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Modeling and Multi-Response Optimization on WEDM Ti6Al4V

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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.
Title: Modeling and Multi-Response Optimization on WEDM Ti6Al4V
Description:
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.

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