Abstract
Milling electrical discharge machining (EDM) enables the machining of complex cavities using cylindrical or tubular electrodes. To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear. Due to the complexity and random nature of the process, existing methods of compensating for such wear usually involve off-line prediction. This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function (RBF) network. Data gained from an orthogonal experiment were used to provide training samples for the RBF network. The model established was used to forecast the electrode wear, making it possible to calculate the real-time tool wear in the milling EDM process and, to lay the foundations for dynamic compensation of the electrode wear on-line. This paper demonstrates that by using this model prediction errors can be controlled within 8%.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Yu Z Y, Masuzawa T, Fujino M. Micro-EDM for three-dimensional cavities-development of uniform wear method [J]. CIRP Annals-Manufacturing Technology, 1998, 47(1): 169–172.
Zhao W S. Advance electrical discharge machining technology, electrical discharge milling machining [M]. Beijing: National Defense Industry Press, 2003 (in Chinese).
Dauw D. On the derivation and application of a real-time wear sensor in EDM [J]. CIRP Annals—Manufacturing Technology, 1986, 35(1): 111–116.
Bleys P, Kruth J P, Lauwers B. Sensing and compensation of tool wear in milling EDM [J]. Journal of Materials Processing Technology, 2004, 149: 139–146.
Tricarico C, Forel B, Orhant E. Measuring device and method for determining the length of an electrode [P]. US: 6072143. 2000-06-06.
Mizugaki Y. Contouring electrical discharge machining with on-machine measuring and dressing of a cylindrical graphite [C]// Proceedings of the 1996 IEEE IECON 22nd International Conference V3. Taipei: IEEE Press, 1996: 1514–1517.
Feng T, Xiao-mei X U, Tso S K, et al. Application of evolutionary neural network in impact acoustics based nondestructive inspection of tile-wall [C]// Proc International Conference on Communications, Circuits and Systems. Hong Kong: IEEE Press, 2005: 974–978.
Poggio T, Girosi F. Networks for approximation and learning [J]. Proc IEEE, 1990, 78(10): 1481–1497.
Park J, Sandberg I W. Approximation and radial basis function networks neural computation [J]. Neural Computation, 1993, 5(2): 305–316.
Sanner R M, Slotine J J E. Gaussian networks for direct adaptive control [J]. IEEE Transactions on Neural Networks, 1992, 3(6): 837–863.
Chen S, Billings S A, Cowan C F N, et al. Practical identification of narmax models using radial basis functions [J]. International Journal of Control, 1990, 52(6): 1327–1350.
Chen S, Cowan C F N, Grant P M. Orthogonal least squares learning algorithm for radial basis function networks [J]. IEEE Transactions on Neural Networks, 1991, 2(2): 302–309.
Chen X G. Artificial neural network technology and application [M]. Beijing: China Electric Power Press, 2003 (in Chinese).
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: the National High Technology Research and Development Program (863) of China (No. 2007AA04Z345), the National Natural Science Foundation of China (No. 50679041) and the Foundation of Heilongjiang Science and Technology Committee (No. GA06A501)
Rights and permissions
About this article
Cite this article
Huang, H., Bai, Jc., Lu, Zs. et al. Electrode wear prediction in milling electrical discharge machining based on radial basis function neural network. J. Shanghai Jiaotong Univ. (Sci.) 14, 736–741 (2009). https://doi.org/10.1007/s12204-009-0736-5
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-009-0736-5
Key words
- milling electrical discharge machining (EDM)
- electrode wear prediction
- radial basis function (RBF) neural network