Aluminum metal matrix composites have gained significance in various engineering
industries due to their high specific strength, stiffness and fatigue resistance,
low density and thermal expansion coefficient. The need for these materials
in automotive, defense and aerospace industries is high.
Machining of aluminum metal matrix composites by conventional processes generally
resulted in excessive tool wear caused by the hard reinforcement, high cost
of machining, unacceptable short tool life and the subsurface damage (Manna
and Bhattacharya, 2005; Ozben et al., 2008;
Muthukrishnan et al., 2008; Davim,
2003). Hence, non-conventional machining of composites started gaining importance,
especially electrical discharge machining.
Machining aluminum metal matrix using EDM is one of the most extensively used
non-conventional material removal processes. Its uniqueness, that is, the use
of thermal energy to machine electrically conductive parts regardless of hardness
has been its distinctive advantage. Another characteristic of EDM is the lack
of direct contact between the electrode and the work piece, thus eliminating
mechanical stress, chatter and vibration during machining (Ho
and Newman, 2003).
Hocheng et al. (1997) conducted a preliminary
study of material removal in EDM of SiC/Al. They investigated the material removal
characteristics in single and continuous discharge. A correlation between the
major machining parameters, electrical current and on time and the crater size
produced by a singles spark plug was presented. For effective EDM, large electrical
current and short on time were recommended. They also concluded that single
pulse is better because in the continuous pulse, the discharge of SiC/Al is
more irregular and the material removal rate is faster only at the beginning
followed by being retarded due to existence of SiC particles in the gap.
Hung et al. (1994) investigated the feasibility
of applying electrical discharge machining process for cast aluminium MMCs reinforced
with silicon carbide particles. Feasibility of using the non-conventional EDM
process for MMCs was confirmed and models based on two-level factorial experiments
were developed. They concluded that the power level greatly affected the Material
Removal Rate (MRR) and the recast layer. The current alone controlled the surface
Wang and Yan (2000) optimized the blind hole drilling
of AlO3/6061 Al composite using rotary electro-discharge machining
by using Taguchi methodology.
Experimental results confirmed that the copper electrode with an eccentric
through hole had the optimum performance. The polarity of the electrode largely
affected either MRR or SR whilst the peak current mainly affected EWR. The increase
of either the rotational speed of the electrode or the injection flushing pressure
of the dielectric fluid, or the presence of two eccentric through holes in the
electrode might result in higher MRR. They proposed semi-empirical expressions
to simplify the evaluation of the MRR, EWR and SR with several parameters under
various machining conditions.
Mohan et al. (2004) carried out investigation
on electric discharge machining of Al-SiC MMCs using rotary tube electrode.
Various input variables were used to assess the machinability. Peak currents
were confirmed to have positive effects on the MRR, EWR and SR. The MRR, EWR
and SR were more with positive polarity of the electrode than at negative. The
electrode hole diameter and rotational speed had major effect on MRR, EWR and
SR. Genetic algorithm was used to find optimum machining parameters.
Ghoreishi and Atkinson (2002) made a comparative experimental
study of machining characteristics in vibratory, rotary and vibro-rotary electro-discharge
machining. The effects high and low-frequency forced axial vibrations of the
electrode, rotation of the electrode, and combinations of these methods in respect
of MRR, tool wear rate and surface roughness in die sinking EDM with a flat
electrode were compared. The combination of ultrasonic vibration and rotation
of electrode leads to increases in MRR, TWR and SR. Thus, for optimum parameter
settings, a compromise should be made between SR and MRR or TWR. This case was
modeled by stepwise linear regression, significant parameters were found by
ANOVA and optimum machining parameter settings were obtained using overlay contour
Koshy et al. (1993) suggested when the provision
of holes in the electrode is impracticable, flushing of the working gap poses
a major problem. Use of a rotating disk electrode was proposed as a more productive
and accurate technique than use of a conventional electrode. Material removal
rate, tool wear rate, relative electrode wear, corner reproduction accuracy
and surface finish aspects of a rotary electrode were compared with those of
a stationary one. The effective flushing of the working gap brought about by
the rotation of the electrode remarkably improved material removal rate and
machines surfaces with a better finish.
The Grey relational analysis proposed by Deng (1989)
has been proved to effectively resolve the complicated interrelationships among
multiple performance characteristics of the EDM process (Lin
and Lin, 2002; Singh et al., 2004; Jung
and Kwon, 2010). Lin and Lin (2002) proposed a new
approach for the optimization of the electrical discharge machining process
with multiple performance characteristics based on the orthogonal array with
the Grey relational analysis. Optimal machining parameters were determined by
the grey relational grade as the performance index. The machining parameters,
namely work piece polarity, pulse on time, duty factor, open discharge voltage,
discharge current and dielectric fluid are optimized with considerations of
multiple performance characteristics including material removal rate, surface
roughness and electrode wear ratio. Experimental results have shown that machining
performance in the EDM process can be improved effectively through this approach.
Singh et al. (2004) used Grey relational analysis
for optimization of EDM parameters on machining Al-10% SiCP composites.
Orthogonal array was employed with Grey relational analysis to optimize the
multi response characteristics. The experimental result for the optimal setting
shows that there is considerable improvement in the process. The application
of this technique converts the multi response variable to single response Grey
relational grade and therefore, simplifies the optimization procedure.
Jung and Kwon (2010) used Grey relational analysis
for optimization of EDM parameters in machining micro-hole to a minimum diameter
and maximum aspect ratio. They obtained optimum conditions of the machining
parameters to machine a micro-hole of 40 μm average diameter and an aspect
ratio of 10 by using the Grey relational analysis.
The goal of the present study was to determine the optimal machining parameters
for the formation of a blind hole of 12 mm diameter and 5 mm depth under the
given machining conditions. The Taguchi method was employed to elucidate the
effect of the machining parameters on the characteristics of the EDM process.
Additionally, Grey relational analysis was used to find the optimal machining
parameters satisfying the multiple characteristics of the EDM process.
MATERIALS AND METHODS
EDM machine: The machine used for this work was SPARKONIX die-sinking
EDM. Feed in the vertical direction was controlled by a servo drive. The dielectric
fluid used was kerosene and the electrode suction flushing method was used.
|| Electrical discharge machining conditions
|| Work material specifications
|| Machining parameters and their levels
|| Multihole electrode
Removal of debris generated during the machining process being indispensable
to maintain smooth processing during electrical discharge machining, a multihole
electrode was selected over electrode rotation and ultrasonic vibration techniques.
The electrical discharge machining conditions are given in Table
Materials: The work piece was 6061 Aluminium alloy reinforced with 15%
of SiC particles (by volume). The work piece was a disc of diameter 70 and 12
mm thick. The specifications of the work piece are given in Table
The electrode used was electrolytic copper rod with an array of 2 mm holes
drilled in it was used as multihole electrode as shown in Fig.
Experiments and results
Experimental condition The purpose of experiments was to form a blind hole
of 12 mm diameter and 5 mm depth. From the results of preliminary experiments
a multihole electrode with an array of 12 holes, each of diameter 2 mm was selected.
To determine the machining conditions suitable for a hole of 12 mm diameter
and 5 mm depth, the Taguchi method was used. The selected machining parameters,
listed in Table 3, were polarity, discharge current, pulse
on time, pulse off time and pressure of the dielectric fluid. An L18 (21x37)
orthogonal array was selected to determine the 18 trial conditions and their
results are shown in Table 4.
The total time of machining can be given as:
||Time for machining with multihole electrode
||Time for breaking the fins
||Time for finishing with solid electrode
Results of experiment: The experimental results are illustrated in Fig.
2 and the quantitative results for machining time, electrode wear and surface
roughness are shown in Table 5.
Analysis of result according to the polarity: The machining time decreased
from 26.76 min to 20.16 min for the sacrifice of electrode wear rate, which
increased from 14.13 to 22.21 mg min-1 when the polarity of the tool
changes from positive to negative. The reason may be that the transfer of energy
during the charging process was more when the tool was kept at negative polarity
than at the positive. The surface roughness decreased from 7.04 to 6.30 μm.
||Plots of factor effects (a) x = Machining time, (b) x = Electrode
wear rate and (c) x = Surface roughness s/n ratio η = -10log10
|| Experimenters log and experiment results
|| Experimental results by Taguchi method
Analysis of result according to the discharge current: As the discharge
current increased from 4 to 12 amps, the machining time considerably decreased
from 33.96 to 17.92 min. The electrode wear rate and surface roughness increased
from 6.19 to 28.55 mg min-1 and 5.37 μm to 7.94 μm, respectively.
Those phenomena could be attributed to the increased input current lead to increased
discharge energy. The increased discharge energy resulted in a larger sparks,
which removed the debris generated quite effectively. In addition to that, debris
removal at the machining gap is enhanced by forcing the dielectric fluid through
the holes provided in the multihole electrode. The higher discharge energy resulted
in accelerated tool wear and increased surface roughness.
Analysis of result according to the pulse on time: The machining parameters
most influenced by the variation of pulse on time were the machining time and
surface roughness. As the pulse on time increased from 200-600 μsec, the
machining time decreased from 42.84 to 13.86 min. With increase in pulse on
time, energy density on the work piece increased leading to reduced machining
time. The improved flushing conditions by using the multihole electrode also
lead to reduced machining time. The surface roughness increased from 4.56 to
8.95 μm. This was because of formation of larger crater on the surface
of the work piece. The electrode wear rate slightly increased from 17.49 to
18.35 mg min-1.
Analysis of result according to the pulse off time: The pulse off time
variation slightly influenced the machining time and surface roughness. When
the pulse off time varied from 20-60 μsec, the machining time increased
from 19.88 to 27.53 min and surface roughness decreased from 7.12-6.72 μm.
Increased pulse off time meant that the interval between discharges was long
enough to recharge the circuit. As a result, the increment in pulse off time
increased the machining time. The effective flushing with the multihole electrode
reduced debris adherence with the work piece. This reduction in debris adherence
improved the surface finish. The electrode wear rate marginally increased from
17.46 to 18.34 mg min-1.
Analysis of result according to the dielectric pressure: As the dielectric
pressure increased from 0.25 to 0.75 kg cm-2, the machining time
increased from 23.33 to 26.46 min. There was decrease in electrode wear rate
from 22.80 to 20.19 mg min-1. Those phenomena could be due to the
cooling effect produced by the dielectric fluid during flushing Yilmaz
and Okka (2010). Too much of cleaning of the gap often reduced discharge
frequency because of the fast recovery of insulation resulted in increased machining
time and decreased electrode wear rate. The surface roughness increased from
6.62 to 7.11 μm. The increased debris adherence due to cooling effect resulted
in increased surface roughness.
Determination of machining condition
Grey relational analysis: The original reference sequence and the sequence
for comparison can be represented as Xo (k) and Xi (k),
I = 1, 2,
, m; k = 1, 2,
, n, respectively. Here, m is the total
number of experiments, while n is the total number of observation data. If the
target value of the original sequence is the-larger-the-better,
then the original sequence is normalized according to:
If it is the-smaller-the-better, then the original sequence is normalized as:
In the case where there is a specific value, the original is normalized as:
Here, OB is the target value.
Grey relational coefficient and grey relational grades: The grey relational
co-efficient is defined as follows:
Δoi(k) is the deviation sequence of the reference sequence
X0* and the comparability sequence Xi*, that is:
where, ξ is the distinguishing coefficient, ζε [0,1].
A Grey relational grade is a weighted some of the Grey coefficient and is defined
Here the Grey relational grade γ (X0*, Xi*)
represents the level of correlation between the reference and the comparability
sequence. If a particular comparability sequence is more important to the reference
sequence than the other comparability sequence, the Grey relational grade for
that comparability sequence and the reference sequence will exceed that for
the other Grey relational grades.
Evaluated Grey relational coefficient and grades: The experimental results
for the machining time, electrode wear rate and surface roughness are listed
in Table 5. Since smaller values for those parameters were
desirable, the data sequence had the-smaller-the-better characteristic.
|| Experimental results after normalization process
|| Calculated grey relational co-efficient and grade
Hence, Eq. (3) is employed for the data processing, the
results of which are listed in Table 6. Eq.
5 was utilized to determine the Grey relational coefficient and Eq.
(7) was used to find the Grey relational grade. The results are tabulated
in Table 7. In this case, the reference sequences were set
to 1, that is, Xi* (k) = 1 and the distinguishing coefficient
ζ was set to 0.5.
ANOVA results of grey relational grades: Table 8 lists
the results of the analysis of variance (ANOVA) for the machining time, electrode
wear rate and surface roughness using the calculated values for the Grey relational
coefficients and Grey relational grades of Table 7. The Table
8 figures show that the contribution of discharge current was the most significant,
with 63.72% contribution. The significant factors were the dielectric pressure
with 10.85% and pulse on time with 10.03% contribution. These three parameters
controlled the machining time, electrode wear rate and surface roughness simultaneously
Based on the above discussion, the optimal machining parameters are the electrode
polarity at level 2, discharge current at level 1, pulse on time at level 2,
pulse off time at level 1 and dielectric pressure at level 2.
Confirmation tests: Once the optimal level of the machining parameters
was selected, the final step was to predict and verify the improvement of the
performance characteristics using the optimal level of machining parameters.
The estimated grey relational grade
using the optimal level of the machining parameters can be calculated as:
where, γm is the total mean of the grey relational grade,
is the mean of the grey relational grade at the optimal level and q is the number
of machining parameters that significantly affects the multiple performance
|| Results of analysis of variance (ANOVA)
|| Results of machining performance using initial and optimal
Based on Eq. 8, the estimated grey relational grade using
the optimal machining parameters can then be obtained. Table 9
shows the results of the conformational experiment using the optimal machining
parameters. As shown in the Table 9, machining time was decreased
from 12.84 to 8.64 min, electrode wear rate was improved from 23.8 to 9 mg min-1
and surface roughness was improved from 8.895 to 4.780 μm. It was clearly
shown that the multiple performance characteristics in the EDM process were
greatly improved through this study.
In this study attempt was made to find the optimal machining conditions for
drilling of a blind hole of 12 mm diameter and 5 mm depth. The Taguchi method
was employed to determine the relations between the machining parameters and
the process characteristics. The machining parameters affecting the blind hole
drilling were revealed by executing 18 experiments. To determine the machining
parameters affecting the machining time, the electrode wear rate and surface
Grey relational analysis was used. The discharge current was found to be the
most significant controlling parameter. The dielectric pressure and pulse on
time were the significant factors affecting the process characteristics.
The obtained optimal machining conditions were electrode polarity negative,
discharge current 4 amps, pulse on time 400 μsec, pulse off time 10 μsec
and dielectric pressure 0.5 kg cm-2. Under these conditions machining
time was 8.64 min, electrode wear rate was 9 mg min-1 and surface
roughness was 4.78 μm.