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Experimental study on electrical discharge machining of Inconel using RSM and NSGA optimization technique

  • R. Senthil Kumar
  • P. Suresh
Technical Paper
  • 34 Downloads

Abstract

This work presents the optimization while machining of Inconel superalloy as base material and copper as the electrode using ZNC electrical discharge machine with positive polarity. Formation of complex shapes by this material alloy with reasonable speed and surface finish is not an easy task with the traditional machining. Moreover, the edge profile (which is circular in this work) formed by the energy released by the spark does not result with expected profile. After machining, it is observed that the edge of the profile gets disintegrated because of which the material will lose its fatigue strength. Central composite design of response surface methodology (RSM) is used with six blocks for the analysis. For experimentation, peak voltage (Vin), peak current (Iin) and pulse on-time (Ton) are taken as key input parameters based on the literature support. MRR: metal removal rate, SR: surface roughness, TWR: tool wear rate and edge disintegration (overcut OC) have been taken as performance measures. Then, the RSM is employed over the input parameters to get the suitable mathematical model, and the outcome reveals that the suggested model helps to predict the factor values within the limit of investigation. At 95% CI, the results show that the input current value has the greatest influence on the disintegration factor, followed by the input voltage and then spark-on on-time the MRR and OC of measurements. It is observed that almost near optimal is observed at voltage level of 1 mV, current value of 17 mA and on-time of 850 μs. The desirability is 0.489 for MRR, 0.703 for TWR, 0.649 for SR and 0.649 for OC at the optimal setting of the parameters. Finally, non-sorting genetic algorithm is utilized for optimization of multiple responses.

Keywords

Electrical discharge machining Inconel Material removal rate Tool wear rate Surface roughness Response surface methodology Non sorting genetic algorithm 

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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Mechanical EngineeringMuthayammal Engineering CollegeRasipuramIndia

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