Machine-assisted travel speed control in manual welding torch operation
详细信息    查看全文
  • 作者:S. J. Chen (1)
    N. Huang (1) (2)
    Y. K. Liu (2)
    Y. M. Zhang (2)

    1. Welding Research Institute
    ; Beijing University of Technology ; Beijing ; 100124 ; China
    2. Institute for Sustainable Manufacturing and Department of Electrical Engineering
    ; University of Kentucky ; Lexington ; KY ; 40506 ; USA
  • 关键词:Welding ; Sensing ; Control ; Modeling ; Human welder ; Manual welding
  • 刊名:The International Journal of Advanced Manufacturing Technology
  • 出版年:2015
  • 出版时间:February 2015
  • 年:2015
  • 卷:76
  • 期:5-8
  • 页码:1371-1381
  • 全文大小:3,304 KB
  • 参考文献:1. O鈥?Brien R (ed) (1998) / Welding Handbook, 8th Edition 2 - Welding Processes. AWS
    2. Renwick R, Richardson R (1983) Experimental investigation of GTA weld pool oscillations. Weld J 62(2):29s鈥?5s
    3. Hardt D, Katz J (1984) Ultrasonic measurement of weld penetration. Weld J 63(9):273s鈥?81s
    4. Nagarajan S, Banerjee P, Chen W, Chin B (1992) Control of the welding process using infrared sensors. IEEE Trans Robot Autom 8(1):86鈥?3 CrossRef
    5. Ghanty P, Vasudevan M, Mukherjee DP (2008) Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool. Sci Technol Weld Join 13(4):395鈥?01 CrossRef
    6. Zhao CX, Richardson IM, Kenjeres S, Kleijn CR, Saldi Z (2009) A stereo vision method for tracking particle on the weld pool surface. J Appl Phys 105(12):104鈥?23
    7. Wang J, Wang W, Chen S (2009) Inspection of welding pool height from shading in pulsed GTAW with wire filler. Ind Robot: Int J 36(3):270鈥?76 CrossRef
    8. Li LP, Yang XQ, Zhang FY, Lin T (2011) Research on surface recover of aluminum alloy p-GTAW pool based on SFS. Robotic Weld Intell Autom Lect Notes Electr Eng 88:307鈥?14
    9. Zhang WJ, Liu YK, Zhang YM, (2013) 鈥淩eal-time Measurement of the Weld Pool Surface in GTAW Process,鈥?in / Proc. 2013 I.E. International Instrumentation and Measurement Technology Conference (I2MTC 2013), Minneapolis, MN, May 6鈥?
    10. Pietrzak KA, Packer SM (1994) Vision-based weld pool width control. J Eng Ind-Trans ASME 116(1):86鈥?2 CrossRef
    11. Chen H, Lv F, Lin T et al (2009) Closed-loop control of robotic arc welding system with full-penetration monitoring. J Intell Robot Syst 56(5):565鈥?78 CrossRef
    12. Liu YK, Zhang YM (2013) Control of 3D weld pool surface. Control Eng Pract 21(11):1469鈥?480 CrossRef
    13. Liu YK, Zhang YM (2014). Model-based predictive control of weld penetration in gas tungsten arc welding. IEEE Trans Control Syst Technol 22(3):955鈥?66
    14. Liu YK, Zhang WJ, Zhang YM (2013). Dynamic neuro-fuzzy based human intelligence modeling and control in GTAW. IEEE Trans Autom Sci Eng. doi:10.1109/TASE.2013.2279157
    15. Liu K, Zhang YM, Kvidahl L (2014). Skilled human welder intelligence modeling and control: part I鈥攎odeling. Weld J 93:46s鈥?2s
    16. Liu K, Zhang YM, Kvidahl L (2014). Skilled human welder intelligence modeling and control: part II鈥攁nalysis and control applications. Weld J 93:s162鈥搒170
    17. Yong LR (1969) On adaptive manual control. IEEE Trans Man Mach System 10:292鈥?31 CrossRef
    18. Kim WS, Tendick F, Ellis SR, Stark LW (1987) A comparison of position and rate control of tele-manipulation with consideration of manipulator system dynamics. IEEE J Robot Autom 3:426鈥?36 CrossRef
    19. Inooka H, Koitabashi T (1990) Experimental studies of manual optimization in control tasks. IEEE Control System Magazine 10:20鈥?3 CrossRef
    20. Kelly CR (1968) Manual and automatic control, a theory of manual control and its application to manual and automatic systems. Wiley, New York
    21. McRuer DT, Krendel ES (1957) Dynamic response of human operators, WADC-TR-56-524. US Airforce
    22. Chen LK, Ulsoy AG (2000) Identification of a nonlinear driver model via narmax modeling. Proceedings of American Control Conference :2533鈥?
    23. Tsuji T, Tanaka Y (2005) Tracking control properties of human-robotic systems based on impedance control. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 35(4):523鈥?35 CrossRef
    24. Itoh E, Suzuki S (2005) Nonlinear approach for human internal models: feed forward and feedback roles in pilot maneuver. Systems, Man and Cybernetics 2005. IEEE Int Conf 3:2455鈥?462
    25. Delice II, Ertugrul S (2007) Intelligent modeling of human driver: a survey. 2007 I.E. Intelligent Vehicles Symposium 648鈥?1
    26. Ertugrul S (2008) Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment. Eng Appl Artif Intell 21:259鈥?68 CrossRef
    27. Daniel T (2014) Leap Motion: 3D hands-free motion control, unbound. http://news.cnet.com/8301-11386_3-57437404-76/leap-motion-3d-hands-free-motion-control-unbound/
    28. Bj枚rck 脜 (1996) Numerical Methods for Least Squares Problems. SIAM
    29. Zhang W, Liu YK, Wang X, Zhang YM (2012) Characterization of three-dimensional weld pool surface in gas tungsten arc welding. Weld J 91:195s鈥?03s
    30. Astrom KJ, Wittenmark B (1995) Adaptive control. Addison-Wesley, Reading
    31. Zhang WJ, Xiao J, Chen HP, Zhang YM (2014) 鈥淢easurement of three-dimensional welding torch orientation for manual arc welding process,鈥?Measurement Science and Technology, 25 (2014) 035010 (17pp), doi:10.1088/0957-0233/25/3/035010
  • 刊物类别:Engineering
  • 刊物主题:Industrial and Production Engineering
    Production and Logistics
    Mechanical Engineering
    Computer-Aided Engineering and Design
  • 出版者:Springer London
  • ISSN:1433-3015
文摘
Welding is a skill-demanding, labor-intensive operation which should be automated whenever possible. Unfortunately, manual welding will still be irreplaceable in applications where the needed versatility and accessibility cannot be achieved by robots. On the other hand, the skills needed for manual welding typically require a long time to develop. In particular, maintaining the torch to travel in desired speed is a challenge. This paper thus proposes to use a feedback control system to assist the welder in regulating his/her arm movement in real time to achieve the desired torch travel speed. To this end, the travel speed (system output) is tracked by a motion sensor. The welder is instructed by a visual command to adjust the travel speed as system input. An auto-regressive moving average model has been used to correlate the system output to the system input as the human welder response model. To determine how the input needs to be adjusted to assisting the human welder to achieve the desired speed, a feedback control algorithm has been developed. Experiments verified that the proposed feedback control system is capable of assisting the human welder to track the desired travel speed.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700