Modeling and simulation of industrial waterjet stripping for complex geometries

  • Braden James
  • Harry A. PiersonEmail author


Industrial waterjet stripping/cleaning is a prime example of a dull, dirty, dangerous manufacturing process that is ripe for automation, yet it remains a manual task in most instances due to complex workpiece geometry and/or low-volume, high-mix production. Recently developed automated tool trajectory planning algorithms and collaborative path planning frameworks offer a potential solution but are of limited use without corresponding process models and simulation tools to evaluate toolpath quality. Existing process models do not consider the spray impingement angle or the cumulative effect of successive tool passes—both of which are inevitable when spraying geometries that possess concave and/or discontinuous features. This research proposes a novel process model that includes impingement angle and accounts for the cumulative, ablative nature of the process. It also develops a simulation algorithm that applies this model to complex geometries while considering shading effects caused by protrusions and overhangs. Model parameters are determined via a design of experiments approach and nonlinear regression, and verification experiments on complex test parts show good agreement between predicted and measured results. Paired with the aforementioned trajectory planning tools, this research represents a complete robotic process planning solution for waterjet stripping/cleaning of complex parts in high-mix, low-volume manufacturing.


Waterjet Process modeling Process simulation Process automation 



This research was partially supported by National Science Foundation Grant No. 0732686. The authors also gratefully acknowledge the participation of Red River Army Depot in the physical experiments. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.


  1. 1.
    Woods A, Pierson HA (2018) Developing an ergonomic model and automation justification for industrial spraying operations: a case study. In: Proceedings of the 2018 industrial and systems engineering conference, Orlando, FLGoogle Scholar
  2. 2.
    Leu MC, Meng P, Geskin ES, Tismeneskiy L (1998) Mathematical modeling and experimental verification of stationary waterjet cleaning process. J Manuf Sci Eng 120:571–579. CrossRefGoogle Scholar
  3. 3.
    Meng P, Geskin ES, Leu MC, Li F, Tismeneskiy L (1998) An analytical and experimental study of cleaning with moving waterjets. J Manuf Sci Eng 120:580–589CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Brown SL, Pierson HA (2018) A collaborative framework for robotic task specification. Procedia Manuf 17:270–277CrossRefGoogle Scholar
  6. 6.
    Brown S (2018) Collaborative robotic path planning for industrial spraying operations on complex geometries. MS Thesis, University of Arkansas, FayettevilleGoogle Scholar
  7. 7.
    Daoming G, Jie C (2006) ANFIS for high-pressure waterjet cleaning prediction. Surf Coat Technol 201(3–4):1629–1634. CrossRefGoogle Scholar
  8. 8.
    Guha A, Barron RM, Balachandar R (2011) An experimental and numerical study of water jet cleaning process. J Mater Process Technol 211(4):610–618. CrossRefGoogle Scholar
  9. 9.
    Hlaváč LM (2015) Application of water jet description on the de-scaling process. Int J Adv Manuf Technol 80(5–8):721–735CrossRefGoogle Scholar
  10. 10.
    Miao X, Ye F, Wu M, Song L, Qiang Z (2019) The method of 3D nozzle tilt cutting of abrasive water jet. Int J Adv Manuf Technol:1–6Google Scholar
  11. 11.
    Hashish M, DuPlessis MP (1979) Prediction equations relating high velocity jet cutting performance to stand off distance and multipasses. J Eng Ind 101(3):311–318CrossRefGoogle Scholar
  12. 12.
    Parikh PJ, Lam SS (2009) Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol 40(5–6):497–502CrossRefGoogle Scholar
  13. 13.
    Xia W, Yu SR, Liao XP (2010) Paint deposition pattern modeling and estimation for robotic air spray painting on free-form surface using the curvature circle method. Ind Robot 37(2):202–213. CrossRefGoogle Scholar
  14. 14.
    Chen W, Liu H, Tang Y, Liu J (2017) Trajectory optimization of electrostatic spray painting robots on curved surface. Coatings 7(10):155–155. CrossRefGoogle Scholar
  15. 15.
    He Z, Lu B, Hong J, Wang Y, Tang Y (2007) A novel arc-spraying robot for rapid tooling. Int J Adv Manuf Technol 31(9–10):1012–1020. CrossRefGoogle Scholar
  16. 16.
    Chen W, Zhao D (2013) Path planning for spray painting robot of workpiece surfaces. Math Probl Eng 2013:1–6. CrossRefGoogle Scholar
  17. 17.
    Sheng W, Xi N, Song M, Chen Y, MacNeille P (2000) Automated CAD-guided robot path planning for spray painting of compound surfaces. IEEE Int Conf Intell Robots Syst 3:1918–1923. CrossRefGoogle Scholar
  18. 18.
    Kabir AM, Langsfeld JD, Shriyam S, Rachakonda VS, Zhuang C, Kaipa KN, Marvel J, Gupta SK (2016) Planning algorithms for multi-setup multi-pass robotic cleaning with oscillatory moving tools. IEEE International Conference on Automation Science and Engineering 2016-Novem:751–757.
  19. 19.
    Summers DA (1995) WaterJetting technology, 1st edn. E & FN SponGoogle Scholar
  20. 20.
    Burnham KP, Anderson DR (2004) Multimodel inference. Sociol Methods Res 33(2):261–304. MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pierson HA, Gashler MS (2017) Deep learning in robotics: a review of recent research. Adv Robot 31(16):821–835. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of ArkansasFayettevilleUSA

Personalised recommendations