Designing steps and simulation results of a pulse classification system for the Electro Chemical Discharge Machining (ECDM) process - an artificial neural network approach

T.K.K.R. Mediliyegedara*, A. K.M. De Silva, D. K. Harrison, J. A. McGeough, D. Hepburn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationApplied Soft Computing Technologies: The Challenge of Complexity
EditorsAjith Abraham, Bernard Baets, Mario Koeppen, Bertram Nickolay
PublisherSpringer
Pages343-352
Number of pages10
ISBN (Electronic)9783540316626
ISBN (Print)9783540316497
DOIs
Publication statusPublished - 13 Apr 2006

Publication series

NameAdvances in Soft Computing
Volume34
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Keywords

  • signal
  • algorithm
  • algorithms
  • bioinformatics
  • classification
  • complexity
  • evolution
  • evolutionary computation
  • genetic algorithm
  • kernel
  • layout
  • neural network
  • optimization
  • robot
  • robotics

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications

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