modelling of material removal and surface roughness on ... · modelling of material removal and...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:01 46

145001-7676-IJMME-IJENS © February 2014 IJENS

I J E N S

Modelling of Material Removal and Surface

Roughness on Machining of Ti-6Al-4V Through EDM

Process G.Kumanan

1, D.Kanagarajan

2 and R.Karthikeyan

3

1Assistant professor in Manufacturing Engineering, Directorate of Distance Education, Annamalai University.

Email:[email protected] 2Assistant professor in Manufacturing Engineering, Annamalai University, Annamalai Nagar, Tamilnadu, India.

Email:[email protected], 3Department of Mechanical Engineering, Birla Institute Technology, Dubai.

Email:[email protected]

Abstract— The present work investigates the influence of

processing parameters of electrical discharge machining (EDM)

such as peak current, pulse on time, rotational speed and flushing

pressure. This study also attempts to study the effects of

parameters on EDM performance characteristics on Ti-6Al-4V

alloy using copper electrode as tool material. The performance

characteristics are analysed in terms of material removal rate

(MRR) and surface roughness (Ra), by varying the process

parameters. Mathematical model correlating the influences of

these variables on performance characteristics of the workpiece

are investigated. Design of experiments (DOE) and Response

Surface Methodology (RSM) are used to identify an optimal

machining condition. Analysis of variance (ANOVA) has been

performed for the validity test of the fit and adequacy of the

proposed models. The optimum machining conditions in favour

of MRR and Ra are estimated and verified with proposed

optimized results and the amounts of relative errors calculated. It

is observed that the developed model is within the limits of the

agreeable error (2%), when compared to experimental results.

This result leads to desirable MRR, Ra and economical industrial

machining to optimize the process parameters. This confirms the

feasibility and effectiveness of the adopted approach.

Index Term-- EDM, Ti-6Al-4V, MRR, Ra, RSM, ANOVA.

I. INTRODUCTION

The demand for complicated shapes within tighter

tolerances in many industrial applications is gradually

increasing with the introduction of new materials. This has

been a big challenge for engineers to meet out these demands.

The usage of titanium and its alloys is increasing in many

industrial and commercial applications. Titanium alloy is

unique in that it has excellent properties such as a high

strength–weight ratio, high temperature strength and

exceptional corrosion resistance [1]. In spite of its strength and

increased utility of titanium alloys, it is very difficult to

machine titanium alloys economically with conventional

techniques due to their poor machinability. The energy-based

technique, such as electrical discharge machining (EDM), has

continued to advance and gain favour as an alternative to

traditional machining methods. Different from traditional

machining methods, the material removal in the EDM process

is achieved through melting and vaporization [2,3].

EDM has been widely applied in modern metal

industry for producing complex cavities in moulds and dies,

which are difficult to machine by conventional machining.

The use of electrical discharge machining in the production of

forming tools to produce plastics mouldings, die castings,

forging dies etc. Electrical discharge machining (EDM)

technique possesses noticeable advantages over other

conventional machining process and can machine any hard

material effectively [4,8]. Proper selection of parameters in

EDM is very much essential to achieve better performance

characteristics that are still challenging for a particular

machining setup. The evaluation of the performance

characteristics and optimization of machining parameters have

received maximum research attention. It is in general observed

that out of the two main performance characteristics, material

removal rate (MRR) and surface roughness (Ra) have

traditionally received greater research attention [9-12].

The proposed work emphasizes the development of

mathematical models using RSM, based on the experimental

results for correlating the process variables such as peak

current (I, amp), pulse on time (Ton, μs), electrode rotational

speed (S, rpm), and flushing pressure (P, kg/cm2). Finally, an

attempt has been made to obtain optimum machining

conditions with respect to each of the machining parameters

considered in the present study with the help of response

surface methodology.

The remainder of the paper is organized as follows;

Section 2 describes the experimental procedure. Results are

discussed in section 3. Section 4 concludes the work with

future directions.


145001-7676-IJMME-IJENS © February 2014 IJENS I J E N S

II. EXPERIMENTAL PROCEDURE

In the present work experiments were designed on the

basis of experimental design technique using response surface

design method.

A. Equipment Used

A number of experiments are conducted to study the

effects of various machining parameters on EDM process.

These studies have been undertaken to investigate the effects

of peak current (I), pulse on time (Ton), electrode rotational

speed(S) and flushing pressure(P) on MRR and Ra. The

selected workpiece material for the research work is Ti-6Al-

4V. Copper cylindrical electrode of 12mm diameter is used as

a tool and kerosene is used as a dielectric fluid.

The equipment used to perform the experiments is a

die sinking EDM (Electronica-M100 MODEL) machine, it is

equipped with transistor switched power supply. The electrode

is fed downwards under servo control into the workpiece. The

dielectric fluid is circulated by jet flushing one of the primary

objectives is to study the effect of rotation of tool. Therefore, a

mechanism to rotate the tool is developed and the electrode is

rotated to machining on the surface of the work piece. Hence

all the experiments are performed with peak current, pulse on

time, electrode rotational speed and flushing pressure as

process variables. The ranges of these parameters are selected

on the basis of preliminary experiments conducted by using

one variable at a time approach.

Experiments were conducted according to DOE

concept covering full range of input variables. For each

experiment, a new set of tool and workpiece has been used.

The response variables selected for this study is metal removal

rate (MRR) and surface roughness (Ra) are calculated.

B. Material Removal Rate Measurement

Metal removal rate (MRR) is expressed as the ratio of

the difference of weight of the workpiece before and after

machining to the machining time.

( g/min)

(1)

Where ' and ' are the weights of the

workpiece before and after machining, and ’t’ is the

machining time.

C. Surface Roughness Measurement

There are various methods available for measuring

the surface roughness of the workpiece. The surface roughness

have been measured on a surf coder SE 1200 surface testing

analyser. For each sample, five set of readings on surface

roughness have been taken and average value of those five

readings has been considered as the final reading.

D. Design of Experiment (DOE)

The design of experiments technique is a very

powerful mathematical tool, which permits us to carry out the

modelling and analysis of the influence of process variables

on the response variables. The response variable is an

unknown function of the process variables, which are known

as design factors. There are a large number of factors that can

be considered for machining of a particular material in EDM.

However, the literature shows [18] that the following four

machining parameters are the most widespread among the

researchers and machinists to control the EDM process, peak

current(I,amp), pulse-on time(Ton,μs), rotational

speed(S,rpm) and flushing pressure(P,kg/cm2). In the present

study these are selected as design factors while other

parameters have been assumed to be constant over the

experimental domain. The upper and lower limits of a factor

are coded as +2 and -2 respectively, the coded value being

calculated from the following relationships.

(2)

Where is the required coded value of a variable x.

The process variables/design factors with their values on

different levels are listed in Table 1. The selection of the

values of the variables is limited by the capacity of the

machine used in the experimentation as well as the

recommended specifications for different workpiece tool

material combinations. Table 2 shows the experimental design

matrix and results for MRR and Ra in the present study.

TABLE I

PROCESS PARAMETERS AND THEIR LEVELS

Process parameters Levels

-2 -1 0 1 2

Peak Current (A)

5 7.5 10 12.5 15

Pulse on time (μs) 100 200 300 400 500

Rotational speed(rpm) 50 100 150 200 250

Flushing pressure(kg/cm2) 0.5 1 1.5 2 2.5

E. Modelling of EDM characteristics

In statistics, response surface methodology explores

the relationships between several explanatory variables and one

or more response variables. The main idea of RSM is to use a

set of designed experiments to obtain an optimal response. In

this work, RSM is utilized for establishing the relations

between the different EDM process parameters with a variety



of machining criteria and exploring their effects on MRR and

Ra. To perform this task second order polynomial response

surface mathematical models can be developed. In the general

case, the response surface is described as (3).

(3)

Where, y is the corresponding response of MRR and

Ra yield by the various EDM process variables and the xi (1, 2

, , , , n) are coded levels of n quantitative process variables, the

terms C0,Ci,Cii and Cij are the second order regression

coefficients. The second term under the summation sign of this

polynomial equation is attributable to linear effect, whereas the

third term corresponds to the higher-order effects; the fourth

term of the equation includes the interactive effects of the

process parameters. Equation (3) can be rewritten according to

the four variables used as in (4).

(4)

Where: x1, x2, x3 and x4 are peak current (I), pulse on

time (Ton), electrode rotational speed(S) and flushing pressure

(P) respectively. Equation of the fitted model for MRR and Ra

is represented in equation (5) and (6).

MRR=0.51757+0.027696I+0.059038Ton+0.023471S+0.04178

8P+0.015081ITon-0.018344IS+0.00680625IP- .00133125TonS

+0.00249375TonP+0.027194SP-0.00346057I2-0.014111Ton

2

+0.00561443S2-0.00461057P

2g/min. (5)

Ra=0.056943-0.000233333I+0.00351667Ton-0.004375S

+0.00240833P+0.0006625ITon-0.000675IS+0.00005IP-

0.00235TonS-0.004.275TonP+0.0060625SP+0.00134554I2

+0.00449554Ton2 +0.00574554S

2-0.000654464P

2µm. (6)

The adequacy of the above two proposed models have

been tested on behalf of both cases, linear and quadratic by

means of analysis of variance (ANOVA) as shown in Table 3

and 4. The variance is the mean of the squared deviations about

the mean or the sum of the squared deviations about the mean

divided by the degrees of freedom. The fundamental technique

is a partitioning of the total sum of squares and mean squares

into components such as data regression and its error. The

number of degrees of freedom can also be partitioned in a

similar way as discussed in Table 5 and 6. The usual method

for testing the adequacy of a model is carried out by computing

the F-ratio of the lack of fit to the pure error and comparing it

with the standard value. The values of P (<α-level) in the

analysis ascertain that the regression model is significant.

The P-value of the quadratic term is less than α-level

(0.05) for MRR and Ra. The results of the analysis justifying

the closeness of fit of the mathematical models are enumerated.

Therefore it can be concluded that the evolved models given by

(Eqn.5 and 6) has been adequately explained the variation in

the machining parameters on MRR and Ra.

III. RESULTS AND DISCUSSION

A. Analysis for Material Removal Rate(MRR)

The normal probability plot of the MRR is noticed

that the residuals are falling on the straight line, which means

that the errors are normally distributed and the regression

model is fairly well fitted with the observed values as shown in

Fig.1. Material removal in EDM mainly occurs by melting,

evaporation, and spalling. Spalling is typical for some ceramic

materials. This spalling effect is most often related to the

generation of large micro cracks (perpendicular and parallel to

the top surface) generated during EDM. These larger micro

cracks make the separation of a small volume of material

during successive discharges much easier [7]. The effect of

parameters on MMR and Ra has been analysed through

contour graphs. The contour plot can help the response surface

to be visualized. A contour plot shows how a response variable

relates to two factors based on a model equation.



TABLE II EXPERIMENTAL DESIGN MATRIX AND RESULTS FOR MRR AND RA

Experiment

No

Peak current (I)

(A)

Pulse on

time(T) (µs)

Electrode rotational

speed(S) (rpm)

Flushing pressure (P)

(kg/cm2) MRR (g/min) Ra (µm)

1 0 0 0 0 0.5205 0.0571

2 -1 1 1 1 0.6358 0.0700

3 0 0 0 0 0.5205 0.0571

4 0 0 -2 0 0.4948 0.0880

5 -1 -1 1 1 0.5355 0.0758

6 0 2 0 0 0.5721 0.0801

7 0 -2 0 0 0.3558 0.0682

8 1 1 1 1 0.6914 0.0480

9 0 0 0 0 0.5000 0.0560

10 -1 1 -1 -1 0.4840 0.0850

11 1 -1 -1 -1 0.4170 0.0652

12 1 1 -1 -1 0.5650 0.0879

13 -1 -1 1 -1 0.4009 0.0520

14 0 0 0 -2 0.4320 0.0590

15 1 -1 1 -1 0.3960 0.0670

16 1 1 1 -1 0.5130 0.0610

17 1 1 -1 1 0.6360 0.0712

18 2 0 0 0 0.5721 0.0620

19 0 0 0 0 0.5205 0.0571

20 -2 0 0 0 0.4410 0.0620

21 -1 1 1 -1 0.4917 0.0606

22 -1 -1 -1 -1 0.3676 0.0667

23 0 0 2 0 0.5909 0.0712

24 -1 -1 -1 1 0.4080 0.0670

25 -1 1 -1 1 0.4720 0.0700

26 0 0 0 2 0.5719 0.0590

27 0 0 0 0 0.5205 0.0571

28 0 0 0 0 0.5205 0.0571

29 1 -1 -1 1 0.4616 0.0670

30 1 -1 1 1 0.5180 0.0742

31 0 0 0 0 0.5205 0.0571



TABLE III

ANOVA FOR METAL REMOVAL RATE (MRR)

Source Sum of squares Df Mean square F valve P valve

Model 0.19 14 0.013 59.54 0.0001 Significant

Peak current(I)A 0.018 1 0.018 82.22 0.0001

Pulse on time(Ton)µs 0.084 1 0.084 373.60 0.0001

Rotational speed(S)rpm 0.013 1 0.013 59.05 0.0001

Flushing pressure(P)kg/cm2 0.042 1 0.042 187.17 0.0001

I*Ton 0.003639 1 0.003639 16.25 0.0010

I*S 0.0005384 1 0.0005384 24.05 0.0002

I*P 0.0007412 1 0.0007412 3.31 0.0876

Ton*S 0.0000283 1 0.00002836 0.13 0.7266

Ton*P 0.0000995 1 0.0000995 0.44 0.5145

S*P 0.012 1 0.012 52.84 0.0001

I2 0.0003424 1 0.0003424 1.53 0.2340

Ton2 0.005694 1 0.05664 25.43 0.0001

S2 0.009014 1 0.009014 4.03 0.0620

P2 0.0006079 1 0.0006079 2.71 0.1189

Residual 0.003582 16 0.0002239

Lack of fit 0.003222 10 0.0003222 5.37 0.0664 Not Significant

Pure Error 0.60260003 26 0.00006004

Cor Total 0.19 30

S=0.015 PRESS=0.019 R.Sq=98.12% R-Sq(pred) =89.99% R-Sq(Adj)=96.47

In electro discharge machining, interaction between the

parameters also plays a leading role. An interaction occurs

when the change in response from the one level of a factor to

another level differs from the change in response at the same

two levels of a second factor. That is, the effect of one factor is

dependent upon a second factor. In the present study, among

the factors interactions considered for MRR, I*Ton, I*S and

S*P are significant at 95 per cent confidence level. This means

that the pulse current is dependent on pulse on time and

electrode rotation. On the other hand, electrode rotation and

flushing pressure are dependent on each other. Similarly for

Ra, the interaction between the factors I*Ton, I*S, Ton*S,

Ton*P and S*P are significant at 95 per cent confidence level.

Design-Expert® SoftwareMRR g/min

Color points by value ofMRR g/min:

0.6914

0.3558

Residuals

pe

rc

en

t

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00

1

5

10

20

30

50

70

80

90

95

99

Fig. 1.Normal probability plot residuals for MRR.



Design-Expert® SoftwareFactor Coding: ActualMRR g/min

Design points above predicted valueDesign points below predicted value0.6914

0.3558

X1 = A: Peak current(I) AX2 = B: Pluse on Time (Ton) µs

Actual FactorsC: Rotational Speed (S) rpm = 150.00D: Flushing Pressure (P) kgf/cm2 = 1.50

100.00

200.00

300.00

400.00

500.00

5.00

7.50

10.00

12.50

15.00

0.3

0.4

0.5

0.6

0.7

0.8

M

RR

g

/min

A: Peak current(I) A B: Pluse on Time (Ton) µs

Fig. 2 (a). Effect of peak current and pulse on time on MRR.

Fig.2(a) presents the influence of peak current and pulse on

time on material removal rate. The experimental results

evidence that increasing peak ampere increase the material

removal rate for pulse on time. In EDM process, the material

removal rate is a function of electrical discharge energy. The

increase of peak current generates high energy intensity and

due to this energy melts more material from the work piece.

Thus material removal rate increases with increases of peak

current. In general, the power of the spark and frequency

defined by the number of pulse per second determine the

process performance [13]. The low frequency and high power

combination results in high metal removal. As pulse on time

increases the frequency reduces and consequently the long

pulse duration increases material removal. It is revealed from

the results that the combination of high pulse on time and high

power conceive more MRR. The same results are achieved by

the researches of [14] and [15]. Fig.2 (b) and 2(c) depicts the

influence of peak current and flushing pressure on MRR. Also

shows that the value of MRR increases with increasing of

flushing pressure and rotational speed.

TABLE IV

ANOVA FOR SURFACE ROUGHNESS(RA)

Source Sum of squares Df Mean square F valve P valve

Model 0.003346 14 0.000239 372.47 0.0001 Significant

Peak current(I)A 0.000001307 1 0.000001307 2.04 0.1728

Pulse on time(Ton)µs 0.0002968 1 0.00045947 462.53 0.0001

Rotational speed(S)rpm 0.0004594 1 0.00013922 715.88 0.0001

Flushing pressure(P)kg/cm2 0.0001392 1 0.0001392 16.93 0.0001

I*Ton 0.000007022 1 0.000007022 10.94 0.0044

I*S 0.00000729 1 0.00000729 11.36 0.0039

I*P 0.00000004 1 0.00000004 0.62 0.8060

Ton*S 0.00008836 1 0.00008836 137.70 0.0001

Ton*P 0.0002924 1 0.0002924 55.68 0.0001

S*P 0.0005881 1 0.0005881 916.42 0.0001

I2 0.00005177 1 0.00005177 80.78 0.0001

Ton2 0.0005779 1 0.0005779 900.61 0.0001

S2 0.000944 1 0.000944 1471.07 0.0001

P2 0.00001225 1 0.00001225 19.09 0.0005

Residual 0.00001027 16 0.000006417

Lack of fit 0.00000923 10 0.0000000923 5.34 0.0796 Not Significant

Pure Error 0.000001037 6 0.0000001229

Cor Total 0.003356 30

S=0.0008011 PRESS=0.00005458 R-Sq= 99.69% R-Sq(Pred)=98.37% R-Sq(Adj)=99.43





0.3558

X1 = A: Peak current(I) AX2 = C: Rotational Speed (S) rpm

Actual FactorsB: Pluse on Time (Ton) µs = 300.00D: Flushing Pressure (P) kgf/cm2 = 1.50

50.00

100.00

150.00

200.00

250.00

5.00

7.50

10.00

12.50

15.00

0.3

0.4

0.5

0.6

0.7

0.8

M

RR

g

/min

A: Peak current(I) A C: Rotational Speed (S) rpm

Fig. 2 (b).Effect of peak current and flushing pressure on MRR.



0.3558

X1 = A: Peak current(I) AX2 = D: Flushing Pressure (P) kgf/cm2

Actual FactorsB: Pluse on Time (Ton) µs = 300.00C: Rotational Speed (S) rpm = 150.00

0.50

1.00

1.50

2.00

2.50

5.00

7.50

10.00

12.50

15.00

0.3

0.4

0.5

0.6

0.7

0.8

M

RR

g

/min

A: Peak current(I) A D: Flushing Pressure (P) kgf/cm2

Fig. 2 (c). Effect of peak current and rotational speed on MRR.

The flushing pressure of the dielectric fluid enhances

the MRR, with increase in pressure of the dielectric fluid, the

MRR tends to increase. This is because the machining

performance has been improved, as the removed particles in

the machining gap are evacuated more efficiently. When the

cylindrical electrode rotates, due to the centrifugal action, a

new layer of dielectric fluid will be thrown in to machining

gap. This induces a conductive atmosphere for effective

discharge and encourages process stability [7]. The enhanced

discharge increases the MRR and efficiency of the machining

rate. The rate of debris formation is increased at higher peak

current, whereas in the case of rotary electrode, a small whirl

imparted to the electrode brings about a significant increase in

MRR. The maximum MRR (0.6914g/min) is obtained at

highest level of peak current (12.5A), pulse on time (400µs),

rotational speed (200rpm) and flushing pressure (2kg/cm2).

B.Analysis for surface roughness(Ra)

The surface roughness of an Electro Discharge

Machined product can be defined as a chip-forming process

where the chips are spherical debris melted by sparks. So the

surface roughness is depending on the size of spark crater. The

surface roughness is getting worse condition when the current

range was in between 5A to 15A for all electrode materials. A

large discharging energy causes violent sparks and impulsive

forces and results in deeper and larger erosion sand large size

of crater was produced on the machined surface[8]. Fig.3

displays the normal probability plot of the residuals for Ra. It

can be noticed that the residuals are falling on the straight line,

which means that the errors are normally distributed and the

regression model is fairly well fitted with the observed values.

Fig. 3. Normal probability plot for Ra.

Fig.4(a) shows the interaction effect of the pulse on

time with current on Ra. It predicts the Ra value increases with

increasing pulse on time at any value of current. The

machining rate is proportional to the current intensity. High

amperage generally requires a large machining area and

produces greater Ra. This will be observed at high peak current

and long pulse on time; the reason for the larger roughness

values with higher pulse duration can be explained by the

generation of the large craters owing to large amounts of

energy [7].The surface contains larger craters and cracks,

which would result in poor surface finish. The Fig.4(b) and

4(c) shows that the value of Ra increases with increase in peak

current at least up to the maximum level, and its tends to

increase the high valve of flushing pressure. Along with

increased flushing pressure the surface roughness improved

gradually for all current levels, with certain levels after that it

will decrease. With increased peripheral speed of the

electrode, the ignition time delay increases, thus bringing down

the energy transferred though the individual discharges for

material removal. This diminishes the crater dimensions to give

a better roughness value [8].

The least possible surface roughness (Ra)=0.048µm

have been achieved in the following experimental conditions,

I=12.5A, Ton=400µs, S=200rpm and P=2kg/cm2. Optimum

machining parameter combinations for different EDM

Design-Expert® SoftwareRa µm

Color points by value ofRa µm:

0.0929

0.041

Residuals

pe

rc

en

t

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00

1

5

10

20

30

50

70

80

90

95

99



Design-Expert® SoftwareFactor Coding: ActualRa µm


0.041

X1 = A: Peak current(I) AX2 = D: Flushing Pressure (P) kgf/cm2

Actual FactorsB: Pluse on Time (Ton) µs = 300.00C: Rotational Speed (S) rpm = 150.00

0.50

1.00

1.50

2.00

2.50

5.00

7.50

10.00

12.50

15.00

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

R

a

µm

A: Peak current(I) A D: Flushing Pressure (P) kgf/cm2

characteristics are tested through confirmation experiments that

verify reasonably good concurrence with prediction of

response surface method, the corresponding output response

value is (MRR)=0.6812g/min and (Ra)=0.0473µm. The error

between experimental and predicted values lies within 2%.

Obviously, this confirms the reproducibility of the

experimental conclusions. Design-Expert® SoftwareFactor Coding: ActualRa µm


0.041

X1 = A: Peak current(I) AX2 = B: Pluse on Time (Ton) µs

Actual FactorsC: Rotational Speed (S) rpm = 150.00D: Flushing Pressure (P) kgf/cm2 = 1.50

100.00

200.00

300.00

400.00

500.00

5.00

7.50

10.00

12.50

15.00

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

R

a

µm

A: Peak current(I) A B: Pluse on Time (Ton) µs

Fig. 4 (a). Effect of peak current and pulse on time on Ra.

Design-Expert® SoftwareFactor Coding: ActualRa µm


0.041

X1 = A: Peak current(I) AX2 = C: Rotational Speed (S) rpm

Actual FactorsB: Pluse on Time (Ton) µs = 300.00D: Flushing Pressure (P) kgf/cm2 = 1.50

50.00

100.00

150.00

200.00

250.00

5.00

7.50

10.00

12.50

15.00

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

R

a

µm

A: Peak current(I) A C: Rotational Speed (S) rpm Fig. 4(b). Effect ofpeak current and rotational speed on Ra.

Fig .4(c). Effect of peak current and flushing pressure on Ra.

IV. CONCLUSION

In this work, the Ti–6Al–4V alloy was machined by

electrical discharge machining process with different

machining conditions and copper electrode material.

Summarizing the main features of the results, the following

conclusions may be drawn.

RSM revealed that the all four input parameters such

as peak current(I), pulse on time(Ton), electrode

rotational speed(S) and flushing Pressure(P) are the

most influential parameters for MRR and Ra. The

error between experimental and predicted values of

MRR and Ra are within 2 percent.

The optimum MRR(0.6914g/min) and Ra(0.048µm) is

obtained at the corresponding value of input

parameters such as current(12.5A), pulse on

time(400µs), rotational speed(200rpm) and flushing

pressure(2kg/cm2) of die electric fluid. The

improvement of surface finish is also expected at

different level of electrode rotation and flushing

pressure of die electric fluid.

Optimum machining parameter combinations are

compared to the predicted values and the percentage

of error less than 2%.

REFERENCE [1] C.H.C. Haron, and Jawaid, The effect of machining on surface integrity

of titanium alloy TI-6Al-4V, Journal of Material process technology, 166 (2005) 262-274.

[2] S. Prabhu and B.K. Vinayagam, Effect of graphite electrode material

on EDM of AISI D2 tool steel with multiwall carbon nano tube using regression analysis, International Journal of Engineering

Studies,1(2009) 93-104.

[3] K.H. Ho and S.T. Newman, State of the art electrical discharge machining (EDM), International Journal of Machine Tools

Manufacture, 43 (2003) 1287-1300.

[4] M. M. Rahman, Md. Ashikur Rahman Khan, K. Kadirgama M. M. Noor and Rosli A. Bakar, Modeling of Material Removal on

Machining of Ti-6Al-4V through EDM using Copper Tungsten

Electrode and Positive Polarity, International Journal of Mechanical and Materials Engineering, 1(2010) 135-140.

[5] Han-Ming Chow, Bing-Haw Yan, Fuang-Yuan and Jung-Cherng Hung,

Study of added powder in kerosene for the micro-slit machining of titanium alloy using electro-discharge machining, Journal of Materials

processing technology, 101(2000) 95-103.

[6] Ahmet Hascalik, and Ulas Caydas, Electrical discharge machining of titanium alloy (Ti-6Al-4V), Journal of Applied surface science, 253

(2007) 9007-9016.

[7] Yan Cherng Lin, Bing Hwa Yan and Young Chang, Maching characteristics of titanium alloy(Ti-6Al-4V) using a combination

process of EDM with USM Journal of materials processing

technology, 104 (2000) 171-177. [8] Peter Fonda , Zhigang Wanga , Kazuo Yamazaki, and Yuji Akutsu, A

fundamental study on Ti-6Al-4V’s thermal and electrical properties

and their relation to EDM productivity, Journal of materials processing technology,202 (2008) 583-589.

[9] K.M. Tsai and P.J. Wang, Prediction on surface finish in electrical

discharge machining based upon neural network models, International Journal Machine Tools Manufacture, 41(2001) 1385-1403.

[10] D.Kanagarajan, R.Karthikeyan, K.Palanikumar and p.Sivaraj, Influnce

of process parameters on electric discharge machining of wc/30%cocomposites, part B; Journal of engineering manufacture ,222

(2008) 1-9.

[11] D.Kanagarajan ,R.Karthikeyan, K.Palanikumar and J,P. Davim, Application of goal programming technique for Electric discharge

machining (EDM) characteristics of cemented carbide(wc/co),

International Journal of Materials and product Technology,35 (2009 ) 216-227.

[12] G.Krishna Mohana Rao, G Ranga Janardhana, D. Hanumantha Rao and

M. Srinivasa Rao, Development of hybrid model and optimization of metal removal rate in electric discharge machining using artificial



neural networks and genetic algorithm, ARPN Journal of Engineering and Applied Sciences, 209 (2008) 1512-1520.

[13] Purcar Carmen and Ţîţu Mihail Aurel, Theoretical studies on electro

discharge machining with Rotating electrode tool. Romanian Association of Nonconventional Technologies Romania, (2013) 67-74.

[14] J. L.lin, K. S. Wang, B. H. yan and Y. S. Tarng, Optimization of the

electrical discharge machining process based on the Taguchi method with fuzzy logics. Journal of Materials Process Technology, 102,(2000)

48-55.

[15] D.K.Pand and R.K.Bhoi, Electro discharge machining a qualitative approach, Journal of Materials and Manufacturing Processes, 21(2006)

853-862.

[16] K.M.Tsai and P.J.Wang, Semi-empirical model of surface finish on electrical discharge machining, International Journal of Machine Tools

and Manufacture, 41(2002) 1455-1477.

[17] J.T.Huang and Y.S.Liao, Optimization of machining parameters of wire-EDM based on grey relational and statistical analyses,

International Journal of Production Research, 41 (2003) 1707-1720.

[18] S.H. Lee and X.P. Li, Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of

tungsten carbide,Journal of Material Process Technology, 115(2001)

344-358. [19] Saeed Daneshmand, Ehsan Farahmand Kahrizi, Ali Akbar Lotfi

Neyestanak and Mehrdad Mortazavi Ghahi ,Experimental

Investigations into Electro Discharge Machining of NiTi Shape Memory Alloys using Rotational Tool, International Journal of

Electrochemical science, 8(2013) 7484-7497.

G.KUMANAN, have completed B.E (Mechanical) undergraduate in

2001 from Bharathidasan University and post graduate in M.E

(Manufacturing Engineering). After graduation he has joined as a lecturer in Annamalai University, Annamalai nagar, Tamilnadu, where

his current teaching experience is 9 years. I am currently doing my

research under the guidance of eminent professor Dr.D.Kanagarajan, who is specialized in Composite Materials. In future I am planning to

involve myself more in the research area of Composite Materials.

Dr. D. Kanagarajan is working as a Assistant Professor in the

Department of Manufacturing Engineering, Annamalai University,

Annamalai Nagar, India. He received his BE in Mechanical and Production Engineering from Annamalai University and ME in

Production Engineering from Annamalai University. He received his

PhD from Annamalai University. He has contributed about 22 research papers in journals, conferences at national and international level. His

areas of interest are composites, modeling, nontraditional machining

and optimization.

Prof. Dr. R. Karthikeyan is working as a Professor in the Department

of Mechanical Engineering, Birla Institute of Technology, Dubai. He received his BE in Mechanical Engineering from Kumaraguru College

of Technology, Coimbatore, Bharathiyar University and ME in

Production Engineering from Annamalai University. He received his PhD from Annamalai University. He has contributed about 80 research

papers in journals, conferences at national and international level. His

areas of interest are composites, metal forming, metal cutting, modeling nontraditional machining and optimization.

modelling of material removal and surface roughness on ... · modelling of material removal and...

Documents