Skip to main content

Process Model for Evaluating the Peen Velocity in Shot Peening Machine

  • Conference paper
  • First Online:
Advanced Surface Enhancement (INCASE 2019)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

Peening velocity (shot velocity) is one of the key parameters in shot peening process, which directly relates to intensity and coverage area. A desired intensity and/or coverage area can be attained by controlling the peening velocity to the right value. However, this is a challenging task as the peening velocity is the function of many different variables (peening system, nozzle design, air pressure, media (shot) flow rate, shot size, etc.). In this study, we develop a process model that links the input/operating parameters of the peening machine to the average shot stream velocity upon impact. In particular, the formulation of shot stream velocity is derived as the function of input air pressure and media flow rate, which also accounted for the peening system and nature of the flow inside (e.g., nozzle shape, pressure loss, energy transfer, and turbulence, etc.). The model is validated against the experimental data for different inlet pressure as well as the media flow rates. The calculated results are in good agreement with experimental data. Furthermore, the model validity and reliability are examined for the wide range of input parameters and the system parameter. The results also indicated that the developed process model can be applied for different peening machines with different nozzle design by defining relevant model constants. There are a few key applications for the process model; which are (1) the model can support the operators to rapidly estimate and setup the working conditions of the machine to attain the desired peening intensity and coverage area to avoid the cost and time in doing experiments based on trials and errors, and (2) The model also can be used in model predictive control (MPC) to develop the controller for the peening machine.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Harrison, J.: Controlled shot-peening: cold working to improve fatigue strength. Heat Treat. 19, 16–18 (1987)

    Google Scholar 

  2. David, K.: Quantification of shot peening coverage. The Shot Peener, Fall (2014). https://www.electronics-inc.com/wp-content/uploads/QuantificationOfShotPeeningCoverage.pdf

  3. Nguyen, V.B., Poh, H.J., Zhang, Y.W.: Predicting shot peening coverage using multi-phase computational fluid dynamics simulations. Powder Technol. 256, 100–112 (2014). https://doi.org/10.1016/j.powtec.2014.01.097

    Article  Google Scholar 

  4. David, K.: Variability of a shot stream’s measured peening intensity. The Shot Peener, Summer (2011). https://www.shotpeener.com/library/pdf/2011120.pdf

  5. David, K.: Peening intensity: true meaning and measurement Strategy. The Shot Peener, Summer (2016). https://www.shotpeener.com/library/detail.php?anc=2016030

  6. David, K.: Curve fitting for shot peening data analysis. The Shot Peener, 6. Spring (2002). https://www.shotpeener.com/library/pdf/2002091.pdf

  7. David, K., Abyaneh, M.Y.: Theoretical basis of shot peening coverage control. Shot Peener, vol. 13, no. 3, pp. 5–70 (1999). https://www.shotpeener.com/library/pdf/1995043.pdf

  8. David, K.: Theoretical principal of shot peening coverage. Shot Peener, vol. 19, no. 2, pp. 24–26 (2005). https://www.shotpeener.com/library/detail.php?anc=2005145

  9. Bill, B., Kevin, Y.: Particle velocity sensor for improving shot peening process control. Shot Peener, Technological aspects (2005). https://www.shotpeener.com/library/pdf/2005114.pdf

  10. Brunton, S.L., Joshua, L.P., Kutz J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 113, no. 15, pp. 3932–3937 (2016). https://doi.org/10.1073/pnas.1517384113

    Article  MathSciNet  Google Scholar 

  11. Tobias, G.: Model Predictive Control of High Power Converters and Industrial Drives. Wiley, London (2016). ISBN 978-1-119-01090-6

    Google Scholar 

  12. Wang, L.: Model Predictive Control System Design and Implementation Using MATLAB®, p. xii. Springer Science & Business Media, London (2009)

    Google Scholar 

  13. Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit, 474. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rspa.2018.0335

    Article  MathSciNet  Google Scholar 

  14. Zhang, L., Schaeffer H.: On the convergence of the SINDy algorithm. Journal CoRR (2018). http://arxiv.org/abs/1805.06445

Download references

Acknowledgment

This work is supported by the project entitled: “Machine Learning Assisted Control of Shot Peening Process” under Grant number A1894a0032, which is lead by Dr. Kang Chang Wei (IHPC, A*STAR) and Dr. Ampara (ARTC, A*STAR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nguyen Van Bo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Van Bo, N., Te, B., Teo, A., Ahluwalia, K., Aramcharoen, A., Chang Wei, K. (2020). Process Model for Evaluating the Peen Velocity in Shot Peening Machine. In: Itoh, S., Shukla, S. (eds) Advanced Surface Enhancement. INCASE 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0054-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0054-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0053-4

  • Online ISBN: 978-981-15-0054-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics