Parametric optimization and prediction of MRR and surface roughness of titanium mixed EDM for Inconel 718 using RSM and fuzzy logic

Abstract

Powder mixed electric discharge machining (PMEDM) was developed to improve the machining rate and surface quality of conventional EDM. In this study, PMEDM was performed on Inconel 718 by adding titanium particles to the dielectric fluid. The input parameters selected for the experiment were powder concentration, pulse current, gap voltage, pulse on time and pulse off time and the effects of the input parameters were investigated on the material removal rate (MRR) and the surface roughness of the sample. Response surface methodology (RSM) was used to construct the design of experiment. Analysis of variance (ANOVA) was carried out to find out the significant input parameters for each response. Further, Mamdani based fuzzy logic was developed using the experimental data and used to predict the responses in optimized condition. The multi-objective optimization was performed using the RSM desirability function approach to maximize the MRR and minimize the surface roughness. From ANOVA, it is observed that powder concentration, pulse current, gap voltage and pulse on time are the significant input parameters for MRR and surface roughness. The fuzzy result is compared with the experimental data and found the accuracy of prediction is 89.21 % for MRR and 91.23 % for surface roughness. The optimized setting of input parameters is obtained as powder concentration = 8 g/l, pulse current = 9.5 A, gap voltage = 60 V, pulse on time = 150 µs and pulse off time = 20 µs and the corresponding optimum value of MRR and surface roughness are 16.623 mm 3 /min and 3.71 µm respectively. The SEM result indicates that the width of the surface crack is found more in the optimized sample compared to the sample having the least surface roughness. From the EDX analysis, it is found that a very small amount of copper and titanium particles were included on the surface during the machining.