracking precision is a key factor influencing welding quality for master-slave robot remote welding system. However, it does not satisfy the welding requirement due to significant noises. To eliminate the influence of noises upon the seam tracking precision and improve the seam tracking precision, a master-slave robot remote welding system was built and Kalman filtering (KF) was applied to the seam tracking process. The experimental results show that the KF eliminated the influence of noises upon the seam tracking precision and improved the seam tracking precision.


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.


2009 ◽  
Vol 48 (9-12) ◽  
pp. 945-953 ◽  
Author(s):  
Hongbo Ma ◽  
Shanchun Wei ◽  
Zhongxi Sheng ◽  
Tao Lin ◽  
Shanben Chen

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