Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
Electrical discharge machining (EDM) is one of the non-traditional machining processes normally used in manufacturing very hard materials that are electrically conductive. Tool electrodes form one of the main components of the machining system. The major properties that determine the suitability...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American-Eurasian Network for Scientific Information (AENSI Publisher)
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/1/EDM%201%282012%29.pdf |
| Summary: | Electrical discharge machining (EDM) is one of the non-traditional machining processes
normally used in manufacturing very hard materials that are electrically conductive. Tool electrodes
form one of the main components of the machining system. The major properties that determine the
suitability of such electrodes are electrical conductivity, thermal conductivity and density. The
objective of this paper is to present the use of Artificial Neural Network (ANN) architecture in
modeling these properties. In the research, Cu-TaC electrode compacts were produced at two levels
each of the composition and the compacting pressures from copper and tantalum carbide powders for
use in EDM. The compositions of the Cu-TaC are made of 30 % and 55 % wt of TaC, while the
compacting pressures are 1, 500 psi and 3,000 psi. They were subjected to sintering at temperatures of
450°C and 850 °C. The properties were measured before and after sintering. Results showed that the
sintered electrodes are not suitable for EDM because they lost their electrical conductivity. The presintered
electrodes (green compacts) were however found to suitable for EDM. Artificial neural
network technique with 16 experimental runs was used to develop the new models for predicting the
electrical conductivity, thermal conductivity and density of the green compacted electrodes. The
models were been built by using MATLAB 2009b. Results show that ANN models are capable of
predicting the electrode properties with high degree of prediction accuracy compared to the
experimental results |
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