Multi response optimization of PMEDM of Ti6Al4V using Al2O3 and SiC powder added de-ionized water as dielectric medium using grey relational analysis

Abstract

In the present work, surface characteristics of powder mixed electro-discharge machining (PMEDM) process are investigated using alumina and carborundum abrasive powder added dielectric fluid for titanium alloy Ti6Al4V. Deionized water is utilized as dielectric to accomplish an environmentally safe machining climate, and limit the emanation of harmful substances. Pulse ON/OFF time (TON/TOFF), discharge current (IP), and powder concentration (PC) are selected as process variables to reconnoiter characteristics of performance like surface finish, rate of material removal, and tool wear. The multi-response optimization has been performed using grey relational analysis (GRA) to establish the optimal parametric combination of process variables that gives the finest surface quality and minutest tool wear. The investigation results divulge that discharge current (IP) and powder concentration (PC) have the most significant effect on material removal rate (MRR), tool wear rate (TWR), and surface finish. The surface characteristics were evaluated by scanning electron microscope for the optimal parameters combination. The minutest value of surface roughness and tool wear rate is achieved at IP: 06 amps, TON: 05 µS, TOFF: 96 µS, and PC: 0.50 g/L. The optimized set of parameters would help process engineers to attain improved machining performance of PMEDM, economically along with desired surface characteristics.

Optimization of the PM-EDM Process Parameters for Ti-35Nb-7Zr-5Ta Bio Alloy

“…The process becomes considerably more stable and the surface smoothness is enhanced due to the larger spark gap distance, which also makes uniform debris flushing easier. Consequently, the addition of powders to the dielectric fluid considerably ameliorates surface quality [11]. Moreover, when the discharge gap widens due to the addition of powder, less material-in the form of micro debris-is removed from the workpiece surface and less heat energy is produced.…”

“…Authors in [11] investigated the surface characteristics of the PM-EDM process using Al 2 O 3 and SiC abrasive powder added to the dielectric fluid for the Ti6Al4V alloy. The findings of the experiment show that the variables which most significantly affect MRR, Tool Wear Rate (TWR), and surface finish are discharge current (I P ) and powder concentration (P C ).…”

Impact of SiC Particle Incorporated Dielectric Medium on Machining Performance of AA7050/SiC/Al2O3 Hybrid Composites

“…32 The R a value decreases with increase in Ton until the saddle point of 30 μs thereafter it increases. When the Ton was tuned at 30 μs, generated plasma channel was distributed uniformly over the surface beyond that value plasma densification occurs which hinders the machining performance, 33 hence R a increases.…”

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

“…Because of the complexity of the physical phenomena occurring during electrical discharge machining, a significant part of the research has focused on the development of predictive models for the process. One of the most frequently used methodologies that allow us to determine the relationship between the input factors and the results of process optimization are the response surface methodology [ 17 , 18 , 19 , 20 , 21 ], artificial neural networks [ 22 , 23 , 24 ], desirability functions [ 25 , 26 , 27 , 28 ], the fuzzy possibility approach [ 29 , 30 ], and gray relational analysis [ 31 , 32 , 33 ]. The study provided by Jatakar et al [ 34 ] shows that using the ANN algorithm can effectively diagnose and self-monitor complex manufacturing processes without human intervention.…”