Abstract
This paper presents the designing steps and simulation results of a pulse classification system for the ECDM process using artificial neural networks (ANN). An Electro Discharge Machining (EDM) machine was modified by incorporating an electrolyte system and by modifying the control system. Gap voltage and working current waveforms were obtained. By observing the waveforms, pulses were classified into five groups. A feed forward neural network was trained to classify pulses. Various neural network architectures were considered by changing the number of neurons in the hidden layer. The trained neural networks were simulated. A quantitative analysis was performed to evaluate various neural network architectures.
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Mediliyegedara, T., De Silva, A., Harrison, D., McGeough, J., Hepburn, D. (2006). Designing Steps and Simulation Results of a Pulse Classification System for the Electro Chemical Discharge Machining (ECDM) Process – An Artificial Neural Network Approach. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_27
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DOI: https://doi.org/10.1007/3-540-31662-0_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
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