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… CONTINUE READING
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References
SHOWING 1-10 OF 27 REFERENCES
Finite element prediction of material removal rate due to traveling wire electrochemical spark machining
- Engineering
- 2009
- 25
Mechanism of material removal in electrochemical discharge machining: a theoretical model and experimental verification
- Materials Science
- 1997
- 113
- Highly Influential
Machining piezoelectric (PZT) ceramics using an electrochemical spark machining (ECSM) process
- Materials Science
- 1996
- 72
Study of electrochemical discharge machining technology for slicing non-conductive brittle materials
- Materials Science
- 2004
- 90
Mechanism of spark generation during electrochemical discharge machining: a theoretical model and experimental verification
- Engineering
- 1996
- 137
Optimum selection of machining conditions in abrasive flow machining using neural network
- Engineering
- 2000
- 98
Machining of non-conducting materials using electrochemical discharge phenomenon – An overview
- Materials Science
- 2005
- 258