Research on Production Control Mode of Manufacturing System Based on Neuroendocrine Hormonal Regulation

Article Preview

Abstract:

With the developing control and configuration pattern of modern manufacturing system, the complexity of manufacturing system is enlarged and the operating environment is filled with uncertainty. Under the influence of many uncertain factors, reducing delivery time, due-date performance and low inventory become the important goals of production enterprise. This paper presents a bio-inspired manufacturing system control model, combining the knowledge of manufacturing system, biological control and control system. The relationship between response time and production rate of manufacturing system with different capacity is analyzed, production capacity controller is designed using PI and PD, and the control strategy differences of production rate deviation and overshoot is also discussed. Finally, an optimized design for the parameters of a two-layer work-in-process (WIP) inventory controller is analyzed, with the reference to the neuroendocrine regulatory mechanism. The simulation analysis shows that in bio-inspired manufacturing system made of organic manufacturing cells, the shorter the response time of production capacity is, the faster its production rate responses. With the combination of two-layer WIP inventory controller and traditional PID production capacity controller, the production system is more responsive and the production offset and overshoot are small when the unexpected external demand changes of manufacturing system occur.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

201-208

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] B.P. Zhang: manufacturing informatics( Tsinghua University Press, Beijing 2003).

Google Scholar

[2] Friedrich: Cramer of Chaos and order: complex structures of biological systems (Shanghai Science and Technology Education Press, Shanghai 2000).

Google Scholar

[3] X.Y. Tu: Biological Cybernetics ( Beijing: Beijing University of Posts and Telecommunications Press, Beijing 2006).

Google Scholar

[4] H.B. Xian: Biological Cybernetics basis (Beijing Institute of Technology Press, 1991).

Google Scholar

[5] Yongsheng DING: Based on intelligent control and optimization of biological networks (Science Press, 2010).

Google Scholar

[6] Leon S. Farhy. Modeling of oscillations of endocrine networks with feedback, 2003, 3(14): 4-8.

Google Scholar

[7] D.B. Tang, W.B. Gu: A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 2011, 49(5): 1260-1263.

Google Scholar

[8] Ahmed M. Deif and Waguih H. EIMaraghy: A control approach to explore the dynamics of capacity scalability in reconfigurable manufacturing system. Journal of Manufacturing Systems, 2006, 26(1): 14-18.

DOI: 10.1016/s0278-6125(07)00003-9

Google Scholar

[9] Sterman, J.D.: Business Dynamics: Systems Thinking and Modeling for a Complex World. (New York: McGraw-Hill. 2000).

Google Scholar

[10] John, S. Towill, D. R: Dynamics analysis of a WIP compensated support system. International Journal of Mfg. System design, 1994, 20(1): 283-289.

Google Scholar