M. ARULRAJ et al.: OPTIMIZATION OF MACHINING PARAMETERS IN TURNING A HYBRID ALUMINIUM-MATRIX ...
263–268
OPTIMIZATION OF MACHINING PARAMETERS IN TURNING OF
HYBRID ALUMINIUM-MATRIX (LM24–SiC
p
–COCONUT SHELL
ASH) COMPOSITE
OPTIMIZACIJA PARAMETROV STRU@ENJA HIBRIDNEGA
KOMPOZITA (LM24–SiC
p
–PEPEL KOKOSOVIH LUPIN) Z
MATRICO NA OSNOVI ALUMINIJA
Munusamy Arulraj
1
, Ponnusamy Kumaraswamy Palani
2
, Lakshmikanthan Venkatesh
3
1
Department of Mechanical Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore 641109, Tamil Nadu, India
2
Department of Mechanical Engineering, Government College of Engineering, Bargur, Krishnagiri 635104, Tamil Nadu, India
3
Department of Mechanical Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore 641109, Tamil Nadu, India
Prejem rokopisa – received: 2018-08-21; sprejem za objavo – accepted for publication: 2018-11-22
doi:10.17222/mit.2018.184
In any machining process, the vital part is determination of the optimum values for the process parameters to attain the highest
desired quality at a low machining cost. This paper mainly focuses on the surface-roughness optimization in the turning of a
hybrid aluminium-matrix (LM24-SiCp-coconut shell ash) composite through the Taguchi method and a genetic algorithm. All
composite samples for the study were prepared under the optimal squeeze-casting conditions and experimental trials were
selected based on the L9 (3)
4
orthogonal array. The main response considered in this study related to the surface roughness, and
machining parameters such as the cutting speed, feed rate, depth of cut and tool-nose radius were taken into consideration. The
surface roughness was tested on the composites turned with a high-speed CNC lathe machine. From the experimental data, a
regression model of the surface roughness was developed. The optimum machining conditions were obtained through the
Taguchi method and a genetic algorithm and checked through confirmation experiments. In this study, it was concluded that the
genetic algorithm used for determining the optimum machining conditions showed better results than the experimental outcome
based on the orthogonal array and the optimum conditions obtained with the Taguchi method.
Keywords: LM24 aluminium alloy, silicon carbide particles, coconut shell ash, surface roughness, Taguchi method, genetic
algorithm
V katerem koli postopku mehanske obdelave je klju~nega pomena dolo~itev optimalnih procesnih parametrov, ki omogo~ajo
najvi{jo zahtevano kakovost obdelave pri najmanj{ih stro{kih. Avtorji so se v glavnem osredoto~ili na optimizacijo parametrov
stru`enja, ki dolo~ajo povr{insko hrapavost hibridnega kompozita (LM24-SiCp-pepel kokosovih lupin) z Al matrico. Za to so
uporabili Taguchijevo metodo in genetski algoritem. Vsi vzorci za pri~ujo~o {tudijo so bili izdelani s postopkom visoko tla~nega
litja v testastem stanju (angl.: squeeze casting) pri optimalnih parametri~nih pogojih. Eksperimentalni preizkusi so temeljili na
izbrani L9 (3)
4
ortogonalni matrici. Glavni vplivni parametri povr{inske hrapavosti, obravnavani v tej {tudiji, so bili: hitrost
rezanja, hitrost podajanja, globina reza in polmer konice plo{~ice rezilnega orodja. Povr{insko hrapavost kompozitnih vzorcev
so preverjali z merilnikom Mitutoyo SJ-210 po zelo hitrem stru`enju na ra~unalni{ko vodeni (CNC) stru`nici. Na osnovi ekspe-
rimentalnih podatkov so razvili regresijski model za povr{insko hrapavost. Optimalne pogoje mehanske obdelave so dosegli s
pomo~jo Taguchijeve metode in genetskega algoritma in jih preverili s pomo~jo eksperimentalnih postopkov. Avtorji zaklju-
~ujejo, da optimalni pogoji mehanske obdelave, dolo~eni z genetskim algoritmom, dajejo bolj{e rezultate kot eksperimentalni
rezultati, ki temeljijo na optimalnih pogojih, dolo~enih s Taguchijevo metodo za izbrano ortogonalno matrico.
Klju~ne besede: Al zlitina LM24, silicij karbidni delci, pepel kokosovih lupin, povr{inska hrapavost, Taguchijeva metoda,
genetski algoritem
1 INTRODUCTION
Engineering applications require new types of mate-
rials due to the inability of the conventional materials to
fulfill the requirements of the rapidly changing global
market. In the past three decades, extensive research has
been reported by numerous researchers with respect to
mechanical property enhancement. Composite materials
are mostly preferred over the conventional materials for
the advanced engineering applications in the aerospace,
automotive and marine industries due to their flexibility
in tailoring the mechanical properties. Aluminium-
matrix composites always have a firm position in the
light-weight-material category due to their wide range of
desirable characteristics such as low density, good wear
and corrosion resistance and excellent mechanical pro-
perties over the other metal-matrix composites at reason-
able costs.
1–6
In the past, ceramic reinforcements were used to
enhance the mechanical properties but this led to an
increase in the weight and cost for the production of the
composites. This shortcoming induces the quest for
high-performance materials with a low density. The
widely adopted method for producing low-cost compo-
sites enhance the use of inexpensive materials such as
industrial and agro-wastes as secondary reinforcements.
7
Materiali in tehnologije / Materials and technology 53 (2019) 2, 263–268 263
UDK 620.1:620.168:669.71 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 53(2)263(2019)
*Corresponding author e-mail:
arulraj96 gmail.com

The hybrid metal-matrix composites are the current
research interest due to their favorable properties: high
specific strength, toughness, impact strength, low sen-
sitivity to temperature changes, etc. Usually, MMCs are
fabricated through any of the following techniques:
solid-state processing (powder metallurgy), liquid-state
processing (stir casting, squeeze casting, compo casting)
and infiltration technique. Among these methods, the
stir-casting method is widely used because it is an easy
and economical process compared with the other me-
thods. Any fabrication method should ensure a few
essential requirements regarding the casting such as a
uniform distribution of the reinforcement particles within
the matrix and better bonding between them. However,
the major problems associated with the production of
hybrid composites through the stir-casting method are a
non-uniform dispersion, poor wettability and porosity,
and these issues are minimized through the squeeze-
casting method.
8–12
The machining characteristics and qualities of com-
posite parts are another major focus of research because
they directly influence the functional and operational
behavior. One of the major machining qualities of ma-
chined parts is their surface roughness, which plays a
vital role in dynamic working conditions. Due to poor
surface characteristics, fatigue-loaded components are
highly prone to failure.
13
The hard reinforcements (SiC
p
,
B
4
C
p
,T iC
p
and WC
p
) of the MMCs lead to a very com-
plicated machining process due to a hard abrasive nature
of the particles, which results in a high tool wear and
poor surface finish.
14–16
This problem is usually
addressed by adding soft particles such as organic
reinforcements like coconut shell ash or rice husk ash to
hard ceramics particles.
17–20
The process of determining the optimum machining
parameter is usually achieved through selected methods
known as the optimization techniques. A number of
techniques have been adopted for optimizing the process
parameters and they may be categorized as conventional
tools (Taguchi method, response-surface method, grey-
relationship analysis) and soft-computing tools (genetic
algorithm, artificial neural network, particle-swarm
optimization, fuzzy logics).
21–23
Generally, regression
models are developed by relating the process parameters
to the desired output and are obtained with the conven-
tional tools, whereas the soft-computing tools are
adopted for obtaining the correct solution. Many
researchers attempted to optimize the machining para-
meters to achieve certain response criteria such as the
surface roughness, metal-removal rate and tool wear. The
turning process is the basic machining process per-
formed on most of the fundamental machine elements.
The major influencing parameters, reported in the
literature for the turning process, are the cutting speed,
feed rate, depth of cut, workpiece variables, cutting-tool
variables and cutting fluids.
24
Turning parameters for Al-SiC-Gr hybrid metal-
matrix composites were optimized using a grey-fuzzy
algorithm. Researchers investigated the effects of the
spindle speed, depth of cut, feed rate and mass fraction
of SiC
p
on the tool-flank wear, and they further opti-
mized the parameters using the response-surface metho-
dology. The optimization of the machining parameters of
the hybrid composite materials directly affects the
production cost and life of the components.
25–27
Agro-wastes such as rice husk, red mud, fly ash,
coconut shell ash, corncob ash etc. can serve as promis-
ing reinforcements for aluminium-matrix composites.
Coconut shell ash as one of the potential reinforcements
for aluminium-matrix composites has not been studied
adequately due to its machinability characteristics. The
LM24 aluminium alloy, widely used to fabricate
automotive parts like cylinder blocks, pistons, piston
rings, camshafts etc., was used as the matrix material in
this study. The purpose of the present study was to
investigate the effects of high-speed turning parameters
like the cutting speed, feed rate, depth of cut and tool-
nose radius on the surface roughness of hybrid alumi-
nium-metal-matrix composites. The Taguchi method and
genetic algorithm were employed to predict the optimum
cutting conditions for obtaining the optimum surface
roughness on the turned hybrid composite components.
2 MATERIAL AND THEIR CHARACTERISTICS
2.1 Materials used
The LM24 aluminium alloy was used as the matrix
material and its chemical composition is shown in
Table 1. SiC
p
with the average particles size of 150 μm
and coconut-shell-ash particles (150 μm) were used as
reinforcement materials. The fabricated hybrid metal-
matrix composite had fixed shares of the reinforcements:
2.5 % of the coconut-shell-ash particles and 7.5 % of
SiC
p
. However, the resulting percentage of the reinforce-
ment particles decreased the mechanical properties.
28
An LM24 aluminium alloy ingot was melted in an
electric furnace capable of heating up to 1200 °C. The
reinforcement particles including 2.5 % of coconut shell
ash and 7.5 % of SiC
p
were preheated at 500 °C in a se-
parate crucible furnace. The molten metal was fully
degassed using hexachloroethane (C
2
Cl
6
) tablets to
remove the entrapped gases and other impurities present.
M. ARULRAJ et al.: OPTIMIZATION OF MACHINING PARAMETERS IN TURNING A HYBRID ALUMINIUM-MATRIX ...
264 Materiali in tehnologije / Materials and technology 53 (2019) 2, 263–268
Table 1: Chemical composition of LM24
Element Si Fe Cu Mn Mg Cr Ni Zn Al
JIS (w/%) 7.5–9.5  3 3.0–4.0  0.5  0.3  0.5  0.5  3 Balance
Ingot (w/%) 7.848 0.785 3.433 0.14 0.15 0.025 0.049 1.334 86

The use of hexachloroethane (C
2
Cl
6
) tablets is probably
the most common method of degassing in foundries.
Even though it may be the oldest technique, C
2
Cl
6
tablets
normally provide for effective degassing in the case of a
small amount of aluminium melt. The preheated rein-
forcement particles were gradually added into the pure
molten metal while maintaining the constant stirring
speed at 500 min
–1
for 10 min.
28
The optimum squeeze-
casting parametric conditions are given in Table 2.
Table 2: Squeeze casting process parameters
Parameter Value
Squeeze pressure 200 MPa
Pouring temperature 690
o
C
Die preheating temperature 500
o
C
Mold temperature 211
o
C
Pressure duration 15 s
2.2 Microstructure analysis
The specimens were prepared for a microstructure
analysis; they were polished and the surfaces were
cleaned with Keller’s reagent. The casting samples
obtained under the optimal squeeze-casting parametric
conditions showed a homogeneous dispersion of the
reinforcement particles in the matrix phase and good
wettability. The quality of the castings was determined in
terms of porosity, agglomeration of reinforcement,
shrinkage defects etc., which were not visible in the
micro-examination of the specimens, exposing the
quality of the castings. A sample microstructure is shown
in Figure 1.
2.3 High-speed turning
A high-speed turning operation was performed on the
casting samples using a computer-numerical-control
(CNC) turning center (ECOTURN-25) manufactured by
Geedee weiler pvt ltd., Coimbatore, India, shown in Fig-
ure 2. The dimensions of the casting samples used for
the turning operation were 25 mm in diameter and
100 mm in length. Tool holder PDJNL 1616H11 and un-
coated carbide-insert 332-SF H13A cutting tool were
used in the turning operation as shown in Figure 3. The
surface roughness (R
avg
) of the turned casting samples
was measured with a surface-roughness tester (Mitutoyo
SJ-210).
M. ARULRAJ et al.: OPTIMIZATION OF MACHINING PARAMETERS IN TURNING A HYBRID ALUMINIUM-MATRIX ...
Materiali in tehnologije / Materials and technology 53 (2019) 2, 263–268 265
Figure 2: High-speed CNC turning center
Figure 1: Optical microstructure of LM24/SiC
p
/coconut shell ash
composite
Figure 3: Turning of the composite
Table 3: Experimental parameters and their levels
Parameter Notation
Level
123
Cutting speed (min
–1
) A 3250 3500 3750
Feed rate (mm rev
–1
) B 0.1 0.15 0.2
Depth of cut (mm) C 0.1 0.2 0.3
Tool-nose radius (mm) D 0.4 0.8 1.2

2.4 Design of experiments
The most influential parameters affecting the surface
roughness, namely, the cutting speed (A), feed rate (B),
depth of cut (C) and tool-nose radius (D) were consi-
dered and their levels are given in Table 3.
3 RESULTS AND DISCUSSION
3.1 Taguchi method
The Taguchi method is a powerful statistical tool,
widely applied to improve the performance of machining
processes with an extensive reduction of the time for
conducting experiments; it also considerable reduces the
machining cost and improves the product quality.
27,28
The
orthogonal array developed for the set of experiments
and the importance of the signal-to-noise (S/N) ratio,
which together determine how the average value (signal)
of the input variables was attained, and the amount of
variability (noise) were examined.
3.1.1 S/N ratio response
The surface roughness was treated as the output
response and the category of quality characteristics was
the smaller-the-better. The S/N ratio for this response
was estimated using Equation (1) for each experimental
condition and their values are given in Table 4.
S/N
n R
i
i
n
(dB) log =−
⎛ ⎝ ⎜ ⎞ ⎠ ⎟ =
∑ 10
11
10 2
1
(1)
where i=1,2,…,n (here n=4)andR
i
is the response
value for an experimental condition. The mean value (Y)
of the S/N ratios was also calculated using Equation (2)
and the results are given in Table 5.
Mean, Y
N
Y
j
i
N
=
⎛ ⎝ ⎜ ⎞ ⎠ ⎟ =
∑
1
1
(2)
where j=1,2,…,N (here N=9)andY
j
is the S/N ratio
for the j
th
parametric setting.
In order to find the optimum level of the process
parameters, the average S/N ratio response was estimated
for every level of the parameters and the corresponding
details are given in Table 6. Based on the highest value
of the S/N ratio, the optimum level for each parameter
(A: 2
nd
level; B: 3
rd
level; C: 1
st
level; D: 2
nd
level) was
noted.
29–32
Thus, the optimum parametric setting
A
2
B
3
C
1
D
2
(cutting speed: 3500 min
–1
, feed rate: 0.2
mm/rev, depth of cut: 0.1 mm and nose radius: 0.8 mm)
was obtained for the output response.
Table 4: Experimental observations and S/N ratio
Ex.
No.
Parameters and their
levels
Surface roughness (μm)
S/N Ratio
(dB)
ABCDR 1 R 2 R 3 R 4 R avg
1 3250 0.1 0.1 0.4 2.15 2.17 2.13 2.15 2.15 –6.6488
2 3250 0.15 0.2 0.8 1.64 1.66 1.68 1.62 1.65 –4.3497
3 3250 0.2 0.3 1.2 1.87 1.82 1.83 1.88 1.85 –5.3434
4 3500 0.1 0.2 1.2 1.12 1.13 1.13 1.17 1.13 –1.2140
5 3500 0.15 0.3 0.4 1.26 1.22 1.24 1.25 1.24 –1.9382
6 3500 0.2 0.1 0.8 0.42 0.46 0.47 0.45 0.45 2.4988
7 3750 0.1 0.3 0.8 2.52 2.56 2.55 2.54 2.54 –8.1308
8 3750 0.15 0.1 1.2 1.96 1.94 1.94 1.93 1.94 –5.8007
9 3750 0.2 0.2 0.4 1.76 1.75 1.74 1.76 1.75 –4.8608
Y –3.9764
Table 5: Average S/N ratio response
ABCD
Level 1 –5.4473 –5.3312 –3.3169 –4.4826
Level 2 –0.2178 –4.0295 –3.4748 –3.3272
Level 3 –6.2641 –2.5685 –5.1375 –4.1194
Max-Min 6.0463 2.7627 1.8206 1.1553
Rank 1234
Optimum A2 B3 C1 D2
% Contribution 51.3 23.4 15.4 9.8
The response graph shown in Figure 4 describes the
variation of each process control parameter with the out-
put response. From the response graph, the peak points
were taken as the optimum levels of the machining para-
meters, i.e., the cutting speed at the second level, the
feed rate at the third level, the depth of cut at the first
level and the tool-nose radius at the second level.
3.2 Genetic algorithm
Genetic algorithm (GA) is a soft-computing tool for
solving both constrained and unconstrained optimization
problems based on boundary conditions. The optimum
machining conditions were created using the GA tool in
the MATLAB software. GA is a faster and more efficient
tool than the other soft-computing tools.
30
The GA opti-
mization tool is based on machining-performance-
prediction models based on experimental results and
numerical methods. The GA tool provided the optimum
machining conditions in a short time.
3.2.1 Mathematical model
The empirical relationship between the surface
roughness (R
avg
) and turning of the LM24 aluminium
M. ARULRAJ et al.: OPTIMIZATION OF MACHINING PARAMETERS IN TURNING A HYBRID ALUMINIUM-MATRIX ...
266 Materiali in tehnologije / Materials and technology 53 (2019) 2, 263–268
Figure 4: Response graph

alloy reinforced with SiC
p
and coconut-shell-ash parti-
cles was a function of the machining parameters such as
the cutting speed (A), feed rate (B), depth of cut (C) and
tool-nose radius (D) that can be indicated as:
R
avg
= f(A, B, C, D) (3)
The relationship between the control parameters and
their effects on the average surface roughness (R
avg
)was
modeled using a second-order polynomial regression
analysis with the help of statistical software MINITAB
14.
R
avg
= 6.3500 – 4.03333 A – 0.43333 B – 0.55000 C –
0.56667 D + 1.03333 A
2
+ 0.03333 B
2
+ 0.183333 C
2
+
0.13333 D
2
(4)
For this regression model, it was found that r
2
= 0.99
where r is the coefficient of correlation. The value of r
2
indicates the accuracy of the model representing the pro-
cess. As r
2
is nearing unity, this model can be taken as an
objective function for the application of the genetic
algorithm.
3.2.2 Inputs into the GA
The genetic-algorithm solver available in the MAT-
LAB software was used to find the optimum parametric
settings for the minimization of the average surface
roughness of the squeeze castings (R
avg
) from this study.
The regression model given in Equation (3) was used as
the fitness function (objective function). The following
values of the genetic parameters were taken as the inputs
into the MATLAB solver.
Population type: double vector
Number of variables: 04
Bounds (lower): [3250 0.1 0.1 0.4]
Bounds (upper): [3750 0.2 0.3 1.2]
Selection function: stochastic
Crossover fraction: 0.8
Mutation rate: 0.03
Migration: forward
Total number of iterations: 53
Level of display: iterative
It was observed that the fitness value decreased
through generations as shown in Figure 5 and the opti-
mized average surface roughness (0.399 μm) was ob-
tained in the 53
rd
generation. The optimum parametric
settings of the last generation are given in Table 5.
Table 5: Optimum parametric settings
Control parameter Coded condition Uncoded condition
Cutting speed 2 3500 min
–1
Feed rate 3 0.2 mm rev
–1
Depth of cut 1.5 0.15 mm
Tool-nose radius 2.126 0.8 mm
3.3 Confirmation experiments
Confirmation experiments were conducted for the
optimum parametric conditions suggested by the Taguchi
method and genetic algorithm. The average surface-
roughness values (predicted and tested) are given in
Table 6. It is evident that there is good agreement bet-
ween the predicted average surface roughness and actual
surface roughness since the error is less than 4 %. The
optimum settings for the cutting speed (3500 min
–1
) and
feed rate (0.2 mm rev
–1
) were found to be same in the
Taguchi method and genetic algorithm. According to the
percentage-contribution analysis, the effects of depth of
cut and nose radius on the surface roughness are mini-
mum compared to the other machining parameters. The
confirmation experiments proved that the genetic algo-
rithm gives better results than the Taguchi method with
respect to the surface quality and also, indirectly, with
respect to energy savings and production time.
Table 6: Confirmation-test results
S. no.
Optimization
technique
Average surface
roughness, R a
(μm) % error
Predicted Tested
1 Taguchi method 0.467 0.450 3.78
2 Genetic algorithm 0.399 0.386 3.37
4 CONCLUSIONS
The conclusions drawn were based on the surface-
roughness tests conducted on a hybrid aluminium-
metal-matrix composite during a turning operation. From
the percentage-contribution analysis, it was noted that
the cutting speed (51.3 %) and feed rate (23.4 %) were
the most important parameters influencing the surface
roughness, while the depth of cut (15.4 %) and tool-nose
radius (9.8 %) were the least important parameters for
the surface roughness. It is evident that there is good
agreement between the predicted average surface rough-
ness and actual average surface roughness since the error
is less than 4 %. The recommended levels for the turning
parameters of the CNC lathe, minimizing the surface
roughness, were the cutting speed at level 2
(3500 min
–1
), feed rate at level 3 (0.2 mm rev
–1
), depth of
M. ARULRAJ et al.: OPTIMIZATION OF MACHINING PARAMETERS IN TURNING A HYBRID ALUMINIUM-MATRIX ...
Materiali in tehnologije / Materials and technology 53 (2019) 2, 263–268 267
Figure 5: GA result

cut at level 1 (0.1 mm) and tool-nose radius at level 2
(0.8 mm). The optimum settings of the high-speed
turning process parameters regarding the optimum sur-
face roughness can be used wherever aluminium-
metal-matrix composites require a high degree of surface
finish. Confirmation experiments proved that the genetic
algorithm gives better results than the Taguchi method
with respect to the surface quality and also, indirectly,
with respect to energy savings and production time.
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