Optimization of Tool Path Pitch of Spray Painting Robots for Automotive Painting Quality

  • Kiyang Park
  • Doyoung Jeon
Regular Papers Control Theory and Applications


The spray painting robots with mounted electrostatic rotating bell (ESRB) atomizers have been widely used to maintain high painting quality for various car models in rapid automotive production. The focus of this study is on developing the optimum coating path pitch while considering automobile painting quality. We define a deposition model optimized for the ESRB atomizer and introduce a method to optimize the path pitch with a cost function for painting thickness uniformity, which plays an important role in painting quality. In addition, experimental results are presented to verify the validity of the optimal path pitch simulation results. By using the results of this study, it is possible to improve the painting quality and thus the productivity of spray painting robots.


Deposition model ESRB atomizer path pitch optimization spray painting robot thickness uniformity 


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  1. [1]
    K. Im, M. Lai, S. J. Yu, and R. R. Matheson, “Simulation of spray transfer processes in electrostatic rotary bell sprayer,” Journal of Fluids Engineering, vol. 126, 2004.Google Scholar
  2. [2]
    X. Li, B. Zhang, T. A. Fuhlbrigge, O. Landsnes, and S. Riveland, “Paint deposition simulation for robotics automotive painting line,” Proc. of The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, June 4-7, 2014.Google Scholar
  3. [3]
    M. Andulkar, S. Chiddarwar, and A. Paigwar, “Optimal velocity trajectory generation for spray painting robot in offline mode,” AIR’ 15, July 02-04, 2015.Google Scholar
  4. [4]
    A. Gasparetto, R. Vidoni, D. Pillan, and E. Saccavini, “Automatic path and trajectory planning for robotic spray painting,” Proc. of 7th German Conference on Robotics, pp. 211–216, 2012.Google Scholar
  5. [5]
    N. Asakawa and Y. Takeuchi, “Teachless spray-painting of sculptured surface by an industrial robot,” Proc. IEEE Int. Conf. Robotics and Automation, vol. 3, pp. 1875–79, April 1997.CrossRefGoogle Scholar
  6. [6]
    S. H. Suh, I. K. Woo, and S. K. Noh, “Development of an automated trajectory planning system (ATPS) for spray painting robots,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 1948–1955, April 1991.Google Scholar
  7. [7]
    H. Hyotyniemi, “Minor moves-global results: robot trajectory planning,” Proc. of the 2nd International IEEE Conference on Tools for Artificial Intelligence, pp. 16–22, November 1990.CrossRefGoogle Scholar
  8. [8]
    W. Persoons and H. Van Brussel, “CAD-based robotic coating of highly curved surfaces,” Proc. of the 24th International Symposium on Industrial Robots, pp. 611–618, November 1993.Google Scholar
  9. [9]
    T. Balkan and M. A. S. Arikan, “Modeling of paint flow rate flux for circular paint sprays by using experimental paint thickness distribution,” Mechanics Research Communications, vol. 26, no. 5, pp. 609–617, 1999.CrossRefzbMATHGoogle Scholar
  10. [10]
    Y. Zhang, Y. Huang, F. Gao, and W. Wang, “New model for air spray gun of robotic spray-painting,” Chinese Journal of Mechanical Engineering, vol. 42, no. 11, pp. 226–233, 2006.CrossRefGoogle Scholar
  11. [11]
    Y. Zeng, J. Gong, N. Xu, and N. Wu, “Tool trajectory optimization of spray painting robot for many-times spray painting,” International Journal of Control and Automation, vol. 7, no. 8, pp. 193–208, 2014.CrossRefGoogle Scholar
  12. [12]
    E. Freund, D. Rokossa, and J. R. mann, “Process-oriented approach to an efficient off-line programming of industrial robots,” Proc. IEEE Industrial Electronics Society Conf. (IECON), vol. 1, pp. 208–213, 1998.CrossRefGoogle Scholar
  13. [13]
    M. A. Sahir and T. Balkan, “Process modeling, simulation, and paint thickness measurement for robotic spray painting,” J. Robot. Syst., vol. 17, no. 9, pp. 479–494, 2000.CrossRefzbMATHGoogle Scholar
  14. [14]
    D. C. Conner, P. N. Atkar, A. A. Rizzi, and H. Choset, “Development of deposition models for paint application on surfaces embedded in IR for use in automated trajectory planning,” Robotics Institute, Carnegie Mellon, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-02-08, June 2002.Google Scholar
  15. [15]
    D. C. Corner, A. Greenfield, P. N. Atkar, A. A. Rizzi, and H. Choset, “Paint deposition modeling for trajectory planning on automotive surfaces,” IEEE Transactions on Automation Science and Engineering, vol. 2, no. 4, 2005.Google Scholar
  16. [16]
    P. N. Atkar, A. Greenfield, D. C. Corner, H. Choset, and A. A. Rizzi, “Uniform coverage of automotive surface patches,” The International Journal of Robotics Research, vol. 24, no. 11, pp. 883–898, 2005.CrossRefGoogle Scholar
  17. [17]
    Y. Chen, W. Chen, K Chen, and B. Li, “Motion planning of redundant manipulators for painting uniform thick coating in irregular duct,” Hindawi Publishing Corporation Journal of Robotics, vol. 2016.Google Scholar
  18. [18]
    R. A. Waltz, J. L. Morales, J. Nocedal, and D. Orban, “An interior algorithm for nonlinear optimization that combines line search and trust region steps,” Mathematical Programming, vol. 107, no. 3, pp. 391–408, 2006.MathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    R. H. Byrd, M. E. Hribar, and J. Nocedal, “An interior point algorithm for large-scale nonlinear programming,” SIAM Journal on Optimization, vol. 9, no. 4, pp. 877–900, 1999.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringSogang UniversitySeoulKorea
  2. 2.Company of DOOLIM-YASKAWA Co., LtdGyeonggi-doKorea

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