Corpus ID: 212560504

Artificial Neural Network Modeling For Material Removal Rate In Traveling Wire Electro-Chemical Spark Machining Process

@inproceedings{Charan2017ArtificialNN,
  title={Artificial Neural Network Modeling For Material Removal Rate In Traveling Wire Electro-Chemical Spark Machining Process},
  author={P M Charan},
  year={2017}
}
  • P M Charan
  • Published 2017
  • Manufacturing Engineers are facing new challenges during machining of electrically nonconducting or partially conducting materials such as glass, quartz, ceramics and composites. Traveling Wire Electrochemical Spark Machining (TW-ECSM), a largely unknown technology, has been applied successfully for cutting, these types of electrically non-conducting or partially conducting materials. However, very few theoretical works have been reported related to machining performance of TW-ECSM process… CONTINUE READING

    Figures and Tables from this paper

    References

    SHOWING 1-10 OF 27 REFERENCES
    On the analysis of the electrochemical spark machining process
    • 145
    Mechanism of material removal in electrochemical discharge machining: a theoretical model and experimental verification
    • 113
    • Highly Influential
    Machining piezoelectric (PZT) ceramics using an electrochemical spark machining (ECSM) process
    • 72
    Machining of non-conducting materials using electrochemical discharge phenomenon – An overview
    • 258
    Spark Assisted Chemical Engraving in the light of electrochemistry
    • 102