Advertisement

Adaptive Machining Using Function Blocks

  • Lihui WangEmail author
  • Xi Vincent Wang
Chapter

Abstract

In a Cyber-Physical System (CPS), sensors or other communicating tools embedded in physical entities are responsible for real-time data acquisitions. Operation decisions are adaptively made according to the physical inputs, and are transferred to the physical entities in order to optimise the performance of the system. Within a CPS, function blocks are applied at control level. Function blocks, as data and function carriers, are embedded in machining processes by combining machining features (MFs), which represent machining information, e.g. machining sequence, machining parameters, and other relevant machining resources. MFs are enriched to carry much more machining information and knowledge. A reachability-based MF sequencing method then generates MF sequence adaptively to minimise the cutting tool change times. Moreover, 3-axis based setups can be merged and dispatched adaptively to the selected machine tool.

References

  1. 1.
    L. Wang, A. Haghighi, Combined strength of holons, agents and function blocks in cyber-physical systems. J. Manuf. Syst. 40(Part 2), 25–34 (2016)CrossRefGoogle Scholar
  2. 2.
    J. Shi, W. Arndt, F. Hu, J. Tang, Isolating—a new resampling method for gene order data, in 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (2011), pp. 135–140Google Scholar
  3. 3.
    P. Derler, E.A. Lee, A.S. Vincentelli, Modeling cyber–physical systems. Proc. IEEE 100(1), 13–28 (2012)CrossRefGoogle Scholar
  4. 4.
    J. Lee, B. Bagheri, H.A. Kao, A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  5. 5.
    L. Wang, M. Törngren, M. Onori, Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37(Part 2), 517–527 (2015)CrossRefGoogle Scholar
  6. 6.
    E.A. Lee, Cyber-physical systems—are computing foundations adequate? October, 1(Jan 2006), pp. 1–9Google Scholar
  7. 7.
    S. Horbach, Advances in Sustainable and Competitive Manufacturing Systems (2013)Google Scholar
  8. 8.
    V. Vyatkin, IEC 61499 as enabler of distributed and intelligent automation: state of the art review. IEEE Trans. Industr. Inf. 7(4), 768–781 (2011)CrossRefGoogle Scholar
  9. 9.
    W. Ji, L. Wang, A. Haghighi, M. Givehchi, X. Liu, W. Ji, An enriched machining feature based approach to cutting tool selection. Int. J. Comput. Integr. Manuf. 1–10 (2017)Google Scholar
  10. 10.
    M. Givehchi, A. Haghighi, L.H. Wang, Generic machining process sequencing through a revised enriched machining feature concept. J. Manuf. Syst. 37, 564–575 (2015)CrossRefGoogle Scholar
  11. 11.
    W. Ji, L. Wang, A. Haghighi, M. Givehchi, X. Liu, A reachability based approach for machining feature sequencing. J. Manuf. Syst. 40, 96–104 (2016)CrossRefGoogle Scholar
  12. 12.
    N. Deo, Graph Theory with Applications to Engineering and Computer Science (Prentice-Hall, New Jersey, 1974)zbMATHGoogle Scholar
  13. 13.
    R. Meenakshi Sundaram, Process planning and machining sequence. Comput. Ind. Eng. 11(1–4), 184–188 (1986)CrossRefGoogle Scholar
  14. 14.
    P. Prabhu, S. Elhence, H. Wang, R. Wysk, An operations network generator for computer aided process planning. J. Manuf. Syst. 9(4), 283–291 (1990)CrossRefGoogle Scholar
  15. 15.
    Z. Liu, L. Wang, Sequencing of interacting prismatic machining features for process planning. Comput. Ind. 58(4), 295–303 (2007)CrossRefGoogle Scholar
  16. 16.
    L. Wang, N. Cai, H.-Y. Feng, Z. Liu, Enriched machining feature-based reasoning for generic machining process sequencing. Int. J. Prod. Res. 44(8), 1479–1501 (2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    L. Wang, J. Ma, H.-Y. Feng, An adaptive and optimal setup planning system, in 4th IEEE Conference on Automation Science and Engineering (2008), pp. 67–72Google Scholar
  18. 18.
    L. Wang, H.-Y. Feng, N. Cai, Architecture design for distributed process planning. J. Manuf. Syst. 22(2), 99–115 (2003)Google Scholar
  19. 19.
    L. Wang, N. Cai, H.-Y. Feng, Function blocks enabled dynamic set-up dispatching and execution monitoring. Int. J. Comput. Integr. Manuf. 22(1), 3–12 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Production EngineeringKTH Royal Institute of TechnologyStockholmSweden

Personalised recommendations