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This paper presents an investigation on the effect and optimization of machining parameters on the kerf (cutting width) and material removal rate (MRR) of titanium alloy (TI-6AL-4V) using wire electrical discharge machining WEDM with a brass wire diameter of 0.5mm. The experimental studies were conducted under varyingpulse-off time, peak current, wire feed and wire tension. The settings of machining parameters were determined by using Taguchi experimental design method. The multiple performance characteristics based on the statisticalbased analysis of variance (ANOVA) and grey relational analysis (GRA) was attempted. Analysis of variance was used to study the significance of process parameters on grey relational grade (GRG) which showed the most significant factor. The GRG obtained from the GRA was used to optimize the WEDM process. The optimum process parameters are determined by the GRG as the overall performance index. To validate the findings, confirmation experiment had been carried out at the optimal set of parameters, and the predicted results were found to be in good agreements with experimental findings. Improved machining performance in the WEDM process has been achieved by using this approach.

Wire electrical discharge machining (WEDM) is a material removal process of electrically conductive materials by the thermo-electric source of energy which is extensively used in machining of materials for a highly precision productivity. This work presents the machining of titanium alloy (TI-6AL-4V) using WEDM with a brass wire diameter of 0.25mm.The objective of this work is to study the influence of three machining parameters, namely peak current (IP), feed rate (FC) and wire tension (WT) to cutting speed and surface roughness. Response Surface Methodology was used to develop second order model in order to predict cutting rate and surface roughness responses. The results showed that the average percentage error between the predicted and experimental value for both models was less than 2%.Furthermore, the developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in good agreement with the predicted values.

Wire electrical discharge machining is a material removal process of electrically conductive materials by the thermo-electric source of energy. This kind of machining extensively used in machining of materials with highly precision productivity. This work presents the machining of titanium alloy (TI-6AL-4V) using wire electro-discharge machining with brass wire diameter 0.5mm.The objective of this work is to study the influence of three machining parameters namely peak current, pulse off time and wire tension to cutting rate, material removal rate, surface roughness and kerf width followed by suggesting the best operating parameters towards good machining characteristics. A full factorial experimental design was used with variation of peak current, feed rate and wire tension, with results evaluated using analysis of variance techniques. Parameter levels were chosen based on best practice and results from preliminary testing. Main effects plots and percentage contribution ratios are included for the main factors and their interactions.

Wire electrical discharge machining (WEDM) is a material removal process of electrically conductive materials by the thermo-electric source of energy which is extensively used in machining of materials for a highly precision productivity. This work presents the machining of titanium alloy (TI-6AL-4V) using WEDM with a brass wire diameter of 0.25mm.The objective of this work is to study the influence of three machining parameters, namely peak current (IP), feed rate (FC) and wire tension (WT) to cutting speed and surface roughness as a responses. Response Surface Methodology was used to develop second order model in order to predict cutting speed and surface roughness responses. The results showed that the average percentage error between the predicted and experimental value for both models was less than 2%. Effects of each parameter and their interaction with percentage contribution ratios (PCR) are included for each response.