Process Optimization and Estimation of Oxygen Assisted Wire Electrical Discharge Machining Performance Using Artificial Neural Network

Oxygen assisted WEDM is a thermo- electrical process in which material is removed by a series of sparks between work piece and wire electrode (tool) of material AISI202. The part and wire are immersed in a dielectric (electrically non-conducting) fluid, usually oxygen which also acts as a coolant and flushes the debris away. The material which is to be cut must be electrically conductive. By observing present research based on literature it is identify the gap among them and finely decided that optimization of process parameter of OXYGEN ASSISTED wirecut EDM OF AISI202 using artificial neural network. Wire Electrical Discharge Machining (WEDM) is used where parts are accurately machined with varying hardness or complex shapes, which have sharp edges that are difficulties observed in conventional machining process. This study outlines the development of model and its application to optimize WEDM machining parameters using the Taguchi’s technique which is based on the robust design. Experimentation was performed as per Taguchi’s L’16 orthogonal array. Each experiment has been performed under different cutting conditions of pressure, pulse-on, pulse-off and peak current,. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Three responses namely material removal rate, surface roughness have been considered for each experiment. Based on this analysis, process parameters are optimized. ANOVA is performed to determine the relative magnitude of the each factor on the objectivefunction. Estimation and comparison of responses was done using artificial neural network.