International Journal of Mechanical and Materials Engineering (IJMME), Vol. 7 (2012), No. 1, 9–15.
ANALYSIS AND PARAMETRIC OPTIMIZATION OF ABRASIVE HOT AIR JET
MACHINING FOR GLASS USING TAGUCHI METHOD AND UTILITY CONCEPT
N. Jagannatha1*, S.S. Hiremath2 and K. Sadashivappa3
1
2
Department of Industrial & Production Engineering, SJM Institute of Technology, Chitradurga 577502, India
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
3
Department of Industrial & Production Engineering, Bapuji Institute of Engineering and Technology,
Davangere 577004, India
*Corresponding author’s E-mail: jagan_nath05@rediffmail.com
Received 26 July 2011, Accepted 11 January 2012
optimization problems in manufacturing engineering
(Vijian and Arunachalam, 2006; Chen and Chen, 2007;
Zhang et al., 2007; Mahapatra and Chaturvedi, 2009;
Basavarajappa et al., 2009; Bushroa et al., 2011; Nor et
al., 2011; Ng et al., 2011) and ANOVA has been used
successfully in process optimization.
ABSTRACT
Carrier media plays a major role in removal of material
in Abrasive Jet Machining (AJM). In this paper, an
attempt has been made to use hot air as carrier media in
AJM. Modified Taguchi robust design analysis is
employed to determine optimal combination of process
parameters. The Analysis Of Variance (ANOVA) is also
applied to identify the most significant factor. It can be
found that the air temperature is the most significant
factor on Material Removal Rate (MRR) and Roughness
of machined surface (Ra). It has been observed that there
is good agreement between the predicted values and
experimental values of optimization. The influence of
temperature on MRR and Ra has been discussed. It can be
found that at high temperature, there is a sufficient
evidence of more plastic deformation accompanied by
brittle fracture failure which results in increase of MRR
and reduction of roughness.
Utility concept is a simple, useful and provides an
appropriate solution for multi-response optimization
problems. It is found that a little work has been reported
(Kaladhar et al., 2011) on multi-response optimization
in machining to determine the best combination of the
process parameters. Recently Grey relational analysis is
successfully employed in conjunction with Taguchi
design of experiments to optimize the multiple response
problems (Sathia and Jaleel, 2010; Das and Sahoo,
2011). The process parameters which affect the shape of
the surface machined by AJM using Design of
experiment
and
ANOVA
were
analyzed
(Balasubramaniam et al., 1999). The design of nozzle
and variable parameters like pressure of carrier media,
abrasive types and size, abrasive flow rate and stand-off
distance have effects on MRR and it has been discussed
by experimental investigations. Drilling of glass sheets
with different thicknesses have been carried out by AJM
in order to determine its machinability under different
controlling parameters (El-Domiaty et al., 2009). An
intermittent jet mechanism to increase the efficiency of
jet in micro-grooving and also developed statistical
models for the prediction and process optimization of
micro abrasive intermittent jet machining (Zhang et al.,
2005). Enough research work has not been carried out on
hot air jet machining. It has been proved that the cutting
of glass material can also be performed using only hot air
jet (Muralidhar et al., 1982). A compact portable hot air
jet gun has been developed for thermal cutting of glass
plate and the effect of various parameters on cutting rate
has been discussed (Prakash et al., 2001).
Keywords: Abrasive hot air jet, Material Removal Rate
(MRR), Roughness, ANOVA, Multi-response S/N ratio.
1. INTRODUCTION
The glass and other brittle materials can be machined by
non-conventional processes such as Ultrasonic
Machining (USM), Abrasive Jet Machining (AJM),
Electrical Discharge Machining (EDM), Electrochemical
Machining (ECM), Laser Beam Machining (LBM) and
Plasma Arc Machining (PAM). Abrasive Jet Machining
has high degree of flexibility, and hence it is typically
used for machining of glass and ceramic materials.
Manufacturers are trying to reduce the operation cost and
increase the quality of products. The surface roughness
and MRR are significant characteristics in machining of
glass using AJM. There is a need to optimize the process
parameters in a systematic way to achieve the output
characteristics /responses by using experimental methods
and statistical models. Taguchi’s robust design method is
suitable to solve the metal cutting problem like milling
with minimum number of trials as compared with a full
factorial design and one factor at a time method (Ghani et
al., 2004). Tasirin et al. (2007) reported that Taguchi
method can also be applied for food drying problems to
optimize process parameters.
From the above literature survey, it has been found that
the existing research works on AJM have not focused on
carrier media. In this paper, an attempt has been made to
use hot air as carrier media in AJM. In this consideration,
an abrasive hot air jet machine has been developed. It can
be applied for various operations such as drilling, surface
etching, engraving and micro finishing on the glass and
its composites. The multi characteristics optimization
The approach adopted by design of experiment through
the Taguchi orthogonal array is very popular for solving
9
model based on Taguchi method and Utility concept has
been employed to determine the optimal combination of
the machining parameters to attain the minimum surface
roughness and maximum MRR simultaneously. The
confirmation test is also conducted to verify the results.
The effect of air temperature (hot air) on MRR and
surface roughness is also discussed in this paper.
for grooving processes. The specimens were washed and
weighed before machining. The maskants were stuck on
the surface of specimen to protect the un machined part
of work material.
2. MATERIALS & METHODS
2.1 Experimentation
The schematic diagram of experimental set up is shown
in Figure1. It consist of portable Abrasive chamber,
Heating chamber with controller, Mixing head, Nozzle
and Three axes table with CNC controller. A part of air
flows through the abrasive chamber and the feeding of
abrasive particles take place due to the pressure
difference in the abrasive chamber and main flow.
Abrasive particles are mixed with air and then they enter
into the mixing head where the hot air is mixed with
abrasives and then passed through the nozzle as shown in
Figure 2. The abrasive hot jet is available at the tip of
nozzle striking on the target. The flow of abrasive is
controlled by a valve below the chamber. The nozzles are
usually made up of Sapphire / Tungsten carbide material
of hardness 50-60 HRC. As the Tungsten carbide
material is of high cost, in this research work, alloy steel
(EN38) heat treated of hardness 50 HRC was used for
nozzles. The temperature of air at the exit of nozzle is
measured using sensors (Thermocouples).
Figure 2 Abrasive hot air jet striking on surface of glass
plate
The material used for the maskants were steel or bronze
as they resist the high temperature of abrasive hot air jet.
After the test, the samples were cleaned with pressurized
air and final weight was measured using a digital
electronic balance (BSA224S-CW SARTORIOUS,
GERMANY) with resolution of 0.1mg. Four
measurements for each sample were taken and the
average value was the final reading. The weight loss per
unit time for each specimen is calculated and considered
as material removal rate. Similarly the roughness of
machined part was measured using a surface roughness
tester (Surf Test SJ201P, Mitutuyo, Japan). The average
Roughness value Ra of the machined part was recorded at
four different locations and the mean value was
considered as a roughness of surface.
Table 1 Process parameters and their levels
Parameter
A
B
C
Figure 1 Schematic of Abrasive hot air jet process
2.2 Materials
In the present work, Soda-lime glass was used as the
work material. The suitable size of specimen was used
10
SOD (mm)
Feed rate (mm/min)
Air temperature (oC)
Level
1
4
20
27
Level
2
8
30
200
Level
3
12
40
320
Table 2 Orthogonal array and Experimental Results with S/N Ratios
Factors
Sl.
No.
1
2
3
4
5
6
7
8
9
A
4
4
4
8
8
8
12
12
12
B
20
30
40
20
30
40
20
30
40
C
27
200
320
200
320
27
320
27
200
MRR
(g/min)
Ra (µm)
0.089
0.130
0.171
0.102
0.140
0.062
0.135
0.050
0.051
2.54
1.74
1.37
2.01
1.45
2.84
1.47
3.05
2.92
[
]
-21.012
-17.721
-15.340
-19.827
-17.077
-24.152
-17.393
-26.020
-25.848
-8.096
-4.810
-2.734
-6.063
-3.227
-9.066
-3.346
-9.685
-9.307
(
S/N ratio
Multiresponse
η (dB)
-14.554
-11.265
-9.037
-12.945
-10.152
-16.609
-10.369
-17.852
-17.577
)
[
( )
( )
(
)]
(3)
where U(x1, x2, x3 ... xn) is the overall utility of n process
response characteristics and Ui(xi) is utility of i th
response characteristic. Assignment of weights is based
on the requirements and priorities among the various
responses. Therefore the general form or weighted from
of Eq. (3) can be expressed as
(
where
The multi-response methodology based on Taguchi’s
robust design technique and Utility concept was used for
optimizing the multi-responses like MRR and Ra.
Taguchi’s standard S /N ratios were selected to obtain the
optimum parameters combination (Ross, 1996). They
were, Larger the better type S/N ratio for MRR and
Smaller the better type S/N ratio for Ra as calculated by
Eqs. (1) and (2) respectively.
]
S/N ratio
Ra
η1 (dB)
consumers (Kumar et al., 2010). The overall usefulness
of a process /product can be represented by a unified
index termed as utility which is the summation of the
individual utilities of various quality characteristics. It is
difficult to obtain the best combination of process
parameters, when there are multi-responses to be
optimized. The adoption of weights in the utility concept
helps in this difficult situations by differentiating the
relative importance of various responses. If xi represents
the measure of effectiveness of i th process response
characteristic and n represents number of responses,
then the overall utility function can be written as (Bunn,
1982)
2.3 Parameters and Design
In this process, a large number of variables are involved
and all these variables affect the machining results
directly or indirectly. For the purpose of present
investigation, only major and easy to control variables
like stand-off distance, feed rate and air temperature were
considered in this experiment. The other experimental
parameters were kept constant throughout the machining
as shown in Table 1. In order to obtain high efficiency in
the planning and analysis of experimental data, the
Taguchi parameter design was applied. Taguchi method
uses a statistical measure of performance called Signalto-Noise(S/N) ratio. The ratio depends on the quality
characteristics of the product/process to be optimized.
The standard S/N ratios generally used are as follows: Nominal-is-best, Smaller-the-better and Larger-theBetter. The optimal setting is the parameter combination,
which has the highest S/N ratio, (Taguchi, 1986;
Mahapatra and Chaturvedi, 2009). One of the important
steps involved in Taguchi’s technique is selection of
orthogonal array. An orthogonal array is a small set from
all possibilities which helps to determine least number of
experiments. Which will further help to determine the
optimum level for each process parameters and establish
the relative importance of individual process parameters.
In this work, the orthogonal array L9 was selected.
[
S/N
ratio
MRR
η1 (dB)
)
∑
( )
(4)
∑
where Wi is the weight assigned to the i th response
characteristic. The utility concept employs the weighing
factors to each of S/N ratio of the responses to obtain a
multi response S/N ratio for each trial of the orthogonal
array. The multi-response S/N ratio is calculated by the
equation.
(5)
where w1 and w2 are the weighing factors associated with
the S/N ratio of MRR and Ra respectively. These
weighing factors were decided based on the priorities
among the various responses to be simultaneously
optimized. In the present work, weighing factors of 0.5
for MRR and 0.5 for Ra are assumed. This gives
priorities to all responses for simultaneous minimization
and maximization. The overall mean of η associated with
k number of trials is computed as;
(1)
(2)
2.4 Utility Concept
Utility can be defined as the usefulness of a product or a
process in reference to the levels of expectations to the
11
∑
(6)
3. RESULTS AND DISSCUSSION
3.1 Analysis of single response
Experiments were conducted on soda lime glass plate to
study the performance of grooving process using
orthogonal array L9. The values of Single-response S/N
ratios MRR (η1) and Ra (η2) are calculated using Eqs.
(1) and (2) respectively. The combined multi-response
S/N ratio is calculated using Eq. (5) shown in Table 2.
The individual mean values of S/N ratios of responses of
MRR (η1) and surface roughness Ra (η2) are shown in
Table 3 and Table 5 respectively. It can be found that the
optimal combination A1B1C3 is largest value of S/N
ratios of MRR and Ra respectively. Therefore A1B1C3 is
the optimal combination of both responses MRR and Ra.
The main effect plots (Figure 3 and Figure 4) shows that
the optimum condition for MRR and Ra are at level 1 (4
mm) of SOD, level 1 (20 mm/min) of Feed rate and
level 3 (320oC) of air temperature.
Figure 3 Main effects plot based on the S/N ratio of
MRR (η1)
Table 3 Means of S/N ratio values of MRR
Parameter
A
B
C
SOD
(mm)
Feed rate
(mm/min)
Air temperature
(oC)
Level 1
Level 2
Level 3
-18.024
-20.352
-23.087
-19.410
-20.272
-21.780
-23.728
-21.132
-16.603
Figure 4 Main effects plot based on the S/N ratio of Ra
(η2)
Table 4 Results of ANOVA for MRR
Source
D
F
Seq SS
Adj SS
Adj
MS
A
2
38.529
38.529
19.264
B
2
8.629
8.629
4.314
C
2
78.009
78.009
39.004
Error
Total
2
8
3.675
128.841
3.675
1.837
F
10.4
7
2.35
21.2
3
Contri
bution
P (%)
29.90
6.69
Table 6 Results of ANOVA for Ra
60.54
2.852
Table 5 Means of S/N ratio values of Ra
Parameter
A
B
C
SOD (mm)
Feed rate (mm/min)
Air temperature (oC)
Level
1
-5.213
-5.835
-8.949
Level
2
-6.135
-5.907
-6.726
It can be seen that air temperature has the highest
contribution of about 60.54% for MRR and 80.99% for
Ra, the other parameters have less contributions. It is
clear that the air temperature is one of the significant
factors that has more impact than any other factors on
MRR and Ra.
Level
3
-7.446
-7.035
-3.102
Sourc
e
D
Seq SS
F
A
B
C
Error
Total
2
2
2
2
8
7.566
2.720
52.258
1.980
64.524
Adj SS
Adj
MS
7.566
2.720
52.258
1.980
3.783
1.360
26.129
0.990
F
3.82
1.37
26.40
Contr
ibutio
n
P (%)
11.72
4.21
80.99
3.06
3.2 Optimal parameter combination of Multiresponse
The optimal combination of process parameters for
simultaneous optimization of MRR and Ra is obtained
by the mean values of the multi-response S/N ratio of the
overall utility value as shown in Table 7.
The statistical software with an analytical tool of
ANOVA is used to determine which parameter
significantly affects the performance characteristics. The
results of ANOVA for the Single-response S/N ratios of
MRR (η1) and surface roughness Ra (η2) are shown in
Table 4 and Table 6.
The larger value of the multi-response S/N ratio means
the comparable sequence exhibiting a stronger
12
correlation with the reference sequence. Based on this
study, the combination A1B1C3 shows the largest value of
the multi-response S/N ratio for the factors A, B, and C
respectively. Therefore, A1B1C3 is the optimal parameter
combination of the Abrasive hot air jet machining for
glass.
The A1 B1 C3 is an optimal parameter combination of the
Abrasive hot air jet machining of single as well as
multiple responses. Therefore, the combination A1B1C3 is
treated as the confirmation test. The predicted optimal
value of response can be calculated using the equation.
∑
( (
)
(7)
Table 7 Means of multi-response S/N Ratio
Parameter
A SOD (mm)
B Feed rate (mm/min)
C Air temperature (oC)
Level 1
-11.618
-12.622
-16.338
Level 2
-13.235
-13.089
-13.935
where m is the total mean of the response S/N ratio at
the optimal level and mi is the S/N ratio at optimal
parameter. The predicted optimal values for singleresponse and multi-responses are listed in Table 9.
Level 3
-15.266
-14.407
-9.852
In order to validate, the experiment (four trials) is
conducted according to the optimal parameters levels
(A1B1C3) and the corresponding values of performance
measures are taken. Table 9 shows the predicted multiresponse S/N ratio and multi-response S/N ratio obtained
from the experiment. It may be noted that there is good
agreement between the estimated value (-7.346) and the
experimental value (-8.216). Therefore, the condition A1
B1 C3 of the parameter combination of the Abrasive hot
air jet machining process is treated as optimal. The
optimal combination A1 B1 C3 (4 mm, 20 mm/min and
320o C) is also confirmed by ANOVA. It can be found
that the hot air is influencing on MRR and Ra of Abrasive
Hot Air Jet Machining on glass.
Table 8 ANOVA for the multi- response S/N ratio
Source
D
F
Seq SS
Adj SS
Adj
MS
A
B
C
Error
Total
2
2
2
2
8
20.040
5.141
64.485
2.761
92.428
20.040
5.141
64.485
2.761
10.020
2.571
32.243
1.381
F
7.26
1.86
23.35
Contri
bution
P (%)
21.68
5.56
69.76
2.98
The results of ANOVA for the Multi-response S/N ratios
as shown in Table 8. On the examining of the percentage
of contribution (P%) of the different factors, it can be
seen that air temperature has the highest contribution of
about 69.76% and the other parameters have less
contributions. It is clear that the air temperature is one of
the significant factors that have more impact than any
other factors. The main effect plots (Figure 5) shows that
the optimum condition for Multiple response is at level 1
(4 mm) of SOD, level 1 (20 mm/min) of Feed rate and
level 3 (320oC) of air temperature.
4. INFLUENCE OF AIR TEMPERATURE ON MRR
and Ra
The effect of air temperature on MRR and Ra was studied
for grooving process using silicon carbide (SiC) of size
100µm as carrier media. The results are plotted as shown
in Figure 6 and Figure 7. It can be noticed from Figure 6
that the air temperature has more significant effect on
MRR at air temperature above 100oC. It can be found
that MRR at higher temperature is more than that at low
(room) temperature. From Figure 7, it can be found that
the roughness of machined surface decreases as the
temperature of the air is increased. It is further observed
that the value of surface roughness is very less at higher
temperature and is more of at room temperature.
3.3 Confirmation test
After identifying the most influential parameters, the
final phase is to verify the experimental results (MRR
and Ra) by conducting the confirmation test.
Figure 5 Main effects plot based on the multi- response
S/N ratio
Figure 6 Effect of temperature on Material removal rate
for grooving
13
Table 9 Results of Confirmation Test
Single response
optimization
Multi response
optimization
Performance
Characteristic
Optimal
setting
Predicted Optimal
S/N ratio
Experimental Optimal
S/N ratio
MRR
A1B1C3
-13.063
-14.154
Ra
A1B1C3
-1.632
-2.278
MRR and Ra
A1B1C3
-7.346
-8.216
As hot air is supplied on the target, the temperature of
target is increased resulting in increasing the size of
radial crack initiated by impact of abrasive material. It
helps in removal of larger size of chips from the work
material as indicated by an arrow mark in Figure 9.
In agreement with this, our study reveals that the MRR of
material at high temperature is more as compared to that
of at low temperature. The removal of material in the
form of larger cracks creates a new smooth bottom of
target and thus the roughness of the machined surface in
reduced. The morphology of eroded surface indicates that
at low temperature, there is an evidence of crack
initiation taking place by brittle nature. It can be
observed from Figure 9 that at high temperature, deep
chipping of material takes place due to more plastic
deformation. Hence,erosion rate increases and thus the
hot air has its influence in increasing MRR and reducing
roughness of machined surface.
Larger and deep chips
Figure 7 Effect of temperature on Roughness for
grooving
Brittle initiation of crack
Figure 9 Micrograph of machined surface at high
temperature (3200C)
5. CONCLUSION
The optimization of process parameters of Abrasive hot
air jet machining using Taguchi orthogonal array with
multi-response analysis is discussed in this paper. From
the experimental investigation and analysis, the
following conclusions can be drawn

Figure 8 Micrograph of machined surface at room
temperature (270C)
14
It has been found that the combination A1 B1C3
show the largest value of the Multi-response S/N
ratio for the factors A, B, and C, respectively.
Therefore, A1 B1C3 is the optimal parameter
combination (Stand-off distance of 4 mm, the Feed




rate of 20 mm/min and the Air temperature of 320 oC)
of the Abrasive hot air jet machining for glass.
Through ANOVA, the percentage of contribution to
the Air temperature is more as compared to other
parameters. Hence, the air temperature is the most
significant factor for the Abrasive Hot air jet
machining for the minimization of the roughness of
machined surface and maximization of MRR.
It can be found that there is good agreement between
the estimated value (-7.346) and the experimental
value (-8.216). Therefore, the condition A1 B1 C3 of
the parameter combination of the Abrasive hot air jet
machining process was treated as optimal.
From the experimental results, it has been found that
the air temperature has the greatest impact on MRR
and Ra of grooved surface.
It can be observed from micrographs that at high
temperatures, there is sufficient evidence of more
plastic deformation accompanied by brittle fracture
failure which results in increase of MRR and
reduction Ra.
Concept in solving the Multi- objective Problem
When Turning AISI 202Austenitic Stainless Steel,
Journal of Engineering Science and Technology
Review 4 (1): 55-61.
Kumar, P., Barua, P.B. and Gaindhar, J.L. 2010. Quality
Optimization
(Multi-characteristics)
Through
Taguchi’s Technique and Utility Concept, Quality
and Reliability Engineering International 16: 475485.
Mahapatra, S.S. and Chaturvedi, V. 2009. Modeling and
analysis of Abrasive wear performance of composites
using Taguchi approach, International Journal of
Engineering, Science and Technology 1: 123-135
Muralidhar, S., Pal, S., Mittal, R. and Kale, S.R. 1982. A
Study of
thermal cutting of glass, Journal of
American Ceramic Society 82 (8): 2166-2176.
Ng, C.F., Kamaruddin, S., Siddiquee, A.N., Khan, Z.A.
2011. Experimental investigation on the recycled
hdpe and optimization of injection moulding process
parameters via Taguchi method, International Journal
of Mechanical and Materials Engineering 6 (1): 8191.
Nor, N.H.M., Muhamad, N., Ibrahima, M.H.I., Ruzia,
and Jamaludin, K.R. 2011. Optimization of Injection
molding parameter of Ti-6Al-4V powder mix with
palm stearin and polyethylene for the highest Green
strength by using Taguchi method, International
Journal of Mechanical and Materials Engineering 6
(1): 126-132.
Prakash, E.S. Sadashivappa, K. and Singaperumal, M.
2001. Non Conventional cutting of Plateglass using
hot air jet: Experimental studies, Mechatronics 11:
595-615.
Ross, P.J. 1996. Taguchi Techniques for Quality
Engineering, McGraw-Hill Book Company,NewYork
Sathia, P. and Jaleel, M.Y.A. 2010. Gray based Taguchi
method for Optimization of Bead Geometry in Laser
Bead-on-Plate Welding, Advances in Production
Engineering and Management 4: 225-234.
Taguchi, G. 1986. Introduction to quality engineering,
Proceedings of Asian Productivity Organization,
UNIPUB, White Plains, NewYork
Tasirin, S.M., Kamarudin, S.K., Ghani, J.A. and Lee,
K.F. 2007. Optimization of drying parameters of
bird’s eye chili in a fluidized bed dryer, Journal of
Food Engineering 80: 695–700
Vijian, P. and Arunachalam, V.P. 2006. Optimization of
squeeze cast parameters of LM6aluminium alloy for
surface roughness using Taguchi method, Journal of
Material Processing Technology 180: 161-166.
Zhang, J.Z., Chen, J.C. and Kirby, E.D. 2007. Surface
roughness optimization in a end– milling operation
using the Taguchi design method, Journal of Material
Processing Technology 184: 233-239.
Zhang, L., Kuriyagawa, T., Yasutomi, Y. and Zhao, J.
2005. Investigation into micro Abrasive intermittent
jet machining, International Journal of Machine Tools
& Manufacture 45: 873–879.
ACKNOWLEDGEMENTS
The authors would like to thank the Visvesvaraya
Technological University (VTU), Karnataka, India for
providing the financial support to carried out this
research work under VTU research grant scheme.
REFERENCES
Balasubramaniam, R., Krishnan, J. and Ramakrishnan N.
1999. An Experimental Study on the Abrasive Jet
Deburring of Cross-Drilled Holes, Journal of Material
Processing Technology 91: 178-182.
Bunn, D.W. 1982. Analysis for optimal decisions, John
Wiley & Sons, New York.
Bushroa, A.R., Masjuki, H.H. and Muhamad, M.R. 2011.
Parameter optimization of sputtered Ti interlayer
using Taguchi method, International Journal of
Mechanical and Materials Engineering 6 (2): 140146.
Chen, D.C. and Chen, C.F. 2007. Use of Taguchi
method to study a robust design for the sectioned
beams curvature during rolling, Journal of Materials
Processing Technology 190: 130-137.
Das, S.K. and Saahoo, P. 2011. Tribological
characteristics of electro less Ni–B coating and
optimization of coating parameters using Taguchi
based grey relational analysis, Materials and Design
32: 2228–2238.
El-Domiaty, A., Abid El-, H.M. and Shaker, M.A. 2009.
Drilling of Glass Sheets by Abrasive Jet Machining,
World Academy of Science Engineering and
Technology 56: 61-67.
Ghani, J.A., Choudhury, I.A. and Hassan, H.H. 2004.
Application of Taguchi method in the optimization of
end milling parameters, Journal of Materials
Processing Technology 145: 84–92.
Kaladhar, M., Subbaiah, K.V., Rao, C.S. and Rao, K.N.
2011. Application of Taguchi approach and Utility
15