• 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}
}
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… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 27 REFERENCES
Finite element prediction of material removal rate due to traveling wire electrochemical spark machining
Manufacturing engineers are facing new challenges during machining of electrically nonconducting or partially conducting materials such as glass, quartz, ceramics, and composites. Traveling wire
On the analysis of the electrochemical spark machining process
Spark Assisted Chemical Engraving in the light of electrochemistry
Physical principles and miniaturization of spark assisted chemical engraving (SACE)
Spark assisted chemical engraving (SACE) is an unconventional micromachining technology based on electrochemical discharge phenomena for glass and various ceramics. The limits of SACE with respect to
...
...