Open AccessExperimental investigations and multi criteria optimization during machining of A356/WC MMCs using EDM
Author(s) -
Akash Singh,
Kunisetti V. Praveen Kumar,
Kondru Gnana Sundari,
Rishitosh Ranjan,
B. Surekha
Publication year - 2022
Publication title -
decision science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 18
eISSN - 1929-5804
pISSN - 1929-5812
DOI - 10.5267/j.dsl.2021.12.001
Subject(s) - electrical discharge machining , materials science , machining , tungsten carbide , surface roughness , die casting , metallurgy , metal matrix composite , composite number , mechanical engineering , composite material , casting , engineering
In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.