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Advanced model-based controller for cyber-physical shot peening process

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Abstract

This study reports new development of a practical model-based controller (namely model predictive control, MPC) for shot peening, which is the first application to such a process for fully automated operation. In particular, the feedback MPC is developed based on a process model that links inlet air pressure of the machine to air pressure at a peening nozzle. In addition, a proxy model is developed to link measurement variable(s) to peening intensity as the intensity cannot be measured online for real-time feedback control. During process control, the process model is used to simulate future dynamics of the peening process to guide the controller for optimal control action, while the proxy model translates the setting intensity to air pressure reference set-point for real-time tracking. Both the controller and model development rely on the physical machine’s constraints and capabilities. The pressure sensors and sensor locations are carefully selected to ensure controllability and observability. Single input/single output feedback MPC with future process pre-view capability is developed and integrated into the actual shot peening machine. The MPC has been demonstrated and validated using both in-silico and onsite controls for different scenarios. The obtained results show that the developed MPC is stable, robust, and accurate as it can automatically adjust inlet air pressure to attain the desired intensity. Finally, MPC can also help to reduce up to 25% of production cost by eliminating the cost, time, materials waste, and labor in performing experimental trials to build the saturation curve for actual operational guidance.

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References

  1. Drozda T, Wick C, Benedict JT, Veilleux RF, Bakerjian R, et al. (1985) Tool and Manufacturing Engineers Handbook: Materials, Finishing and Coating (Vol. 3). 4th Ed. Society of Manufacturing Engineers. Dearborn Michigan

  2. Kyriarou S (1996) Shot peening mechanics-a theoretical study. Proceedings of the 6th International Conference on Shot Peening. pp. 505–516

  3. Baiker S, Paul M, and et al. (2009) Shot Peening: A Dynamic Application and Its Future, 2nd Ed. MFN Publishing House. Switzerland

  4. Marsh KJ (1993) Shot peening: techniques and applications. Engineering Materials Advisory Service Ltd. United Kingdom. p.320

  5. Kirk D (2016) Peening intensity true meaning and measurement strategy. Shot Peener Mag 30(3):26–31

    Google Scholar 

  6. Surface Enhancement Committee (2017) Procedure for using standard shot peening test strip. SAE J443. SAE Int. https://doi.org/10.4271/J443_201708

  7. Champaigne J (1992) Shot peening intensity measurement. Shot Peener Mag 6(4):1–6

    Google Scholar 

  8. Kumar B (2014) Control systems in shot peening - a discussion. Shot Peener Mag 28(2):23–25

    Google Scholar 

  9. Tobias G (2016) Model predictive control of high-power converters and industrial drives. Wiley, London ISBN 978-1-119-01090-6

    Google Scholar 

  10. Garcia CE, Prett DM, Morari M (1989) Model predictive control: theory and practice – a survey. J Autom 25(3):335–348. https://doi.org/10.1016/0005-1098(89)90002-2

    Article  MATH  Google Scholar 

  11. Morari M, Lee JH (1999) Model predictive control: past, present and future. J Comput Chem Eng 23(4-5):667–682. https://doi.org/10.1016/S0098-1354(98)00301-9

    Article  Google Scholar 

  12. Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. J Control Eng Pract 11(7):733–764. https://doi.org/10.1016/S0967-0661(02)00186-7

    Article  Google Scholar 

  13. Abdennour R, Ksouri M, M’sahli F (2002) Experimental nonlinear model based predictive control for a class of semi-batch chemical reactors. Int J Adv Manuf Technol 20:459–463. https://doi.org/10.1007/s001700200178

    Article  Google Scholar 

  14. Abdennour R, Ksouri M, M’Sahli F (2002) Nonlinear model-based predictive control using a generalised Hammerstein model and its application to a semi-batch reactor. Int J Adv Manuf Technol 20:844–852. https://doi.org/10.1007/s001700200225

    Article  Google Scholar 

  15. Khouaja A, Garna T, Ragot J, Messaoud H (2017) Robust predictive controller for nonlinear uncertain process based on S-PARAFAC Volterra models. Int J Adv Manuf Technol 90:2309–2323. https://doi.org/10.1007/s00170-016-9556-6

    Article  MATH  Google Scholar 

  16. Nguyen VB, Tran SBQ, Khan SA, Rong J, Lou J (2020) POD-DEIM model order reduction technique for model predictive control in continuous chemical processing. Comput Chem Eng 133:106638. https://doi.org/10.1016/j.compchemeng.2019.106638

    Article  Google Scholar 

  17. Becha T, Hamri H, Kara R, Dutilleul SC (2017) Model predictive control of an electroplating line without stopping the production. Int J Adv Manuf Technol 91:4095–4105. https://doi.org/10.1007/s00170-016-9980-7

    Article  Google Scholar 

  18. Cauffriez L, Willaeys D (2006) A predictive model for improving production line performance. Int J Adv Manuf Technol 29:969–979. https://doi.org/10.1007/s00170-005-2583-3

    Article  Google Scholar 

  19. Kim DY, Kim DM, Park HW (2018) Predictive cutting force model for a cryogenic machining process incorporating the phase transformation of Ti-6Al-4 V. Int J Adv Manuf Technol 96:1293–1304. https://doi.org/10.1007/s00170-018-1606-9

    Article  Google Scholar 

  20. Mirkoohi E, Bocchini P, Liang SY (2019) Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102:1557–1566. https://doi.org/10.1007/s00170-019-03296-y

    Article  Google Scholar 

  21. Liu Y, Wang L, Brandt M (2019) Model predictive control of laser metal deposition. Int J Adv Manuf Technol 105:1055–1067. https://doi.org/10.1007/s00170-019-04279-9

    Article  Google Scholar 

  22. Lu H, Kearney M, Li Y, Liu S, Daniel WJT, Meehan PA (2016) Model predictive control of incremental sheet forming for geometric accuracy improvement. Int J Adv Manuf Technol 82:1781–1794. https://doi.org/10.1007/s00170-015-7431-5

    Article  Google Scholar 

  23. Lu H, Kearney M, Liu S, Daniel WJT, Meehan PA (2017) Two-directional toolpath correction in single-point incremental forming using model predictive control. Int J Adv Manuf Technol 91:91–106. https://doi.org/10.1007/s00170-016-9672-3

    Article  Google Scholar 

  24. Žapčević S, Butala P (2013) Adaptive process control based on a self-learning mechanism in autonomous manufacturing systems. Int J Adv Manuf Technol 66:1725–1743. https://doi.org/10.1007/s00170-012-4453-0

    Article  Google Scholar 

  25. Tao F, Zhang H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art. IEEE Trans Ind Inf 15(4):2405–2415. https://doi.org/10.1109/TII.2018.2873186

    Article  Google Scholar 

  26. Tao F, Qi Q, Wang L, Nee AYC (2019) Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5(4):653–661. https://doi.org/10.1016/j.eng.2019.01.014

    Article  Google Scholar 

  27. Nguyen VB, Poh HJ, Zhang YW (2014) Predicting shot peening coverage using multi-phase computational fluid dynamics simulations. J Powder Technol 256:100–112. https://doi.org/10.1016/j.powtec.2014.01.097

    Article  Google Scholar 

  28. Brunton SL, Joshua LP, Kutz JN (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci U S A 113(15):3932–3937. https://doi.org/10.1073/pnas.1517384113

    Article  MathSciNet  MATH  Google Scholar 

  29. Kaiser E, Kutz JN, Brunton SL (2018) Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proc R Soc A Math Phys Eng Sci 474:20180335. https://doi.org/10.1098/rspa.2018.0335

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang L, Schaeffer H (2019) On the Convergence of the SINDy Algorithm. Journal of Multiscale Modeling & Simulation 17(3):948–972. https://doi.org/10.1137/18M1189828

  31. Nguyen VB, Ba T, Teo A, Ahluwalia K, Aramcharoen A, Kang CW (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

    Google Scholar 

  32. Chen T, Carlos G (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 16. ACM Press. https://doi.org/10.1145/2939672.2939785

  33. Unpingco J (2014) Python for Signal Processing: Featuring IPython Notebooks, Springer Nature Switzerland

  34. Mahnke W, Leitner SH (2009) OPC Unified Architecture The future standard for communication and information modeling in automation. ABB Review 3:56–61

  35. Leitner SH, Mahnke W (2006) OPC UA - Service-oriented Architecture for Industrial Applications. ABB Corporate Res. Center 48: 61–66

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Funding

This work is supported by the project entitled “Machine Learning Assisted Control of Shot Peening Process” under Grant number A1894a0032 at Institute of High-Performance Computing (IHPC) and Advanced Remanufacturing and Technology Centre (ARTC), A*STAR Singapore.

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Authors and Affiliations

  1. Fluid Dynamics Department, Institute of High-Performance Computing, A*STAR, Singapore, 138632, Singapore

    Van Bo Nguyen, Te Ba, Si Bui Quang Tran & Chang Wei Kang

  2. Data-Driven Surface Enhancement Group, Advanced Remanufacturing and Technology Centre, A*STAR, Singapore, 637143, Singapore

    Augustine Teo, Ampara Aramcharoen & Kunal Ahluwalia

Authors
  1. Van Bo Nguyen
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  2. Augustine Teo
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  3. Te Ba
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  4. Ampara Aramcharoen
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  6. Si Bui Quang Tran
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  7. Chang Wei Kang
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Contributions

1) Van Bo Nguyen: developed process model, developed control system, developed control scenarios, performed both in-silico and onsite controls, built ideas for this paper, analyzed data, and wrote original and revised paper.

2) Te Ba: developed proxy model, analyzed data, and revised paper.

3) Augustine Teo: design experiments, performed experiments, and performed onsite control.

4) Kunal Ahluwalia: provided some idea on pressure sensor placement, setup experiment, analyzed data, and revised paper.

5) Ampara Aramcharoen: provide ideas on experimental trials and sensor selection.

6) Si Bui Quang Tran: helped to perform onsite controls, collected data, and revised paper.

7) Chang Wei Kang: managed and provided direction for research development, provided ideas on control design, analyzed data and revised paper.

Corresponding author

Correspondence to Van Bo Nguyen.

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Nguyen, V.B., Teo, A., Ba, T. et al. Advanced model-based controller for cyber-physical shot peening process. Int J Adv Manuf Technol 114, 2929–2943 (2021). https://doi.org/10.1007/s00170-021-07009-2

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  • DOI: https://doi.org/10.1007/s00170-021-07009-2

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