Abstract
It is difficult for numerical method to forecast and control the anode shape in Electrochemical Machining (ECM) with an uneven interelectrode gap, so this paper introduces Artificial Neural Network (NN) to solve this problem. The experiments with different cathode shapes and minimal interelectrode gaps are carried out and the corresponding anode shapes are obtained. Those cathode and anode shapes are discretized and taken as the input samples of a B-P network. Quasi-Newton algorithm is used to train this network. To verify the validity of the trained network, results obtained by NN are compared with that obtained by the experiments, and the results show that the former is close to the later, which indicates it is feasible to apply NN to solve this problem.
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© 2004 Springer-Verlag Berlin Heidelberg
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Pang, G., Xu, W., Zhai, X., Zhou, J. (2004). Forecast and Control of Anode Shape in Electrochemical Machining Using Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_41
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DOI: https://doi.org/10.1007/978-3-540-28648-6_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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