Robotic Weld Seam Correction Control System Based on Visual Sensing

doi:10.3901/JME.2019.17.048

Abstract

Abstract: Regarding multi-pass weld seam tracking for industrial robots, a robotic weld seam correction control strategy is proposed based on visual sensing and the corresponding visual sensing weld seam correction control system is established. The system mainly consists of two subsystems:Visual sensing acquisition and calibration system, and robotic weld seam correction control system. Placed together with the welding torch, the industrial camera can obtain the groove or weld images in real time. The operators can observe the monitor software screen, which is designed by Windows Forms, to judge whether the welding torch deviates from target beyond the allowable range. If so, a correction command will be sent to a programmable logic controller (PLC) from the control panel; the PLC will send a control signal to the robot controller; the robot will read the information flow from the robot sensor interface (RSI) to convert the signal from PLC into the robot end offset which can be executed by the robot; in such way, the welding gun can be adjusted to a proper position to achieve weld seam correction control. During the verification test, the system is sensitive and has a reasonable accuracy in weld seam correction; the system works smoothly and steadily with good weld formation during the whole process.

Key words: visual sensing, correction control, seam tracking, multi-pass welding, industrial robot

CLC Number:

TG409

WANG Zhijiang, XUE Kunxi, WU Dingyong, YANG Hui, SHEN Junqi, LIU Jie, HU Shengsun. Robotic Weld Seam Correction Control System Based on Visual Sensing[J]. Journal of Mechanical Engineering, 2019, 55(17): 48-55.

References

[1] 陈健,苏金花,张毅梅. 《中国制造2025》与先进焊接工艺及装备发展[J]. 焊接,2016(3):1-5. CHEN Jian,SU Jinhua,ZHANG Yimei. Development of advanced welding procedure and equipment with "Made in China 2025"[J]. Welding & Joining,2016(3):1-5.
[2] 骆敏舟,方健,赵江海. 工业机器人的技术发展及其应用[J]. 机械制造与自动化,2015(1):1-4. LUO Minzhou,FANG Jian,ZHAO Jianghai. The Development and the application of the industrial robot technology[J]. Machine Building & Automation,2015(1):1-4.
[3] 许燕玲. 基于视觉及电弧传感技术的机器人GTAW三维焊缝实时跟踪控制技术研究[D]. 上海:上海交通大学,2013. XU Yanling. Research on real-time tracking and control technology of three dimension welding seam during welding robot GTAW process based on vision sensor and arc sensor[D]. Shanghai:Shanghai Jiao Tong University,2013.
[4] 何银水,孔萌,陈华斌,等. 基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别[J]. 焊接学报,2015,36(12):51-55. HE Yinshui,KONG Meng,CHEN Huabin,et al. Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding[J]. Transactions of The China Welding Institution,2015,36(12):51-55.
[5] 华天润,郭蕴华,吴业振,等. 焊接机器人焊缝视觉跟踪修正系统开发与实验研究[J]. 热加工工艺,2016(15):196-199. HUA Tianrun,GUO Yunhua,WU Yezhen,et al. Development and experimental research of weld visual tracking correction system of welding robot[J]. Hot Working Technology,2016(15):196-199.
[6] 尹湘云,殷国富,胡晓兵,等. 基于支持矢量机回归的机器人视觉系统定位精度[J]. 机械工程学报,2011,47(1):48-54. YIN Xiangyun,YIN Guofu,HU Xiaobing,et al. Positioning accuracy of robot vision system based on support vector machine regression[J]. Journal of Mechanical Engineering,2011,47(1):48-54.
[7] 贾丙西,刘山,张凯祥,等. 机器人视觉伺服研究进展:视觉系统与控制策略[J]. 自动化学报,2015,41(5):861-873. JIA Bingxi,LIU Shan,ZHANG Kaixiang,et al. Survey on robot visual servo control:Vision system and control strategies[J]. Acta Automatica Sinica,2015,41(5):861-873.
[8] 陶波,龚泽宇,丁汉. 机器人无标定视觉伺服控制研究进展[J]. 力学学报,2016,48(4):767-783. TAO Bo,GONG Zeyu,DING Han. Survey on uncalibrated robot visual servoing control[J]. Chinese Journal of Theoretical and Applied Mechanics,2016,48(4):767-783.
[9] LI X,LI X,GE S,et al. Automatic welding seam tracking and identification[J]. IEEE T. Ind. Electro.,2017,64(9):7261-7271.
[10] DING D. Visual neural network model for welding deviation prediction based on weld pool centroid[J]. Int. J. Pattern Recogn.,2018,32(8):1859014.
[11] LEE S K,NA S J. A study on automatic seam tracking in pulsed laser edge welding by using a vision sensor without an auxiliary light source[J]. J. Manuf. Syst.,2002,21(4):302-315.
[12] PRECUP R E,PREITL S,RUDAS I J,et al. Design and experiments for a class of fuzzy controlled servo systems[J]. IEEE-ASME T. Mech.,2008,13(1):22-35.

[1]	LIU Daxin, WANG Ke, LIU Zhenyu, XU Jiatong, TAN Jianrong. Research on Digital Twin Modeling Method for Robotic Assembly Cell Based on Data Fusion and Knowledge Reasoning [J]. Journal of Mechanical Engineering, 2024, 60(5): 36-50.
[2]	MENG Yuan, SHI Baojun, ZHANG Dequan. Research and Improvement of Kriging-HDMR Modeling Method [J]. Journal of Mechanical Engineering, 2024, 60(5): 249-263.
[3]	LIANG Liang, WU Chengdong, LIU Shichang. Optimal Trajectory Planning Algorithm of Industrial Robot Based on Joint Loss Constraint [J]. Journal of Mechanical Engineering, 2024, 60(19): 11-19.
[4]	YU Zhen, LIU Hailin, QIU Zurong, AN Qi. Industrial Robot End Effector Pose Repeatability Test Based on IGCF-IBSCF and BCF-IBPSO-NESN Algorithm [J]. Journal of Mechanical Engineering, 2024, 60(19): 71-87.
[5]	ZHANG Dequan, LI Xingao, ZHANG Ning, NING Guosong, HAN Xu. Refinement Modeling Method of Error Field in Full Domain for Industrial Robot and Its Corresponding Error Compensation Strategy [J]. Journal of Mechanical Engineering, 2024, 60(13): 316-329.
[6]	XIN Yang-gui, GAO Shi-yi, ZHAO Yun-qiang, LI Su, XU Wang-hui, YU Chen. Research and Application of Force-position Mixed-control of Robotic Friction Stir Welding [J]. Journal of Mechanical Engineering, 2023, 59(8): 91-98.
[7]	ZHOU Jian, ZHENG Lianyu, FAN Wei, ZHANG Xuexin, CAO Yansheng. Adaptive Online Compensation for Industrial Robot Positioning Error [J]. Journal of Mechanical Engineering, 2023, 59(5): 53-66.
[8]	WANG Rugui, DONG Yichen, CHEN Huiqing. A Time Optimal Trajectory Planning of the Metamorphic Industrial Robot [J]. Journal of Mechanical Engineering, 2023, 59(5): 100-111.
[9]	LIU Wei, LIU Shun, DENG Zhaohui, GE Jimin. Research Progress on Positioning Error Compensation Technology of Industrial Robot [J]. Journal of Mechanical Engineering, 2023, 59(17): 1-16.
[10]	ZHOU Ce, BAI Bin, YE Nan. Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression [J]. Journal of Mechanical Engineering, 2023, 59(14): 328-338.
[11]	WU Jinhui, ZHANG Dequan, HAN Xu. Robust Design of Parameter Tolerance to Industrial Robot [J]. Journal of Mechanical Engineering, 2023, 59(11): 147-158.
[12]	HAN Te, LI Yanfu, LEI Yaguo, LI Naipeng, LI Xiang. Semi-supervised Fault Diagnosis Method via Graph Label Propagation and Discriminative Feature Enhancement for Critical Components of Industrial Robot [J]. Journal of Mechanical Engineering, 2022, 58(17): 116-124.
[13]	WANG Wenxi, HUANG Yun, LIU Zhanfang, ZOU Lai, Lü Chong. Analysis of Gyroscopic Moment in High-speed Grinding of Robots with Large Curvature Surfaces [J]. Journal of Mechanical Engineering, 2022, 58(15): 208-215.
[14]	ZHANG Zekun, GUO Kai, SUN Jie. High-precision Closed-loop Robust Control of Industrial Robots Based on Disturbance Observer [J]. Journal of Mechanical Engineering, 2022, 58(14): 62-70.
[15]	JIA Aiting, HONG Bo, LI Xiangwen, GAO Jiapeng, WU Gefei, QU Yuanyuan. Real Time Tracking Method of 3D Zigzag Welding Robot Swing GMAW Based on Online Trajectory Detection [J]. Journal of Mechanical Engineering, 2022, 58(14): 116-125.