Optimization of surface roughness in high speed end milling operation using

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
249
OPTIMIZATION OF SURFACE ROUGHNESS IN HIGH-SPEED END
MILLING OPERATION USING TAGUCHI’S METHOD
S. Madhava Reddy
Associate Professor, Dept. of Mechanical Engg.,
Mahatma Gandhi Institute of Technology, Hyderabad- 500 075, A.P, India.
ABSTRACT
This paper presents the optimization of high speed milling process parameters with respect to
the surface roughness of samples and the study of the Taguchi design application to surface quality
in a CNC end milling operation. The Taguchi design is an efficient and effective experimental
method in which a response variable can be optimized given various control factors using fewer
resources than a factorial design. An orthogonal array of L9 was used and ANOVA analyses were
carried out to identify the significant factors affecting surface roughness. In the present investigation,
the straight flute end-milling cutter was used for machining of Al-Si-Mg-Fe alloy work pieces. The
surface roughness depends upon the cutting speed, feed rate and depth of cuts. The influence of high-
speed end milling on surface roughness in Al-Si-Mg-Fe alloy work pieces, which were cast by the
sand, investment and die casting method, was investigated. The results concluded that the surface
roughness decreases with increase in the cutting speed, increases with increase in feed rate and depth
of cut. Confirmation test with optimal levels of machining parameters were carried out in order to
illustrate the effectives of Taguchi’s optimization method.
Key words: Optimization; Surface roughness; Milling operations; taguchi method
1. INTRODUCTION
One of the most common metal removal operations used in industry is the end milling
process. It is common manufacturing process widely used in a variety of sectors, such as the
aerospace, die, machinery design and automobile as well as manufacturing industries. In order to
ensure the machining quality and to reduce cost and time, it is indispensable to develop research on
milling process including the prediction of cutting forces, form errors and surface quality. The
machined surface topography and the geometric shape and texture of the machined surface is
essential because the latter directly affects the surface quality [1]. Surface roughness is an important
measure of the technological quality of a product and a factor that greatly influences manufacturing
cost. The mechanism behind the formation of surface roughness is very dynamic, complicated and
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 4, July - August (2013), pp. 249-258
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250
process dependent, it is very difficult to calculate its value through theoretical analysis [2]. The
dynamic nature and widespread usage of milling operations in practice have raised a need for
seeking a systematic approach that can help to set-up milling operations in a timely manner and also
to help achieve the desired surface roughness quality [3]. Under periodically varying milling forces,
the cutting tool experiences both static and dynamic deformations, which are passed as dimensional,
and surface finish errors to the product. The conditions that affect the surface finish are feed, speed,
material, and tool geometry.
Traditional experimental design procedures are too complicated and not easy to use. A large
number of experimental works have to be carried out when the number of process parameters
increases. To solve this problem, the Taguchi method uses a special design of orthogonal arrays to
study the entire parameter space with only a small number of experiments. Taguchi is the developer
of the Taguchi method. Taguchi methods have been widely utilized in engineering analysis and
consist of a plan of experiments with the objective of acquiring data in a controlled way, in order to
obtain information about the behavior of a given process. The greatest advantage of this method is
the saving of effort in conducting experiments; saving experimental time, reducing the cost, and
discovering significant factors quickly.
Taguchi’s robust design method is a powerful tool for the design of a high quality system. He
considered three steps in a process and product development; system design, parameter design, and
tolerance design. In system design, the engineer uses scientific and engineering principles to
determine the fundamental configuration. In the parameter design step, specific values for the system
parameters are determined. Tolerance design is used to determine the best tolerance for the
parameters. In addition to the S/N ratio a statistical analysis of variance (ANOVA) can be employed
to indicate the impact of process parameters on surface finish values. In this way the optimal levels
of process parameters can be estimated.. The analysis results of related subjects discussed above are
given in the following sections. The steps applied for Taguchi optimization in this study are
presented in fig.1 [4].
Fig.1. Steps applied in Taguchi’s optimization design procedure
Determine suitable working levels of the design
factors
Select proper Orthogonal Aray(OA)
Run Experiments
Analyze Data
Identify Optimum Condition
Confirmation runs
Determine the results of parameter design by
tightening the tolerance of the significant
factors
System Design
Parameter Design
Tolerance Design

251
In the present work, the influence of high-speed end milling on surface roughness in Al-Si-
Mg-Fe alloy work pieces, which were cast by the sand, investment and die casting method, was
investigated. The end mills have cutting teeth on the end as well as on the periphery of the cutter. In
the present investigation, the straight flute end-milling cutter was used for machining of Al-Si-Mg-Fe
alloy work pieces. The effects of high speed milling of cast Al-Si-Mg-Fe alloys were investigated in
terms of surface morphology of work piece. The surface morphology was investigated through the
measurement of surface roughness and scanning electron microscopy (SEM).
2. EXPERIMENTAL SET-UP
Straight flute end milling cutters are generally used for milling either soft or tough materials.
In order to develop monitoring functions, displacement sensors are installed on the spindle unit of a
high precision machining center. The machine used in the study is a vertical type-machining center
(figure 2).
Fig.2. High speed vertical milling center
Fig.3. The dimensions of workpiece used high-speed milling
The PCD end-milling cutter having four straight flutes was used in this investigation. High-
pressure coolant jet was employed for cooling and lubrication of the high-speed machining
operations. The spindle has constant position preloaded bearings with oil-air lubrication, and the
maximum rotational speed is 20,000 rpm. The dimensions of the work piece used in the high-speed
milling are shown in figure 3. The temperature of the work piece material was measured using
thermocouple.

252
3. DESIGN OF EXPERIMENTS
The Al-Si-Mg-Fe alloy was prepared and chemical analysis of their ingredients was done.
The chemical composition of alloy is given in Table 1. The sand mould, investment shell, and cast
iron mould were employed to prepare the samples for high-speed end milling.
Table-1: Chemical composition of alloys
Alloy Composition determined spectrographically, %
Element Al Si Mg Fe Cu Mn Cr
% 85.22 9.0 2.0 3.5 0.01 0.25 0.02
3.1Selection of the quality characteristics
The selection of quality characteristics to measure as experimental outputs greatly influences
the number of tests that will have to be done statistically meaningful. The quality characteristics,
which was selected to influence the high-speed end milling of Al-Si-Mg-Fe alloy, is the surface
roughness.
3.2 Selection of machining parameters
This is the most important phase of investigation. If important parameters are unknowingly
left out of the experiment, then the information gained from the experiment will not be in a positive
sense. The parameters, which influence the performance of the high-speed end milling, are:
• Microstructure of Al-Si-Mg-Fe alloy
• Cutting speed
• Feed rate
• Depth of cut
• Coolant
Since the high-speed milling involves high cost of machining, the process parameters were
optimized using Taguchi's method. Taguchi techniques offer potential savings in test time and money
by more efficient testing strategies. Not only are savings in test time and cost available but also a
more fully developed product or process will emerge with the use of better experimental strategies.
Table-2 Control parameters and levels
Parameter Symbol Level – 1 Level – 2 Level-3
Casting c Sand casting Investment casting Die casting
Cutting speed, m/min n 600 1200 1800
Feed rate, mm/min f 1000 3000 5000
Depth of cut, mm d 0.2 0.4 0.6
Control parameters are those parameters that a manufacturer can control the design of high-
speed end milling. The levels chosen for the control parameters were in the operational range of the
high-speed end milling. Trial runs were conducted by choosing one of the machining parameters and
keeping the rest of them at constant values. The selected levels for the chosen control parameters are
summarized in Table – 2. Each of the four control parameters was studied at three levels.

253
3.3 Assignment of control parameters
The orthogonal array, L9 was selected for the high-speed end milling. The parameters were
assigned to the various columns of orthogonal array (OA). The assignment of parameters along with
the OA matrix is given in Table–3.
Table-3 Orthogonal Array (L9) and control parameters
Treat No. n f d c
1 1 1 1 1
2 1 2 2 2
3 1 3 3 3
4 2 1 2 3
5 2 2 3 1
6 2 3 1 2
7 3 1 3 2
8 3 2 1 3
9 3 3 2 1
3.4 Analytical Techniques
After all tests were conducted, the decisions were made with the assistance of the following
analytical techniques:
• Analysis of variance: ANOVA is a statistically based, objective decision-making tool for
detecting any differences in average performance of groups of items tested. The decision
takes variation into account. The parameter which has Fisher’s ratio (F-ratio) larger than the
criterion (F- ratio from the tables) are believed to influence the average value for the
population, and parameters which has an F- ratio less than the criterion are believed to have
no effect on the average. Percent contribution indicates the relative power of a parameter to
reduce variation.
• Plotting method: To plot the effect of influential factors, the average result for each level
must be calculated first. The sum of the data associated with each level in the OA column
divided by the number of tests for that level would provide the appropriate averages. Plots
may be made with equal increments between levels on the horizontal axes of the graphs to
show the relative strengths of the factors. The strength of a factor is directly proportional to
the slope of the graph.
3.5 Measurement of surface roughness
The surface roughness was measured using profile graph as shown in figure 4 . The
rectangular machined specimen was placed on a support. The stylus was then adjusted till the stylus
probe touched the machined specimen and a zero reading was obtained on the digital monitor. The
stylus was then moved parallel the surface of the machined specimen for a sampling length of 5mm.
The readings were noted down on the digital monitor. The same procedure was repeated at five
different locations on the surface of machined specimen and the mean Ra value was calculated.

254
Fig.4. Measurement of surface roughness
3.6 Scanning Electron Microscopy (SEM)
The surfaces of the high-speed machined test samples were examined in scanning electron
microscope (SEM) to determine the surface morphology. The samples for SEM observation were
obtained from the machined specimens by sectioning parallel to the machined surface and the
scanning was carried in IICT (Indian Institute of Chemical Technology) S-3000N Toshiba shows
Scanning Electron Microscope Figure 5.
Fig.5. S-3000N Toshiba Scanning Electron Microscope
4. RESULTS AND DISCUSSION
The experiments were conducted randomly and repeated twice. The surface roughness (Ra)
values under various combinations of machining variables are given in Table 4. The summary of
ANOVA (analysis of variance) for surface roughness is shown in Table 5. According to the analysis
of variance, all the process variables have significant influence on the variation of surface roughness.
The percent contribution indicates that the variable d (depth of cut) all by itself contributes the most
toward the variation observed in the surface roughness (Ra values): almost 73.45%. The variable f
(feed rate) contributes over 13.42% of the total variation observed. The variables n (cutting speed)
and c (type of casting) contribute 7.02% and 6.09% respectively to the total variation in surface
roughness.

255
Table-4: Experimental results of surface roughness
Treat No. Surface roughness (Ra), microns
Trial-1 Trial-2
1 3.76 3.72
2 4.48 4.34
3 5.52 5.56
4 3.69 3.75
5 5.19 5.24
6 3.82 3.91
7 4.56 4.67
8 3.04 2.89
9 4.68 4.61
Table-5: ANOVA summary of surface roughness
Column NoSource Sum 1 Sum 2 Sum 3 SS v V F P
1 n 124.944109.227 99.634 0.726433 2 0.36322 85.4628 7.02
2 f 97.204 105.672131.6021.399433 2 0.69972 164.639 13.42
3 d 74.483 108.800157.491 7.6969 2 3.8485 905.518 73.45
4 c 123.307110.768 99.634 0.630433 2 0.31522 74.1686 6.09
5 e ---- ---- --- 0.03825 9 0.00425 --------- ---------
6 T ---- ---- ---- 10.49145 17 -------- --------- ---------
0
1
2
3
4
5
6
0 400 800 1200 1600 2000
R
a
,
x
1
0
-6
m
Speed, m/min
Fig.6: Effect of cutting speed on the surface roughness
The effect of cutting speed on the surface roughness is shown in Figure 6. The surface
roughness decreases with increase in the cutting speed. Indeed, low speeds are used for rough cutting
and high speeds are employed for fine finishing in the machine shop.
The effect of feed rate on the surface roughness is illustrated in Figure 7. It can be understood
that the surface roughness increases with increase in feed rate. The cutting with a tool having a
certain wear generates surface roughness than a fresh tool, because the tool wear is proportional to
the cutting feed rate and roughness is a reproduction of the tool nose profile on the workpiece
surface.

256
Figure 7: Effect of feed rate on the surface roughness
The variation of surface roughness of the workpiece on account of depth of cut is shown in
Figure 8. It is clearly seen that the roughness increases with increase in depth of cut. Depth of cut
plays major role on the roughness of the workpiece surface. The rigidity of the cutting and machine
is largely depending upon the depth of cut. The rigidity decreases with increase in depth of cut [3].
The amount of heat generation increases with increase in depth of cut, because the cutting tool has to
remove large volume of material from the workpiece. The plastic deformation of the workpiece is
proportional to the amount of heat generation in the workpiece. Large amount of plastic deformation
in the workpiece promotes roughness on the workpiece surface.
Fig.8. Effect of depth of cut on the surface roughness
Fig.9 Effect of casting condition on the surface roughness
The variation of surface roughness with casting condition is shown in Figure 9. The sand cast
specimens promote greater roughness than die cast specimens. This is due to fact that the sand
specimens have coarse grain structure, whereas the die cast specimens have fine grain structure. The
surface roughness of investment cast specimens is intermediate to the sand and die cast specimens.

257
In the SEM micrography Figure 10(a), the white lines parallel to the milling direction are
caused by the tearing of material. The tearing of material is observed with deeper depth of cuts. In
Figure 10(b) the white lines caused by tearing are less evident and the spreading of material is
observed. The spreading of material is resulted with lower depth of cuts.
Fig.10: Surface morphology after high-speed milling (SEM)
5. OPTIMIZATION OF HIGH SPEED MILLING PROCESS PARAMETERS
The high speed machining of Al-Si-Mg-Fe alloy depends upon the cutting speed, depth of
cut, feed rate, and casting condition. As the cutting speed increases the surface roughness decreases
(in turn surface finish increases). The surface roughness decreases with decrease in depth of cut and
feed rate. The investment casting process (also known as precision casting process) gives very high
surface finish as compared to the sand casting process. But, the investment casting process is not
economical as compared to the die casting process.
The significant process parameters, which affect the surface roughness of Al-Si-Mg-Fe alloy,
are, therefore, cutting speed (N3), depth of cut (D1), feed rate (F1), and casting condition (C3).
The estimated mean of Surface roughness, microns = N3 + D1 + F1 + C3 – 3T
= 4.075 + 4.025 + 3.523 + 4.075 –3 x 4.302 = 2.792
where ,
T = grand mean
= 4.302 microns
The confidence interval, CI =
;2; ev e
eff
F V
n
α
where,
eα;2;vF = Fisher’s ratio
α = risk
ve = degrees of freedom for error
Ve = error variance
eff
N
n
1+ total degrees of freedom associated with items in the estimate of mean
=
eff
18
n 3.6
1+ 4
= =
For 90% confidence level, the Fisher’s ratio is
10; 2;9F = 3.01
The predicted range of surface roughness is
2.732 surface roughness 2.852< <

258
The process parameters for high-speed milling with their optimum levels are given in
Table-6.
Table-6: Process parameters with optimum levels
Parameter Level
Cutting speed, m/min 1800
Depth of cut, mm 0.2
Feed rate, mm/min 1000
Casting condition Die casting
4. CONCLUSIONS
The surface roughness depends upon the cutting speed, depth of cuts and feed rates. The
surface roughness decreases with an increase in the cutting speed and, increases with increase in feed
rate and depth of cut. The rigidity of the cutting and machine is largely depending upon the depth of
cut.
5. REFERENCES
[1] Tong Gao, Weihong Zhang, Kepeng Qiu and Min Wan, “Numerical simulation of machined
surface topography and roughness in milling process”, Journal of Manufacturing Science
and Engineering, Transaction of the ASME, Vol.128, Feb. 2006, pp.96-103.
[2] Y.H Tsai, J.C. Chen, S.J Lou, In-process surface recognition system based on neural
networks in end milling cutting operations, Int.. Mach. Tool Manufacturing, 39(4), 1999,
pp. 583-605.
[3] Julie Z. Zhang, Joseph C.Chen, and E. Daniel Kirby, Surface roughness optimization in an
end milling operation using the Taguchi design method, Journal of Materials Processing
Technology, 184, 2007, pp.233-239.
[4] Mustafa Kurt, Eyup Bagci and Yusuf Kaynak, Application of Taguchi methods in the
optimization of cutting parameters for surface finish and hole diameter accuracy in dry
drilling processes.
[5] K. M. Vernaza Pena, J. J. Mason and M. Li, High speed temperature measurements in
orthogonal cutting of aluminum, Experimental Mechanics, Vol.42, pp.221-229, 2003.
[6] Chennakesava Reddy.A and Shamraj V.M., Reduction of cracks in the cylinder liners
choosing right process variables by Taguchi method, Foundry, 1998, 10(4), 47-50.
[7] Chennakesava Reddy A., A method to evaluate surface roughness of as-castings, Indian
Foundry Journal, 1996, 42(12), 28-30.
[8] Ezugwu E. O., High speed machining of aero-engine alloys, Journal of the Brazilian Society
of Mechanical Sciences & Engineering, 2004, 26(1), 1-10.
[9] Taguchi G., Introduction to quality engineering, Asian Productivity Organization, 1986.
[10] U. D. Gulhane, M.P.Bhagwat, M.S.Chavan, S.A.Dhatkar and S.U.Mayekar, “Investigating
the Effect of Machining Parameters on Surface Roughness of 6061 Aluminium Alloy in End
Milling”, International Journal of Mechanical Engineering & Technology (IJMET), Volume
4, Issue 2, 2013, pp. 134 - 140, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
[11] Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning Parameters
Optimization for Surface Roughness by Taguchi Method”, International Journal of
Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211,
ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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