Skip to main content
SpringerLink
Log in

Experimental study and prediction model of the cleaning effect induced by self-resonating cavitating waterjet

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

Self-resonating cavitation waterjet (SRCW) has rapidly developed and been widely used in cleaning fields due to its non-thermal and environmentally friendly machining. However, its fuzzy relationship between cleaning effect and working parameters is a significant limit to cleaning quality improving and extensive application of SRCW. Addressing this issue, an evaluation method of cleaning effect induced by SRCW was proposed in this paper, which includes two evaluation parameters, cleaning rate (Rc, mm2/s) and primer damage rate (Rd). Using this method, experiments on the effects of four working parameters (pressure, traverse rate, standoff distance and impact angle) on the cleaning effect were carried out, and the results showed that Rc and Rd is proportional to pressure, with the decrease of attacking angle or the increase of traverse rate and standoff distance, Rd always decreases while Rc increases first and then decreases. A prediction model of SRCW cleaning effect considering pressure, traverse rate, standoff distance and impact angle was established with the use of GA-BP neural network. The prediction results indicate that the model has better adaptive ability and high prediction accuracy of over 95 %. And according to the prediction and different optimization objectives, the working parameters of SRCW were obtained. The study provided a new method to evaluate the cleaning effect of SRCW, and the working parameters were predicted and optimized by reliably modeling, which is of great significance to guide the practical application of SRCW.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Z. Hutyrova, J. Scucka, S. Hloch, P. Hlavacek and M. Zelenak, Turning of wood plastic composites by water jet and abrasive water jet, International Journal of Advanced Manufacturing Technology, 84 (2016) 1615–1623.

    Google Scholar 

  2. Y. Hu, Y. Kang, X.-C. Wang, X.-H. Li, X.-P. Long, G.-Y. Zhai and M. Huang, Mechanism and experimental investigation of ultra high pressure water jet on rubber cutting, International Journal of Precision Engineering and Manufacturing, 15 (2014) 1973–1978.

    Article  Google Scholar 

  3. J.-H. Kang, J.-S. Jang, K.-W. Kim, W.-S. Yoo and J.-W. Lee, Program development on cleaning pattern and performance evaluation for low pressure waterjet, Advances in Mechanical Engineering, 13 (2021).

  4. X. Long, M. Xu, Q. Lyu and J. Zou, Impact of the internal flow in a jet fish pump on the fish, Ocean Engineering, 126 (2016) 313–320.

    Article  Google Scholar 

  5. W. Liu, Y. Kang, X. Wang, B. Yuan and Z. Fang, Forming mechanism and radius prediction of hydraulically generated slots based on a two-walled jet, Journal of Natural Gas Science and Engineering, 36 (2016) 385–401.

    Article  Google Scholar 

  6. H. Soyama, Improvement of fatigue strength by using cavitating jets in air and water, Journal of Materials Science, 42 (2007) 6638–6641.

    Article  Google Scholar 

  7. J. Foldyna, L. Sitek, B. Svehla and T. Svehla, Utilization of ultrasound to enhance high-speed water jet effects, Ultrasonics Sonochemistry, 11 (2004) 131–137.

    Article  Google Scholar 

  8. Y. Lu, F. Huang, X. Liu and X. Ao, On the failure pattern of sandstone impacted by high-velocity water jet, International Journal of Impact Engineering, 76 (2015) 67–74.

    Article  Google Scholar 

  9. C. Kang, H. Liu, T. Zhang and Q. Li, Investigation of submerged waterjet cavitation through surface property and flow information in ambient water, Applied Surface Science, 425 (2017) 915–922.

    Article  Google Scholar 

  10. H. Liu, C. Kang, W. Zhang and T. Zhang, Flow structures and cavitation in submerged waterjet at high jet pressure, Experimental Thermal and Fluid Science, 88 (2017) 504–512.

    Article  Google Scholar 

  11. S. Puttinger and M. Saeedipour, Time-resolved PIV measurements of a deflected submerged jet interacting with liquidgas and liquid-liquid interfaces, Experimental and Computational Multiphase Flow, 4(2) (2022) 175–189.

    Article  Google Scholar 

  12. L. Cui, F. Ma and T. Cai, Investigation of pressure oscillation and cavitation characteristics for submerged self-resonating waterjet, Applied Sciences-Basel, 11 (2021) 6972.

    Article  Google Scholar 

  13. D. Li, Y. Kang, X. Wang, X. Ding and Z. Fang, Effects of nozzle inner surface roughness on the cavitation erosion characteristics of high speed submerged jets, Experimental Thermal and Fluid Science, 74 (2016) 444–452.

    Article  Google Scholar 

  14. D. Li, Y. Kang, X. Ding and W. Liu, Experimental study on the effects of feeding pipe diameter on the cavitation erosion performance of self-resonating cavitating waterjet, Experimental Thermal and Fluid Science, 82 (2017) 314–325.

    Article  Google Scholar 

  15. D. Li, Y. Kang, X. Ding, X. Wang and Z. Fang, Effects of area discontinuity at nozzle inlet on the characteristics of self-resonating cavitating waterjet, Chinese Journal of Mechanical Engineering, 29 (2016) 813–824.

    Article  Google Scholar 

  16. W. Liu, Y. Kang, M. Zhang, X. Wang, D. Li and L. Xie, Experimental and theoretical analysis on chamber pressure of a self-resonating cavitation waterjet, Ocean Engineering, 151 (2018) 33–45.

    Article  Google Scholar 

  17. Z. Fang, F. Zeng, T. Xiong, W. Wei, P. Jiang, Q. Wu, Y. Wang and Y. Fei, Large eddy simulation of self-excited oscillation inside Helmholtz oscillator, International Journal of Multiphase Flow, 126 (2020) 103253.

    Article  MathSciNet  Google Scholar 

  18. X. Wang, Y. Kang, M. Zhang, M. Yuan and D. Li, The effects of the downstream contraction ratio of organ-pipe nozzle on the pressure oscillations of self-resonating waterjets, Energies, 11 (2018) 3137.

    Article  Google Scholar 

  19. V. Gall, E. Rütten and H. P. Karbstein, Cavitation patterns in high-pressure homogenization nozzles with cylindrical orifices: Influence of mixing stream in simultaneous homogenization and mixing, Experimental and Computational Multiphase Flow, 4 (2021) 156–164.

    Article  Google Scholar 

  20. T. Cai, Y. Pan and F. Ma, Effects of nozzle lip geometry on the cavitation erosion characteristics of self-excited cavitating waterjet, Experimental Thermal and Fluid Science, 117 (2020) 110137.

    Article  Google Scholar 

  21. Z. Fang, X. Gao, X. Tao, D. Li, M. Zhang, T. Xiong and P. Jiang, Impact performance of helmholtz self-excited oscillation waterjets used for underground mining, Applied Sciences-Basel, 9 (2019) 3235.

    Article  Google Scholar 

  22. D. Gao and J. Chen, ANFIS for high-pressure waterjet cleaning prediction, Surface & Coatings Technology, 201 (2006) 1629–1634.

    Article  Google Scholar 

  23. K. Babets and E. S. Geskin, Application of fuzzy logic for modeling of waterjet depainting, Machining Science and Technology, 4 (2000) 81–100.

    Article  Google Scholar 

  24. T. P. Latchoumi, K. Balamurugan, K. Dinesh and T. P. Ezhilarasi, Particle swarm optimization approach for waterjet cavitation peening, Measurement, 141 (2019) 184–189.

    Article  Google Scholar 

  25. D. S. Balaji and T. Jeyapoovan, Multi-objective optimization in abrasive water jet peening on AA6063 alloy, International Conference on Advances in Materials Research (ICAMR), Bannari Amman Inst Technol, Sathyamangalam, INDIA, 47 (2019) 1928–1933.

    Google Scholar 

  26. R. Kant and S. S. Dhami, Multi-response optimization of parameters using GRA for abrasive water jet machining of EN31 steel, International Conference on Technology Innovation in Mechanical Engineering (TIME), Bhopal, INDIA (2021) 6141–6146.

  27. D. Zheng, Z.-D. Qian, Y. Liu and C.-B. Liu, Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network, Construction and Building Materials, 158 (2018) 614–623.

    Article  Google Scholar 

  28. H. Wang, Z. Zhang and L. Liu, Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network, Journal of Manufacturing Processes, 63 (2021) 109–120.

    Article  Google Scholar 

  29. H. Cao, L. Liu, B. Wu, Y. Gao and D. Qu, Process optimization of high-speed dry milling UD-CF/PEEK laminates using GA-BP neural network, Composites Part B-Engineering, 221 (2021).

  30. B. H. M. Sadeghi, A BP-neural network predictor model for plastic injection molding process, Journal of Materials Processing Technology, 103 (2000) 411–416.

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by Finance Science and Technology Project of Hainan Province, grant number ZDKJ202015, and Sanya Science and Education Innovation Park of Wuhan University of Technology, grant number 2020KF0039 and 2021KF0023. And this work was also supported by the Green Intelligent Inland Ship Innovation Program.

Author information

Authors and Affiliations

  1. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, 430063, China

    Yunan Yao, Hua Wang & Bin Wang

  2. Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya, 572025, China

    Yunan Yao & Zhenlong Fang

  3. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, China

    Zhenlong Fang

  4. Hubei Key Laboratory of Waterjet Theory and New Technology, Wuhan University, Wuhan, 430074, China

    Deng Li

Authors
  1. Yunan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenlong Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhenlong Fang or Deng Li.

Additional information

Yunan Yao is an Associate Professor in the School of Naval Architecture, Ocean and Energy Power Engineering at Wuhan University of Technology. His research interests include intelligent equipment operation and maintenance support and intelligent robots.

Zhenlong Fang is an Associate Professor in the School of Transportation and Logistics Engineering at Wuhan University of Technology. His major research orientations are flow mechanisms and cavitation characteristics of Helmholtz self-sustained oscillation jets.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, Y., Wang, H., Fang, Z. et al. Experimental study and prediction model of the cleaning effect induced by self-resonating cavitating waterjet. J Mech Sci Technol 36, 5097–5106 (2022). https://doi.org/10.1007/s12206-022-0922-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-022-0922-z

Keywords

Search

Navigation