Optimization and Grey Relational Analysis of En 31steel Using Parameters of Wire cut electric Discharge Machine

Gummadi Phaneendra Kumar


WIRE EDM has become an important non-traditional machining process as itprovides an effective solution for producing components made of difficult to machinematerials like titanium, zirconium etc. and intricate shapes which are not possible byconventional machining methods. Due to large number of process parameters and responseslots of researchers have attempted to model this process. This paper reviews the researchtrends in WEDM on relation between different process parameters like pulse on time(TON),pulse off time(TOFF), servo voltage(sv) on different process responses like Material RemovalRate, Surface Roughness. In addition, the paper highlights different modelling and optimizationmethods. The final part of the paper includes some recommendations about the trends forfuture wire Edm researches.

The main objectives of this study investigate and evaluate the effect of different inputprocess parameters (pulse on time, pulse off time, servo voltage) on material removal rate,surface roughness as response parameters have been considered for each Experiment.Experimentation was planned as per Taguchi’s L27 Orthogonal array during machining of EN31 work material which is High Carbon High Chromium Die Steel (HCHCR). Brass wireelectrode with 0.25mm Diameter was used as tool in the Experiments.The results are analyzed using analysis of variance (ANOVA) method. Thisparametric analysis (ANOVA) shows the percentage contribution of parametersindividually. Grey relational analysis are applied to determine the suitable selectionof machining parameters for wire cut EDM process. A grey relational grade obtainedfrom the grey relational analysis is used to optimize the process parameters. Byanalyzing the Grey relational grade we find the optimum parameters. Confirmation testhas been conducted to optimize the process parameter. Further mathematical models aredeveloped using Box-Behnken design of experiments of response surface methodology tooptimize the process parameters using state of art optimization techniques for future studies.

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