OPTIMIZATION OF EDM PROCESS PARAMETERS ON ALUMINIUM ALLOY 6061/5% SiC COMPOSITE

International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 2, FEBRUARY 2016

All Rights Reserved © 2016 IJORAT 1

OPTIMIZATION OF EDM PROCESS

PARAMETERS ON ALUMINIUM ALLOY

6061/5% SiC COMPOSITE R.Santhana Lakshmi

1, J.Lakshmipathy

2, S.David Blessley

3

Assistant Professor, Mechanical Engineering, Scad College of Engineering and Technology, Cheranmahadevi, India1.

Assistant Professor, Mechanical Engineering, FX Engineering College, Tirunelveli, India2.

Assistant Professor, Mechanical Engineering, S.Veerasamy Chettiar College of Engineering and Technology, Puliangudi,

India3.

Abstract: Electric discharge machining (EDM) is one of the non-traditional processes used for machining metal

matrix composites. Metal matrix composites are used in aero scope, automobile industries and also in medical field.

In this work, the metal matrix composite, AA 6061 reinforced with 5wt% SiC particles were prepared by stir

casting method. Determination of optimal process parameter is quite difficult in EDM machining process if

maximum Material Removal Rate (MRR) and good Surface Roughness (SR) are concerned. Experiments were

carried out using Design of Experiment (DOE) techniques. Optimization techniques were proposed to optimize the

EDM process parameter. The gap Voltage, pulse Current, pulse on time and pulse off time Concentration are

considered as decision variables whereas MRR and SR are the machining parameters used in the proposed work.

Keywords: 6061AA/5%composites, EDM, Metal Removal Rate, Surface roughness.

I. INTRODUCTION

Metal matrix composites (MMC) are gaining

increasing attention for applications in aerospace,

defense, and automobile industries. These materials have

been used in automobile brake rotors and various

components in internal combustion engines. The

limitation of MMC is that the machining of these

composites is very difficult due to the highly abrasive

nature of ceramic reinforcements. Non-conventional

machining techniques such as electro-discharge

machining (EDM), electron beam machining (EBM) and

electrochemical machining (ECM) have also been

utilized for machining. Electro discharge machining

(EDM) is a non-traditional process used mainly to cut

hard or difficult metals, where the application of a

traditional process is not convenient. Optimization

techniques are required to identify the optimal

combination of parameters for maximizing the metal

removal rate (MRR) and minimizing the surface

roughness in EDM process. Quite a few researches have

tried to optimize the machining performance.

S.Gopala Kannan et al.[1] The metal used in this study

(AA 7075 reinforced with 10% wt of B4C particles) was

prepared by stainless casting method. The influence of

the process parameters such as pulse current, gap

voltage, pulse on time and plus off time on material

removal rate and surface roughness are investigated. The

objective was to identify the significant process

parameters that affect the output characteristics and to

develop for MRR and SR. T.M.Chenthil Jegan et al.[2] It

describes the selection of machining parameters,

discharge current, pulse on time and pulse off time in

EDM for the machining of AISI202 stainless steel

material. Grey relational analysis is used for optimizing

the machining parameters MRR and surface roughness.

It conforms discharge current as the main parameter

affecting in the MRR.

II. FABRICATION OF 6061AA/5%SiC

COMPOSITE

The fabrication of AA 6061 with 5wt% SiC was

carried out by stir casting technique. A measured amount

of silicon carbide particles were preheated at around

800oC for 2 hrs to make their surfaces oxidized. A

measured amount of 6061 Aluminium alloy ingots were

taken in a graphite crucible and melted in an electrical

furnace. Pre-heated silicon carbide particles were added

to the melt.



After that, the melt was stirred for 20 minutes at

an average mixing speed of 150-200 rpm to make a

vortex in order to disperse the particles in the melt. After

thorough stirring, the melt was poured into steel moulds

of 35 mm diameter and 100 mm length and allowed to

cool to obtain cast rods. The samples of 35mm diameter

and 20 mm length were prepared from these cast rods.

Stir casting equipment used for the fabrication of

composites is shown in Fig. 1.

Fig. 1 stir casting equipment

The composition of AA 6061 was shown in Table 1.

Table 1 Composition of 6061AA by weight %

III. TAGUCHI EXPERIMENT: DESIGN AND

ANALYSIS

Essentially, traditional experimental design

procedures are too complicated and not easy to use. A

large number of experimental works have to be carried

out when the number of process parameters increases. To

solve this problem, the Taguchi method uses a special

design of orthogonal arrays to study the entire parameter

space with only a small number of experiments.

Taguchi methods have been widely utilized in

engineering analysis and it consists of a plan of

experiments with the objective of acquiring data in a

controlled way in order to obtain information about the

behavior of a given process. The greatest advantage of

this method is the saving of effort in conducting

experiments, saving experimental time, reducing the

cost, and discovering significant factors quickly.

Taguchi’s robust design method is a powerful tool for the

design of a high-quality system. In addition to the S/N

ratio, a statistical analysis of variance (ANOVA) can be

employed to indicate the impact of process parameters

on metal removal valves.

Normally, the full factor would require 34

=81

experimental runs. However, the effect and experimental

cost for such a design may unrealistic. According to the

Taguchi quality design concept, L9 orthogonal array is

used for the experimentation. Table 2 shows the EDM

parameters used as control factors and their levels.

MINITAB15 software is used for graphical analysis of

the experimental data.

Sl.

No.

Control

factors Level - I Level - II Level – III

1. Pulse current

(Amps) 6 10 14

2. Gap voltage

(Volts) 40 50 60

3.

Pulse on time

Ton

(Seconds)

4 6 8

4.

Pulse off time

Toff

(Seconds)

5 7 9

IV. Experimental Set Up

The experiments were conducted on

METATECH EDM machine which is shown in Fig 2.

Here electrical energy is used to generate electrical spark

and material removal occurs mainly due to thermal

energy of the spark. EDM is mainly used to machine

materials with high toughness, high strength and

temperature resistant alloys. EDM can be used to

machine complex geometries in small batches or even on

job-shop basis. Work material to be machined by EDM

must be electrically conductive. In this work tool is taken

as cathode (-) and work piece is taken as anode (+) and

Component Amount (wt%)

Aluminium Balance

Magnesium 0.8-1.2

Silicon 0.4 – 0.8

Iron Max. 0.7

Copper 0.15-0.40

Zinc Max. 0.25

Titanium Max. 0.15

Manganese Max. 0.15

Chromium 0.04-0.35

Others 0.05



the metal is removed from the work piece due to erosion

caused by rapidly recurring spark discharge taking place

between the tool and work piece. Tool material should be

such that it would not undergo much tool wear when it is

impinged by positive ions. In this experiment, copper

electrode is used as tool material. The functions of

dielectric fluid in EDM are to help in initiating discharge

by serving as a conducting medium when ionized, and

conveys the spark. In this experiment, Kerosene was

employed as a dielectric fluid. In this investigation gap

voltage, pulse current, pulse ON time and pulse OFF

time are considered as machining parameters. Discharge

current (I) is the value of the current applied to the

electrode during pulse on-time. Discharge current is one

of the primary input parameters considered together with

discharge duration, constant voltage for given tool and

work piece materials. Pulse ON time is the time for

which current is applied to the electrode during each

EDM cycle. The material removed is directly

proportional to the quality of energy applied during pulse

ON time. This energy is controlled by the current and

pulse ON time. Pulse OFF time is the waiting interval

during two pulse on-time periods.

Fig. 2 Experimental setup of EDM

The MRR have been calculated by analyzing the weight

difference of the work piece using a digital weighting

scale of 0.001 gram precision. The surface roughness

(Ra) has been measured by using “Mitutoyo surface

roughness tester” Ra on each faces of the specimen is

measured and its average value has been reported in

Table 3. The experimental result along with the S/N ratio

has been displayed in Table 3.

V. RESULT AND DISCUSSION

From the experimental data, S/N ratio (η) for

MRR and Ra has been calculated by considering MRR as

“Larger is better” type problem and the Ra as “smaller is

better” type problem. The calculated S/N ratio has been

shown in Table 3. The Minitab 15 statistical software has

been used for analysis.

In order to determine the level of influence of the control

factors the analysis of variance (ANOVA) was

performed for the MRR and Ra data as shown in Table 4

and 5 respectively.

Table 3. Experimental results.

S.No A B C D MRR

(g/min)

S/N ratio

for

MRR

Ra

(µm)

S/N ratio

for

Ra

1 1 1 1 1 0.0258 -31.767 1.32 -2.41148

2 1 2 2 2 0.0356 -28.971 1.54 -3.75041

3 1 3 3 3 0.125 -18.0618 1.10 -0.82785

4 2 1 2 3 0.0275 -31.2133 1.23 -1.7981

5 2 2 3 1 0.365 -8.75414 1.08 -0.66848

6 2 3 1 2 0.014 -37.0774 2.01 -6.06392

7 3 1 3 2 0.189 -14.4708 1.87 -5.43683

8 3 2 1 3 0.25 -12.0412 1.57 -3.91799

9 3 3 2 1 0.29 -10.752 1.33 -2.47703

Table 4 Analysis of MRR data

ANOVA for MRR η value for MRR

Control factor

DF SS % Contribution

Mean ᶯ by factor level(dB)

Level-1 Level-2 Level-3

A 2 367.88 39.71996 -26.27 -25.82 -12.42

B 2 128.90 14.0823 -25.82 -16.59 -21.96

C 2 282.91 30.66482 -26.96 -23.65 -13.76

D 2 147.21 15.5329 -17.09 -26.84 -20.44

ERROR 0 Overall mean(ᶯa)=-17.09

TOTAL 8 926.90 `100 Optimal combination: A3C3

DF- Degrees of Freedom; SS- Sum of squares.

Table 4 reveals the fact that the voltage and the

pulse ON time were the predominant factors in

determining the metal removal rate. The energy content

of the spark is proportional to the pulse on time. Hence it

exhibits the increasing trend with the MRR as depicted in

Fig 3.



Fig. 3 Main Effects Plot for SN ratios

From the Table 5, it is evident that the pulse off

time is the only factor that influences the Ra. The pulse

off time exhibits the decreasing trend with surface finish

as shown in fig 4. This is due to the higher energy pulses

which erodes and evaporates higher amount of material

that produces larger crater on the work piece.

Table 5 Analysis of surface roughness

ANOVA for Ra η value for Ra

Control

factor

D

F

SS %

Contributi

on

Mean ᶯ by factor level(dB)

Level-1 Level-2 Level-3

A 2 4.0799 14.099 -2.330 -2.843 -3.944

B 2 0.3174 1.0968 -3.215 -2.779 -3.123

C 2 5.5651 19.2321 -4.131 -2.675 -2.311

D 2 18.9741 65.5715 -1.852 -5.084 -2.181

ERROR 0 Overall mean(ᶯa)= -3.039

TOTAL 8 28.9365 Optimal combination:D2C1

DF- Degrees of Freedom; SS- Sum of squares; Ra- Surface roughness.

Fig. 4 Main effects plot for Surface roughness

The optimal machining conditions for MRR and

Ra had been A3C3 and D2 respectively as displayed in

Table 4 and 5. The level of C (pulse on time) for optimal

MRR and Ra has been totally contradictory. Hence

achieving higher metal removal rate together with better

surface finish has never achieved. Therefore an

appropriate technique needs to be explored in future.

VI. CONCLUSION

This study has discussed an application of the Taguchi

method for investigating the effects of process

parameters on the metal removal rate value in the electro

Discharge machining of 6061AA/5%SiC composites. In

the EDM process, the parameters were selected taking

into consideration of manufacturer and industrial

requirements. From the analysis of the results in the

EDM process using the conceptual signal-to-noise (S/N)

ratio approach, analysis of variance (ANOVA), and

Taguchi’s optimization method, the following can be

concluded from the present study:

1. Statistically designed experiments based on Taguchi

methods were performed using L9 orthogonal arrays

to analyze the metal removal rate as response

variable. Conceptual S/N ratio and ANOVA

approaches for data analysis drew similar

conclusions.

2. In this study, the analysis of the confirmation

experiment for metal removal rate and surface

roughness has shown that Taguchi parameter design

can successfully verify the optimum cutting

parameters (A3C3) for MRR and (C1D2) for surface

roughness, which are voltage=60 V (A3), pulse ON

time= 8sec(C3).

3. Metal removal rate increases with voltage, pulse on

time and Surface roughness minimize with pulse off

time in electric discharge machining of 6061

Aluminum alloy/5%SiC composites.

VII.REFERENCES

[1] P. Narender Singha, K. Raghukandana, and

B.C. Paib (2004), Optimization by Grey

relational analysis of EDM parameters on

machining Al–10%SiCP composites, Journal of

Materials Processing

Technology,Vol.40,pp.155–156.

[2] Margaret, A. Rajadurai, K and G.

Satyanarayana (2004), Electric discharge

machining of Al-SiC metal matrix composite

using rotary tube electrode, Journal of Material

Processing Technology,Vol.35,pp.153–154

[3] Kagaya, D.G. Solomon and M. Fuad Bahari

(2007), A review on current research trends in

electrical discharge machining, International

Journal of Machine Tools and

Manufacture,Vol.47,pp.1214–1228.

[4] Kanagarajan, R. Singh, T.P. Singh and B.L.

Sethi (2008), A review Surface modification by

electrical discharge machining, Journal of

Materials Processing Technology, Vol.52,

pp.3675–3687.

[5] Rajmohan T, Prabhu R.,Subba Rao and

G.Palanikumar K (2012), Optimization of

Machining parameters in Electrical Discharge

Machining (EDM) of 304 stainless Steel

Procedia Engineering,Vol.38,pp.1030-1036.

[6] T.M. Chenthil Jegan, M.Dev Anand and

D.Ravindran (2012), Determination of Electro

Discharge Machining Parameters in AISI202

Stainless Steel Using Grey Relational Analysis,

Procedia Engineering, Vol.58,pp.4005-4012.

OPTIMIZATION OF EDM PROCESS PARAMETERS ON ALUMINIUM ALLOY 6061/5% SiC COMPOSITE

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