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OCTOBER 19-20, 2012 - YMCA University of Science & Technology

OCTOBER 19-20, 2012 - YMCA University of Science & Technology

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Proceedings <strong>of</strong> the National Conference on<br />

Trends and Advances in Mechanical Engineering,<br />

<strong>YMCA</strong> <strong>University</strong> <strong>of</strong> <strong>Science</strong> & <strong>Technology</strong>, Faridabad, Haryana, Oct <strong>19</strong>-<strong>20</strong>, <strong>20</strong>12<br />

increase in concentration <strong>of</strong> impurities. It was reported that machining becomes unstable at an excessive additive<br />

powder concentration due to the occurrence <strong>of</strong> short-circuits and occurrence <strong>of</strong> discharges at same spot. Since<br />

then a lot <strong>of</strong> research work has been done in the area <strong>of</strong> additive mixed electrical discharge machining and have<br />

shown great effect on process performance in terms <strong>of</strong> material removal rate, surface characteristics, wear ratio<br />

etc.<br />

Jeswani [13] investigated the effect <strong>of</strong> suspended fine graphite powder in dielectric medium <strong>of</strong> electrical<br />

discharge machining on tool steel and reported that addition <strong>of</strong> about 4 g/l <strong>of</strong> fine graphite powder (10 μm in<br />

average size) in kerosene increased metal removal rate (MRR) by 60% and tool wear rate by 15%. This was<br />

attributed to better electrical discharge distribution between spark gap.<br />

Mohri and co worker [14] studied the effect <strong>of</strong> silicon powder in dielectric medium and reported significant<br />

improvement in machining performance. Kumar and Beri studied the effect <strong>of</strong> graphite powder on surface<br />

quality and reported improved surface finish [15]. The aim <strong>of</strong> the present research work was to experimentally<br />

evaluate on surface quality improvement in aluminium powder mixed AEDM <strong>of</strong> Nickel Based Super Alloy 718<br />

with cryogenically treated copper electrode and to find best parametric settings to minimize SR.<br />

2. Experimental Planning and Procedure<br />

Taguchi methodology has been applied to plan and analyze the experiments. The Taguchi method can optimize<br />

performance characteristics through the settings <strong>of</strong> process parameters and reduce the sensitivity <strong>of</strong> the system<br />

performance to sources <strong>of</strong> variation.<br />

Table 1 Process parameters and their levels<br />

Symbol Process parameters units Levels<br />

1 2 3<br />

A Polarity +ve -ve<br />

B Types <strong>of</strong> electrode Copper Cryogenically<br />

treated copper<br />

C Peak current amps 0.5 3 6<br />

D Pulse on time µs 50 100 150<br />

E Duty cycle τ 0.7 0.8 0.9<br />

F Gap voltage volts 40 60 80<br />

G Retract distance mm 1 2 3<br />

H Concentration <strong>of</strong> Powder<br />

(300 Mesh size)<br />

g/l 0 6 12<br />

The experimental parameters and their levels selected for present study are tabulated in Table 1 keeping other<br />

parameters constant based on trial experiments and previous studies. In the present study experiments were<br />

carried out on Electronica make electrical discharge machine; model SMART ZNC (S50).<br />

A copper electrode <strong>of</strong> 8mm φ was subjected to deep cryogenic treatment which consists <strong>of</strong> a slow cool-down rate<br />

(2.5°C/min) from ambient temperature to the temperature <strong>of</strong> liquid nitrogen. When the material reached<br />

approximately at −<strong>19</strong>3.15°C it was soaked for 24 hours.<br />

Experiments were performed with O.A L 36 (2 2 x3 6 ). The results were analyzed for minimum surface roughness<br />

with “smaller the better” quality characteristic with Minitab s<strong>of</strong>tware 15.1.1.The SR was measured in terms <strong>of</strong><br />

arithmetic mean roughness <strong>of</strong> the evaluated roughness pr<strong>of</strong>ile (Ra in µm) by using a Mitutoyo SJ 400 surface<br />

testing analyzer.<br />

3. Results and Discussions<br />

The S/N ratios <strong>of</strong> SR for each trial run have been calculated from experimental data and response table for<br />

smaller the better are summarized in Table 2 giving relative importance <strong>of</strong> each parameter on desired response.<br />

The individual effects <strong>of</strong> input parameters on the S/N ratios for SR are shown in main effect plot (Fig.2).<br />

483

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