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Wire EDM is a complicated machining process that is used for producing complex 2D and 3D shapes. In this work, the process parameters associated with the wire electrical discharge machining (WEDM) of oil hardening non-shrinkage (OHNS) die steel material were investigated through response surface method (RSM) and an artificial neural network (ANN). A quadratic model developed through RSM was used to predict material removal rate (MRR) with appreciable precision. The various input variables, viz. pulse on time (PON), pulse off time (POFF), wire feed rate (WFR) and input current (I), have been considered. A comparison between the predicted and measured values of MRR was performed for each experiment. It was noted that the RSM predicted values are in a 95%confidence interval. Statistical analysis shows the capabilities of the developed models to predict the MRR more accurately. Also, ANN model estimates MRR with high precision compared using the RSM model. Support vector regression (SVR) is also used to analyze the impact of various process parameters. The results show that all approaches are strongly capable of predicting the response. Analysis the WEDM is a very effective. Of the three approaches ANN is superior.
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