Abstract

Wire Electrical Discharge Machining (WEDM) provides an effective solution for machining hard materials with intricate shapes. WEDM is a specialized thermal machining process is capable to accurately machining parts of hard materials with complex shapes. However, selection of process parameters for obtaining higher machining efficiency or accuracy in wire EDM is still not fully solved, even with the most up-to-date CNC WED machine. The study presents the machining of Titanium grade 2 material using L’16 Orthogonal Array (OA). The process parameters considered for the present work are pulse on time, pulse off time, current, bed speed, voltage and flush rate. Among these process parameters voltage and flush rate were kept constant and the other four parameters were varied for the machining. Molybdenum wire of 0.18mm is used as the electrode material. Titanium is used in engine applications such as rotors, compressor blades, hydraulic system components and nacelles. Its application can also be found in critical jet engine rotating and airframes components in aircraft industries. Firstly optimization of the process parameters was done to know the effect of most influencing parameters on machining characteristics viz., Surface Roughness (SR) and Electrode Wear (EW). Then the simpler functional relationship plots were established between the parameters to know the possible information about the SR and EW. This simpler method of analysis does not provide the information on the status of the material and electrode. Hence more sophisticated method of analysis was used viz., Artificial Neural Network (ANN) for the estimation of the experimental values. SR and EW parameters prediction was carried out successfully for 50%, 60% and 70% of the training set for titanium material using ANN. Among the selected percentage data, at 70% training set showed remarkable similarities with the measured value then at 50% and 60%.

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