Adaptive Predictive Control of Backside Weld Width in Pulsed Gas Metal Arc Welding Using Electrical Characteristic Signals as Feedback

被引:4
作者
Cao, Yue [1 ,2 ]
Wang, Zhijiang [1 ]
Hu, Shengsun [1 ]
Wang, Tao [1 ,3 ]
机构
[1] Tianjin Univ, Sch Mat Sci & Engn, Tianjin Key Lab Adv Joining Technol, Tianjin 300354, Peoples R China
[2] Univ Kentucky, Dept Elect Engn, Lexington, KY 40504 USA
[3] Guiyang Precis Casting Co Ltd, Guiyang 550014, Guizhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Welding; Process control; Geometry; Sensors; Heating systems; Voltage control; Electric variables; Adaptive predictive control; backside weld width control; pulsed gas metal arc welding (GMAW-P); system identification; POOL SURFACE; PENETRATION; INFORMATION; TUNGSTEN; DEPTH;
D O I
10.1109/TCST.2023.3258064
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The control of backside weld width in pulsed gas metal arc welding (GMAW-P) is achieved with two electrical characteristic signals as feedback in the present work. The GMAW-P process is adjusted by changing the parameters of the base current period to provide the needed heat input, while the backside weld width is characterized during the amplitude-fixed peak current period. The two characteristic signals, i.e., the relative change in arc voltage and the average arc voltage during the peak current period, exhibit negative linear relationships with backside weld width and are employed as sensing signals. With these two characteristic signals as outputs, the backside weld width control system in GMAW-P is modeled and analyzed. The adaptive predictive controller is thus designed and validated in real-time control. This work provides an easily implemented way for weld geometry control in GMAW-P and thus has a practical meaning.
引用
收藏
页码:2879 / 2886
页数:8
相关论文
共 29 条
[1]  
Astrom B., 1984, COMPUTER CONTROLLED
[2]   Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development [J].
Bestard, Guillermo Alvarez ;
Absi Alfaro, Sadek Crisstomo .
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2018, 40 (09)
[3]   Modeling of weld penetration control system in GMAW-P using NARMAX methods [J].
Cao, Yue ;
Wang, Zhijiang ;
Hu, Shengsun ;
Wang, Wandong .
JOURNAL OF MANUFACTURING PROCESSES, 2021, 65 :512-524
[4]   Multisensor information fusion of pulsed GTAW based on improved D-S evidence theory [J].
Chen, Bo ;
Feng, Jicai .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 71 (1-4) :91-99
[5]   Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion [J].
Chen, Bo ;
Wang, Jifeng ;
Chen, Shanben .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 48 (1-4) :83-94
[6]   Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring [J].
Chen, Huabin ;
Lv, Fenglin ;
Lin, Tao ;
Chen, Shanben .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2009, 56 (05) :565-578
[7]   Model Predictive Control of GTAW Weld Pool Penetration [J].
Chen, Jinsong ;
Chen, Jian ;
Feng, Zhili ;
Zhang, Yuming .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2019, 4 (03) :2762-2768
[8]   Robotic welding systems with vision-sensing and self-learning neuron control of arc welding dynamic process [J].
Chen, SB ;
Zhang, Y ;
Qiu, T ;
Lin, T .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2003, 36 (02) :191-208
[9]   CMAC-based modelling for HPDDL welding process control [J].
Duan, Peiyong ;
Zhang, Yu Ming .
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2006, 1 (02) :107-114
[10]   Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning [J].
Guenther, Johannes ;
Pilarski, Patrick M. ;
Helfrich, Gerhard ;
Shen, Hao ;
Diepold, Klaus .
MECHATRONICS, 2016, 34 :1-11