Skip to main content

Bio-Inspired Manufacturing System Model

  • Chapter
  • First Online:
Adaptive Control of Bio-Inspired Manufacturing Systems

Part of the book series: Research on Intelligent Manufacturing ((REINMA))

Abstract

Nowadays manufacturing enterprises are forced to have manufacturing systems that can support the agile response to emergence and changing conditions. In a biological body, the neuroendocrine-immune system plays very important roles to control and modulate the adaptive behaviours using mutual regulation principles. Inspired by the regulation principles of the biological body, a novel concept of Bio-Inspired Manufacturing System (BIMS) is proposed which can agilely deal with the frequent occurrence of unexpected disturbances at the shop floor level. The control model of BIMS is described from the cybernetics point of view.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

References

  1. Ueda, K., Vaario, J., & Ohkura, K. (1997). Modeling of biological manufacturing systems for dynamic reconfiguration. Annals of the CIRP, 46, 343–346.

    Article  Google Scholar 

  2. Wiendahl, H. P., & Scholtissek, P. (1994). Management and control of complexity in manufacturing. Annals of the CIRP, 43, 533–540.

    Article  Google Scholar 

  3. Leitao, P. (2008). A bio-inspired solution for manufacturing control systems. In A. Azevedo (Ed.), Innovation in manufacturing (pp. 303–314) Boston: Springer.

    Google Scholar 

  4. Shen, W., & Norrie, D. H. (1999). Agent-based systems for intelligent manufacturing: A state-of-the-art survey. Knowledge and Information Systems, 1(2), 129–156.

    Article  Google Scholar 

  5. Brennan, R. W., Fletcher, M., & Norrie, D. H. (2002). An agent-based approach to reconfiguration of real-time distributed control systems. IEEE Transactions on Robotics and Automation, 18(4), 444–451.

    Article  Google Scholar 

  6. Wang, D. S., Nagalingam, S. V., & Lin, G. C. I. (2007). Development of an agent-based Virtual CIM architecture for small to medium manufacturers. Robotics and Computer Integrated Manufacturing, 23(1), 1–16.

    Article  Google Scholar 

  7. Ryu, K., & Jung, M. (2003). Agent-based fractal architecture and modeling for developing distributed manufacturing systems. International Journal of Production Research, 41(17), 4233–4255.

    Article  Google Scholar 

  8. Ryu, K., & Jung, M. (2003). Modeling and specifications of dynamic agents in fractal manufacturing systems. Computers in Industry, 52(2), 161–182.

    Article  Google Scholar 

  9. Brussel, H. Van, Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37(3), 255–274.

    Article  Google Scholar 

  10. Leitao, P., & Restivo, F. (2006). ADACOR: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry, 57, 121–130.

    Article  Google Scholar 

  11. Colombo, A. W., Schoop, R., & Neubert, R. (2006). An agent-based intelligent control platform for industrial holonic manufacturing systems. IEEE Transactions on Industrial Electronics, 53(1), 322–337.

    Article  Google Scholar 

  12. Nahm, Y.-E., & Ishikawa, H. (2005). A hybrid multi-agent system architecture for enterprise integration using computer networks. Robotics and Computer-Integrated Manufacturing, 21, 217–234.

    Article  Google Scholar 

  13. Xiang, W., & Lee, H. P. (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 21, 73–85.

    Article  Google Scholar 

  14. Warnecke, H. J. (1993). The fractal company: A revolution in corporate culture. Berlin: Springer.

    Book  Google Scholar 

  15. Deen, S. M. (2003). Agent-based manufacturing: Advances in the holonic approach. Berlin: Springer.

    Book  Google Scholar 

  16. Okino, N. (1994). Bionic manufacturing system. Journal of Manufacturing Systems, 23(1), 175–187.

    Google Scholar 

  17. Wang, L., Tang, D. B., Gu, W. B., et al. (2012). Pheromone-based coordination for manufacturing system control. Journal of Intelligent Manufacturing, 23(3), 747–757.

    Article  Google Scholar 

  18. Farhy, L. S. (2004). Modeling of oscillations of endocrine networks with feedback. Methods Enzymology, 384, 54–81.

    Article  Google Scholar 

  19. Keenan, D. M., Licinio, J., & Veldhuis, J. D. (2001). A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitaryadrenal axis. PNAS, 98(7), 4028–4033.

    Article  Google Scholar 

  20. SureshKumar, N., & Sridharan, R. (2009). Simulation modeling and analysis of part and tool flow control decisions in a flexible manufacturing system. Robotics and Computer Integrated Manufacturing, 25, 829–838.

    Article  Google Scholar 

  21. Tharumarajah, A., Wells, A. J., & Nemes, L. (1996). Comparison of the bionic, fractal and holonic manufacturing system concepts. International Journal of Computer Integrated Manufacturing, 9(3), 217–226.

    Article  Google Scholar 

  22. Tang, D., Gu, W., et al. (2011). A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 49(5), 1255–1268.

    Article  Google Scholar 

  23. Tu, X. Y., Wang, Z., & Guo, Y. W. (2005). Large systems cybernetics. Beijing: Press of Beijing University of Posts and Telecommunications.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dunbing Tang .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tang, D., Zheng, K., Gu, W. (2020). Bio-Inspired Manufacturing System Model. In: Adaptive Control of Bio-Inspired Manufacturing Systems. Research on Intelligent Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-15-3445-4_1

Download citation

Publish with us

Policies and ethics