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Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

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Abstract

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.

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References

  1. Chen, S. B., Lou, Y. J., Wu, L., and Zhao, D. B.: Intelligent methodology for sensing, modeling and control of pulsed GTAW: Part 1 - bead-on-plate welding, Welding J. 79(6) (2000), 151-163.

    Google Scholar 

  2. Chen, S. B., Wu, L., and Wang, Q. L.: Self-learning fuzzy neural networks for control of uncertain systems with time delays, IEEE Trans. Systems Man Cybernet., Part B: Cybernetics 27(1) (1997), 142-148.

    Google Scholar 

  3. Chen, S. B., Zhao, D. B., Wu, L., and Lou, Y. J.: Intelligent methodology for sensing, modeling and control of pulsed GTAW: Part 2 - butt joint welding, Welding J. 79(6) (2000), 164-174.

    Google Scholar 

  4. Kopacek, P.: Intelligent manufacturing: Present state and future trends, J. Intelligent Robotic Systems 26(3) (1999), 217-229.

    Google Scholar 

  5. Kovacevic, R. and Zhang, Y. M.: Real-time image processing for monitoring the free weld pool surface, ASME J. Manufacturing Sci. Engrg. 119(5) (1997), 161-169.

    Google Scholar 

  6. Kovacevic, R., Zhang, Y. M., and Ruan, S.: Sensing and control of weld pool geometry for automated GTA welding, ASME J. Engrg. Industry 117(2) (1995), 210-222.

    Google Scholar 

  7. Lim, T. G. and Cho, H. S.: Estimation of weld pool sizes in GMA welding process using neural networks, in: The 3rd Internat. Conf. on Trends in Welding Research, Gatlinburg, TN, 1993, pp. 135-142.

  8. Murakami, S.: Weld-line tracking control of arc welding robot using fuzzy logic controller, Fuzzy Sets Systems 32(2) (1989), 31-36.

    Google Scholar 

  9. Pietrzak, K. A. and Packer, S. M.: Vision-based weld pool width control, ASME J. Engrg. Industry 116 (1994), 86-92.

    Google Scholar 

  10. Przakovic, D. and Khani, D. T.:Weld pool edge detection for automated control of weld, IEEE Trans. Robotics Automat. 7(3) (1991), 397-403.

    Google Scholar 

  11. Suga, Y., Mukai, M., and Usui, S.: Measurement of molten pool shape and penetration control applying neural network in TIG weld of thin steel plate, ISIJ Internat. 39(10) (1999), 1075-1080.

    Google Scholar 

  12. Suga, Y., Mukai, M., Usui, S., and Ogawa, K.: Estimation and adaptive control of penetration in GTAWby monitoring dimension of molten pool, in: Proc. of the Internat. Conf. on Offshore Mechanics and Arctic Engineering - OMAE, Vol. 3, 13-17 April 1997, ASME, pp. 95-100.

  13. Suga, Y. and Naruse, M.: Application of neural network to visual sensing of weld line and automatic tracking in robot welding, Welding in the World 34 (1994), 275-284.

    Google Scholar 

  14. Suzuki, A., Hardt, D. E., and Valavani, L.: Application of adaptive control theory to on-line GTA weld geometry regulation, ASME J. Dyn. SystemsMeasm. Control 113(1) (1991), 93-103.

    Google Scholar 

  15. Tzafestas, C. S. and Tzafestas, S. G.: Intelligent robotic assembly and disassembly: General architecture and implementation case studies, in: S. G. Tzafestas (ed.), Advances in Manufacturing: Decision, Control and Information Technology, Springer, Berlin/London, 1999, pp. 267-282.

    Google Scholar 

  16. Tzafestas, S. G. and Kyriannakis, E.: Regulation of GMA welding thermal characteristics via a hierarchical MIMO predictive control scheme assuring stability, IEEE Trans. Industr. Electron 47(3) (2000), 668-678.

    Google Scholar 

  17. Tzafestas, S. G., Raptis, S., and Pantazopoulos, J.: A vision-based path planning algorithm for a robot-mounted welding gun, Images Process. Commun. 2(4) (1996), 61-72.

    Google Scholar 

  18. Vilkas, E. P.: Automation of gas tungsten arc welding process, Welding J. 45(5) (1966), 410-416.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Welding Engineering, Shanghai Jiaotong University, Shanghai, 200030, China

    S. B. Chen, T. Qiu & T. Lin

  2. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Y. Zhang

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  1. S. B. Chen
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  2. Y. Zhang
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  3. T. Qiu
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  4. T. Lin
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Chen, S.B., Zhang, Y., Qiu, T. et al. Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process. Journal of Intelligent and Robotic Systems 36, 191–208 (2003). https://doi.org/10.1023/A:1022652706683

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