Research on Intelligent Controller for EDM Online Parameters

Ji Ming Chen; Yong Hong Liu; Rong Shen; Shi Peng Wu

doi:10.4028/www.scientific.net/KEM.407-408.117

Paper Titles

The Technical Study on Cylinder Cam Machining
p.99

Development of Novel XY Micropositioning Stage
p.103

Optimization of Distribution Model for CNC Lathes Early Failures
p.107

Grey System Theory Modeling for Nonlinear, Dynamic Machine Tool Thermal Error
p.112

Research on Intelligent Controller for EDM Online Parameters
p.117

Laser-Assisted Truing for Metal-Bonded Diamond Wheel
p.122

Research on Mechanics and Control Methods of a New Loading Structure Based on Stewart Platform
p.126

Principle and Experiment of PRS-XY Hybrid NC Machine Tool
p.131

Dynamic Characteristic Analysis and Validation of Spindle Assembly of High Speed Machine Tool
p.135

HomeKey Engineering MaterialsKey Engineering Materials Vols. 407-408Research on Intelligent Controller for EDM Online...

Research on Intelligent Controller for EDM Online Parameters

Abstract:

EDM is a complex time-varying system, and its online parameters have a great influence on processing stability. In order to overcome the difficulty of EDM online parameters control, this paper designed a kind of intelligent controller, based on fuzzy control and ANN technology, to implement real-time control of EDM online parameters. The simulation results show that this controller can improve machining efficiency and enhance processing stability.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 407-408)

Pages:

117-121

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.407-408.117

Citation:

Cite this paper

Online since:

February 2009

Authors:

Ji Ming Chen, Yong Hong Liu, Rong Shen, Shi Peng Wu

Keywords:

Artificial Neural Network (ANN), Electric Discharge Machining (EDM), Fuzzy Control, Intelligent Controller, Online Parameters

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] S. M. Son, H. S. Lim, A. S. Kumar, M. Rahman: Journal of Materials Processing Technology, Vol. 190(2007), pp.73-76.

Google Scholar

[2] S. C. Di, D. B. Wei, G. X. Chi, Z. L. Wang: Key Engineering Materials, Vol. 315-316(2006), pp.516-520.

Google Scholar

[3] M. T. Yan, C. C. Chi: Journal of Materials Processing Technology, Vol. 205(2008), pp.128-137.

Google Scholar

[4] H. L. Gelgele, Y. Wang, Q.F. Yuan, M.L. Fang: International Journal of Machine Tools and Manufacture, Vol. 43(2003), pp.995-999.

DOI: 10.1016/s0890-6955(03)00102-0

Google Scholar

[5] O. Yilmaz, O. Eyercioglu, N. Z. Gindy: Journal of Materials Processing Technology, Vol. 172(2006), No. 3, pp.363-371.

Google Scholar

[6] W. G. Zhang, X. Z. Yang: Fuzzy Control Theory and Application (Northwest Industrial University press, Xian 2004).

Google Scholar

[7] Y. Zhang, X. L. Wang: Automation and Mechatronics, Vol. 2(2004), pp.1147-1151.

Google Scholar

[8] J. Valentincic, D. Kuser, S. Smrkolj, O. Blatnik, M. Junkar: International Journal of Materials and Product Technology, Vol. 29(2007), pp.344-357.

DOI: 10.1504/ijmpt.2007.013128

Google Scholar

[9] F. Think: Neural Network Theory and Realization in MATLAB 7 (Electronic Industry Press, Beijing 2005).

Google Scholar