Electrical Discharge Machining of Beryllium Copper Alloys Using Graphite Electrode
Electrical Discharge Machining (EDM) is commonly used to produce molds and dies, to drill small, burr-free holes and to make prototype quantities of contacts for the aerospace and electronics markets. Most of EDM machines are manufactured and equipped with built-in ‘machining technology’ for steels....
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| Format: | Thesis |
| Language: | English English |
| Published: |
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/7845/1/ABS_%3D%3D%3D__FK_2009_97.pdf |
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| Summary: | Electrical Discharge Machining (EDM) is commonly used to produce molds and dies, to drill small, burr-free holes and to make prototype quantities of contacts for the aerospace and electronics markets. Most of EDM machines are manufactured and equipped with built-in ‘machining technology’ for steels. Apart from steel, beryllium copper alloys are amongst essential material for mould and die making. Therefore, the present study elucidates the die-sinking EDM characteristics of beryllium copper alloys with graphite as an electrode. Experiments were conducted on EDM Die Sinking Charmilles Robofom 35P. The output responses investigated were Material Removal Rate (MRR) and Surface Roughness (Ra). Full factorial and Linear Regression analysis of Design of Experiment (DOE) module in Minitab was employed as a principal methodology to examine the effects of current, polarity, pulse duration and voltage over output responses. The significant and optimum machining parameters for each output responses was also identified and established. Experiment results indicate that the Material Removal Rate (MRR) was mainly affected by current, pulse duration, voltage and interaction between current*pulse duration. For the Surface Roughness (Ra), the significant factors were current, voltage and pulse duration. Confirmation tests were carried out and used to compare results obtained by theoretical predication with those experimentally. It was found that the error margin of factors influenced between the predicted and actual results is 5% for Material Removal Rate (MRR) and 4.2% for Surface Roughness (Ra). |
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