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Camera in the control loop – methods and selected industrial applications

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Trends in Advanced Intelligent Control, Optimization and Automation (KKA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 577))

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Abstract

Our aim is to discuss briefly methods of using cameras in control systems. Then, we concentrate on a new approach to iterative learning control (ILC) for nonlinear repetitive production processes. Finally, we propose the methodology of applying a camera for tuning ILC and illustrate it by the example of a multilayer system for laser power control in selective laser melting (SLM).

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References

  • 1. Boltyanski V. and Poznyak A. The Robust Maximum Principle: Theory and Applications. Springer Science & Business Media, 2011.

    Google Scholar 

  • 2. Chapman K., Johnson W., and McLean T. A high speed statistical process control application of machine vision to electronics manufacturing. Computers & Industrial Engineering, 19(1):234–238, 1990.

    Google Scholar 

  • 3. Davies E. R. Machine vision: theory, algorithms, practicalities. Elsevier, 2004.

    Google Scholar 

  • 4. Hladowski L., Galkowski K., Cai Z., Rogers E., Freeman Ch., and Lewin P. Experimentally supported 2d systems based iterative learning control law design for error convergence and performance. Control Engineering Practice, 18(4):339–348, 2010.

    Google Scholar 

  • 5. Jurewicz P., Rafajłowicz W., Reiner J., and Rafajłowicz E. Simulations for tuning a laser power control system of the cladding process. In IFIP International Conference on Computer Information Systems and Industrial Management, pages 218–229. Springer, 2016.

    Google Scholar 

  • 6. King T. Vision-in-the-loop for control in manufacturing. Mechatronics, 13(10):1123–1147, 2003.

    Google Scholar 

  • 7. Kurzynowski T., Chlebus E., Kuźnicka B., and Reiner J. Parameters in selective laser melting for processing metallic powders. In SPIE LASE, pages 823914–823914. International Society for Optics and Photonics, 2012.

    Google Scholar 

  • 8. Luenberger D. Optimization by vector space methods. John Wiley & Sons, 1997.

    Google Scholar 

  • 9. OLeary P. Machine vision for feedback control in a steel rolling mill. Computers in Industry, 56(8):997–1004, 2005.

    Google Scholar 

  • 10. Owens D. Iterative learning control. An optimization paradigm. Springer, 2016.

    Google Scholar 

  • 11. Owens D. and Hätönen J. Iterative learning controlan optimization paradigm. Annual reviews in control, 29(1):57–70, 2005.

    Google Scholar 

  • 12. Rafajłowicz E., Pawlak-Kruczek H., and Rafajłowicz W. Statistical classifier with ordered decisions as an image based controller with application to gas burners. In International Conference on Artificial Intelligence and Soft Computing, pages 586–597. Springer, 2014.

    Google Scholar 

  • 13. Rafajłowicz E. and Rafajłowicz W. Image driven decision making with application to control gas burners. In IFIP International Conference on Computer Information Systems and Industrial Management – submitted.

    Google Scholar 

  • 14. Rafajłowicz E. and Rafajłowicz W. Iterative learning in repetitive optimal control of linear dynamic processes. In International Conference on Artificial Intelligence and Soft Computing, pages 705–717. Springer, 2016.

    Google Scholar 

  • 15. Rigelsford J. Industrial image processing: Visual quality control in manufacturing. Sensor Review, 21(2), 2001.

    Google Scholar 

  • 16. Rogers E., Galkowski K., and Owens D. Control systems theory and applications for linear repetitive processes, volume 349. Springer Science & Business Media, 2007.

    Google Scholar 

  • 17. Tang L. and Landers R. Melt pool temperature control for laser metal deposition processespart i: Online temperature control. Journal of manufacturing science and engineering, 132(1):011010, 2010.

    Google Scholar 

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Acknowledgements

The research of the 1-st author has been supported by the National Science Center under grant: 2012/07/B/ST7/01216.

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Correspondence to Ewaryst Rafajłowicz .

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Rafajłowicz, E., Rafajłowicz, W. (2017). Camera in the control loop – methods and selected industrial applications. In: Mitkowski, W., Kacprzyk, J., Oprzędkiewicz, K., Skruch, P. (eds) Trends in Advanced Intelligent Control, Optimization and Automation. KKA 2017. Advances in Intelligent Systems and Computing, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-60699-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-60699-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60698-9

  • Online ISBN: 978-3-319-60699-6

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