Brief paperAdaptive interval model control of weld pool surface in pulsed gas metal arc welding☆
Introduction
Gas metal arc welding (GMAW) is one of the most widely used arc welding processes (Linnert, 1994). Its welding current is typically pulsed between a base and peak current to form pulsed GMAW (GMAW-P) in order to deliver heat and mass input in wide ranges while still transferring the liquid metal of melted wire into the weld pool in desired modes. To assure the mechanical strength, the depth of weld penetration or the penetration depth, i.e., how deeply the base metal has been melted, needs to be controlled at a desired level. Unfortunately, because of the lack of sensors that can measure this depth (that is not visible), all manufacturing conditions that may affect the relationship of welding parameters with this depth have to be controlled accurately at relatively high costs in order to assure that pre-determined welding parameters can produce the desired penetration depth. Although many methods have been proposed to obtain different kinds of indirect measurements (Bicknell et al., 1994, Graham and Ume, 1997, Xiao and den Ouden, 1993, Yudodibroto et al., 2004), they are typically complex and not suitable for the manufacturing environment.
Among possible indirect measurements, the weld pool surface appears to be most promising because skilled welders are capable of controlling the penetration depth based on their observation on the weld pool surface. Machine vision (Saeed and Zhang, 2007, Yoo and Lee, 2004, Zhang et al., 1996, Zhang et al., 2006) can be used to measure the weld pool surface but its application in a manufacturing environment is not most desirable because of its complexity and its needs for accurate calibration, complex and reliable image processing, and skills different from the present labor force.
This work aims at controlling the weld pool surface, as an indirect measurement, in GMAW-P to achieve the desired weld penetration depth using an easily implementable innovative method. This innovative method is based on our recent discovery documented in Wang, Zhang, and Wu (2010) and Zhang and Wang (2010) that the change in arc voltage during the peak current period possesses a simple relationship with the weld penetration depth. (This relationship is possible not only because the arc voltage is a direct measurement of arc length but also because the arc length increases as the weld pool surface becomes deeper.) Hence, an appropriate parameter in the waveform of the pulsed current can be selected as the system input and used to control the system output, i.e., the change in the arc voltage during the peak current period.
Section snippets
System description
Fig. 1 illustrates the waveforms of welding current and arc voltage. The current is pulsed between a base level and peak level with a fixed peak current period. The base current period is adjusted to change the heat input and weld penetration capability. The arc voltage continuously varies with the arc length and the change in the arc voltage measurement during a peak current period is defined as the difference between the maximal and minimal values of the voltage measurement during this period
System identification
Step response experiments with different magnitudes are conducted. The parameters are shown in Table 1, where is the control/sample period, and and represent the input before and after the step change respectively. Other experimental conditions are: two 6.3 mm-thick mild steel (C1018) plates of 300 mm×25.4 mm were welded in a square butt joint with a 1.5 mm root opening at the flat position; the welding torch stayed stationary and the work-piece traveled with a welding tractor at
Pre-filter
A low-pass pre-filter is added to the system due to the noises: where is the system output after filtering. Choosing different produces different filtering results. When , the filtering result is acceptable because it can effectively suppress the noises while the resultant control speed is still acceptable.
Eqs. (4), (5) result in where .
Predictive control algorithm
Large noises in the arc voltage signals affect modeling accuracy and a model predictive control algorithm which is insensitive to the modeling inaccuracy is thus introduced. The transfer function for system (6) is Because the welding process is stable, it can be approximated using a finite impulse response (FIR) model with only the first terms. The system can thus be expressed as
Welding experiments
Welding experiments were done to verify the adaptive interval model control algorithm. All the experimental conditions including the material, peak current and duration, base current etc. are exactly the same as those for the step response experiments except for (a) the base current period is now determined by the adaptive interval model control algorithm and (b) different values are now used for the travel speed in different experiments. The resolution of the control variable is 1 ms because
Conclusion
- (a)
A control system has been developed to directly control the weld pool surface as characterized by the arc voltage change during the peak current period and to indirectly control the depth of the weld penetration for the complex but highly productive GMAW-P.
- (b)
Because the system only requires measurements of arc voltage signals, it provides a practical solution for a relatively poor manufacturing environment. Also, because of the fundamental role of the GMAW-P in welding and the lack of acceptable
Zhijiang Wang is currently a lecturer in Tianjin University where he received his B.S. degree in Metal Material Engineering (Welding Major) at 2003. He received his Ph.D. and M.S. degree in Material Process Engineering (Welding Major) from the State Key Laboratory of Advanced Welding Production Technology at the Harbin Institute of Technology, China. Dr. Wang, as a visiting scholar, worked with Dr. YuMing Zhang at the University of Kentucky for more than two years since 2008. His research
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Zhijiang Wang is currently a lecturer in Tianjin University where he received his B.S. degree in Metal Material Engineering (Welding Major) at 2003. He received his Ph.D. and M.S. degree in Material Process Engineering (Welding Major) from the State Key Laboratory of Advanced Welding Production Technology at the Harbin Institute of Technology, China. Dr. Wang, as a visiting scholar, worked with Dr. YuMing Zhang at the University of Kentucky for more than two years since 2008. His research interests include innovative welding processes, monitoring and control of welding processes, and welding automation. His email is [email protected].
YuMing Zhang has been with the University of Kentucky since 1991 where he is currently a Professor and the James R. Boyd Professor in Electrical Engineering. He was a faculty member from 1984 to 1991 in the State Key Laboratory for Advanced Welding Production Technology at the Harbin Institute of Technology, China where he received his Ph.D. degree in Welding Major, and M.S. and B.S. degrees in Control Major. Dr. Zhang is a Fellow of the American Welding Society (AWS), a senior member of the IEEE and the SME, and a member of the ASME. He received The Donald Julius Groen Prize from The Institution of Mechanical Engineers, United Kingdom; The A.F. Davis Silver Medal award from the American Welding Society; the Adams Memorial Membership Award from the American Welding Society; and the 15th IFAC Triennial World Congress Best Poster Paper Prize and Application Paper Honorable Mention from the International Federation of Automatic Control.
Lin Wu has been with the Harbin Institute of Technology, Harbin, China since 1959 where he has been a junior lecturer, lecturer, associate professor, and professor in welding engineering. Professor Wu served the Institute as the founding director of the Center for Robotics Research, the founding director of the State Key Laboratory for Advanced Welding Production Technology, a Vice President, and the Chairman of the Board. In addition, he was one of the initial members in the Expert Commission for Automation in China’s State High-Tech Development Plan (the 863 Program), the President of the Chinese Welding Society, and a Vice President of the International Institute of Welding (IIW). Professor Wu has been recognized by the prestigious “China Welding Lifetime Achievement Award”. His contributions to welding automation, welding robots, automatic weld quality inspection, adaptive control of weld penetration, welding robot vision, intelligent welding, welding remote-control, intelligent robots, spot welding robots, and special robots have brought him 18 national and provincial prizes, 13 books and over 400 papers.
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This work is funded by the National Science Foundation (Arlington, VA, USA) under grant CMMI-0726123 entitled “Measurement and Control of Dynamic Weld Pool Surface in Gas Metal Arc Welding”. This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor Pedro Albertos under the direction of Editor Toshiharu Sugie.
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