Advertisement

Journal of Intelligent Manufacturing

, Volume 19, Issue 4, pp 375–382 | Cite as

A fuzzy PID thermal control system for casting dies

  • Tiebao Yang
  • Xiang Chen
  • Henry Hu
  • Yeou-Li Chu
  • Patrick Cheng
Article

Abstract

In high-pressure die casting processes, proper control of die temperature is essential for producing superior quality components and yielding high production rates. However, die temperature distribution depends on various die design and process variables for which accurate models are normally very difficult to obtain. In this paper, a new intelligent control scheme is proposed for die thermal management. In this scheme, extra cooling waterlines controlled by a pump and solenoid valves are attached to the established cooling channels. A fuzzy PID controller is designed to minimize the temperature differences between channels. The experimental results obtained from a laboratory die casting process simulator indicate that the developed control system is capable of adjusting the desirable supply of cooling water into multiple cooling lines. Hence, the local temperature distribution of the die insert may become more homogeneous.

Keywords

Die casting Fuzzy PID control Data acquisition Intelligent system Thermal management 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bishenden, W., & Bhoia, R. (1999). Die temperature control. In Proceedings of the Transactions of 20th International Die Casting Congress (pp. 161–164).Google Scholar
  2. Booth, S. E. (1970). A die temperature cycle time controller. In Proceedings of the Sixth SDCE International Die Casting Congress (p. 54).Google Scholar
  3. Chen, F. (2002). Data acquisition system for die casting processes. M.A.Sc. Thesis, University of Windsor.Google Scholar
  4. Dent, B. K., & Fifer, R. (1972). Production operation with the Llzro-Battelle die temperature controller. In Proceedings of the Seventh SDCE International Die Casting Congress (p. 5572).Google Scholar
  5. Guzelkaya M., Eksin I. and Yesil E. (2003). Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer. Engineering Applications of Artificial Intelligence 16: 227–236 CrossRefGoogle Scholar
  6. He S.Z., Shaoua T.D. and Xu F.L. (1993). Fuzzy self-tuning of PID. Fuzzy Sets and Systems 56: 37–46 CrossRefGoogle Scholar
  7. Hu, H., Chen, F., Chen, X., Chu, Y., & Cheng, P. (2002). Development of a computer-based data acquisition and control system for die casting Processes. In Proceedings of the EPD Congress 2003 (pp. 439–451). TMS.Google Scholar
  8. Hu H., Chen F., Chen X., Chu Y. and Cheng P. (2004). Effect of cooling water flow rates on local temperatures and heat transfer of casting dies. Journal of Materials Processing Technology 148: 57–67 CrossRefGoogle Scholar
  9. Huang, T. T., Chung, H. Y., & Lin, J. J. (1999). A fuzzy PID controller being like parameter varying PID. In IEEE International Fuzzy Systems Conference Proceeding (pp. 269–275).Google Scholar
  10. Larkin, R. J. (1970). Automatic control die temperature in zinc die casting. In Proceedings of the Sixth SDCE International Die Casting Congress (p. 62).Google Scholar
  11. Lin C.T. and Lee C.S.G. (1996). Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Prentice Hall, Upper Saddle River, NJ Google Scholar
  12. Peterson, A. P. (1975). Thermocycling control of aluminum die casting machines. In Proceedings of the Eighth SDCE International Die Casting Congress (pp. B-t75–Bt024).Google Scholar
  13. Qiao W.Z. and Mizumoto M. (1996). PID type fuzzy controller and parameters adaptive method. Fuzzy Sets and Systems 78: 23–35 CrossRefGoogle Scholar
  14. Reddy, R. (1975). Temperature control of die casting dies. In Proceedings of the Eighth SDCE International Die Casting Congress (pp. B-t75–B-t025).Google Scholar
  15. Takagi, T., & Sugeno, M. (1983). Derivation of fuzzy control rules from human operator’s control actions. In Proceeding of IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis (pp. 55–60).Google Scholar
  16. Tsoukalas L.H. and Uhrig R.E. (1996). Fuzzy and neural approaches in engineering. Jonh Wiley & Sons, New York, NY Google Scholar
  17. Yang, T., Hu, H., Chen, X., Chu, Y., & Cheng, P. (2005a). On-line thermal management system for die casting processes. In Proceedings of 5th International Workshop on Advanced Manufacturing Technologies (pp. 189–196). National Research Council Canada.Google Scholar
  18. Yang, T., Hu, H., Chen, X., Chu, Y., & Cheng, P. (2005b). An intelligent control system for die thermal management. In Proceedings of Materials Science & Technology, Sub-symposium on Automation & Control (pp. 11–20).Google Scholar
  19. Yang T., Hu H., Chen X., Chu Y. and Cheng P. (2007). Thermal analysis of casting dies with local temperature controller. International Journal of Advanced Manufacturing Technology 33: 277–284 CrossRefGoogle Scholar
  20. Xu J.X., Hang C.C. and Liu C. (2000). Parallel structure and tuning of a fuzzy PID controller. Automatica 36: 673–684 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Tiebao Yang
    • 1
  • Xiang Chen
    • 1
  • Henry Hu
    • 1
  • Yeou-Li Chu
    • 2
  • Patrick Cheng
    • 2
  1. 1.Department of Mechanical, Automotive & Materials EngineeringUniversity of WindsorWindsorCanada
  2. 2.Department of Research and DevelopmentRyobi Die Casting (USA) Inc.ShelbyvilleUSA

Personalised recommendations