Abstract
The present work focuses on the impact of expanded graphite (EG) nano-powder along with spark-on-duration (Ton) and spark-off-duration (Toff), and current as factors on increasing material removal rate (MRR), reduction of surface roughness (SR), tool wear rate (TWR), dimensional deviation (DD), and surface defects for Ni55.8Ti. Taguchi’s design having 4 factors at 3 levels was employed to perform the experimental trials. ANOVA has successfully validated the developed regression equations. EDM factors of PC, Toff, current, and Toff were found to be the largest contributing factors with the involvement of 76.91 %, 38.40 %, 34.36 %, and 44.54 % for MRR, TWR, SR, and DD respectively. TLBO algorithm was used in the present work to tackle the conflicting situation and to optimize the response variables. The simultaneous optimization conducted through the Teaching-learning-based optimization technique has yielded optimal parameters setting of Ton at 7 µs, Toff at 5 µs, PC at 1.5 g/L, and current at 10 A by giving optimal response values at MRR of 42.82 mm3/min, TWR of 0.4039 mm3/min, SR of 3.71 µm, and DD of 92.65 µm. Lastly, Scanning electron microscopy was utilized to check EG nano-powder significance on the machined parts' surface morphology.
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