The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and...The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and low working gap was investigated by the Grey-Taguchi method.The influences of peak current,pulse on-time,pulse off-time and spark gap on electrode wear（EW）,material removal rate（MRR） and working gap（WG） in the micro milling electrical discharge machining of Inconel 718 were analyzed.The experimental results show that the electrode wear decreases from 5.6×10-9 to 5.2×10-9 mm3/min,the material removal rate increases from 0.47×10-8 to 1.68×10-8 mm3/min,and the working gap decreases from 1.27 to 1.19 μm under optimal micro milling electrical discharge machining process parameters.Hence,it is clearly shown that multiple performance characteristics can be improved by using the Grey-Taguchi method.展开更多
Electrochemical discharge machining is considered to be a hybrid machining process that combines with EDM and ECM （electro chemical machining）, called ECDM. The material removal is based on two phenomena： electroch...Electrochemical discharge machining is considered to be a hybrid machining process that combines with EDM and ECM （electro chemical machining）, called ECDM. The material removal is based on two phenomena： electrochemical dissolution of the material and thermal erosion of electrical discharges that occur between the cathode ＆ anode electrodes. This process is better used for machining of non conducting materials efficiently. In this research paper shows that a brief literature review study of various measuring instruments used for analysis of various parameters of the electrochemical discharge machining process on various types of materials, tool material, input ＆ output parameters such as surface roughness, surface texture, material removal, tool wear etc..展开更多
This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data colle...This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.展开更多
文摘The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and low working gap was investigated by the Grey-Taguchi method.The influences of peak current,pulse on-time,pulse off-time and spark gap on electrode wear（EW）,material removal rate（MRR） and working gap（WG） in the micro milling electrical discharge machining of Inconel 718 were analyzed.The experimental results show that the electrode wear decreases from 5.6×10-9 to 5.2×10-9 mm3/min,the material removal rate increases from 0.47×10-8 to 1.68×10-8 mm3/min,and the working gap decreases from 1.27 to 1.19 μm under optimal micro milling electrical discharge machining process parameters.Hence,it is clearly shown that multiple performance characteristics can be improved by using the Grey-Taguchi method.
文摘Electrochemical discharge machining is considered to be a hybrid machining process that combines with EDM and ECM （electro chemical machining）, called ECDM. The material removal is based on two phenomena： electrochemical dissolution of the material and thermal erosion of electrical discharges that occur between the cathode ＆ anode electrodes. This process is better used for machining of non conducting materials efficiently. In this research paper shows that a brief literature review study of various measuring instruments used for analysis of various parameters of the electrochemical discharge machining process on various types of materials, tool material, input ＆ output parameters such as surface roughness, surface texture, material removal, tool wear etc..
文摘This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.
