Optimization of Machining Parameters Affecting Surface Roughness of Al6082 in Dry End Milling Operation on VMC

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 82
OPTIMIZATION OF MACHINING PARAMETERS AFFECTING SURFACE
ROUGHNESS OF Al6082 IN DRY END MILLING OPERATION ON VMC
Akhilesh Chaudhary1, Vinit Saluja2
1Research scholar, Dept. of Mechanical Engineering, Geeta Engineering College, Panipat, UP, India
2Assistant Professor, Dept. of Mechanical Engineering, Geeta Engineering College, Panipat, UP, India
------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract - Every manufacturing industry wants to
reduce the manufacturing cost and increase the quality of
a product. The aim of this project work is to optimize the
selected control factors in order to reduce surface
roughness in dry end milling operation. In this project
Taguchi method is used to determine the optimum
machining parameters more efficiently. The cutting
parameters are spindle speed, feed rate and depth of cut.
These parameters are investigated at three different
levels. On the basis of L27 orthogonal array of Taguchi
method 27 experiments were performed. The machining
operation performed on vertical machining centre without
using coolant. The surface roughness was measured by the
device Surface Roughness Tester. These experimental data
were analyzed using Minitab software to identify the most
significant factor. The material used in this project is
Al6082 Aluminium alloy.
Keywords: Surface Roughness, Taguchi, ANOVA, Minitab,
End mill.
1. INTRODUCTION
It is impossible to produce perfectly smooth surface by
machining or other process. It means that by machining
process some roughness is always present on the
surface, which can varies according to changes the
machining parameter. Surface roughness is very
important factor which affect the surface quality of the
product.
The surface roughness is the finer irregularities (peaks
and valleys) of the surface. Surface roughness is a
component of surface texture. It is describe by the
deviations in the direction of the normal vector of a real
surface. These deviations are depends on the surface
quality. The surface is rough if these deviations are large
and surface is smooth if these deviations are small.
2. LITERATURE REVIEW
The many researchers have research on material Al6082,
but very few researchers has research work on End
milling operation on Al6082. In this present work we are
selected the material Al6082. The objective of present
work is to optimize the cutting parameter of Al6082 in
dry end milling operation.
Shetty et al. [1] (2008) were conducted an experiment
and used Taguchi and Response Surface Methodologies
for minimizing the surface roughness in turning of
discontinuously reinforced aluminium composites
(DRACs) having aluminium alloy 6061 as the matrix and
containing 15 vol.% of silicon carbide particles of mean
diameter 25 lm under pressured steam jet approach.
They found the optimum condition for minimum surface
roughness.
Muammer Nalbant et al. [2] (2009) were conducted an
experiment and seen the effects of machining on AISI
1030 steel uncoated, PVD- and CVD-coated cemented
carbide insert variation in feed rates, cutting speeds
while keeping depth of cuts constant. This process was
done without using cooling liquids. The result shows that
a negative relationship between the average surface
roughness and cutting speed for this tool. The surface
roughness was decreased by increasing cutting speed.
Yallese et al. [3] (2010) were conducted the
experiment for cutting forces in dry turning operation of
AISI 52100 hot work-piece steel (50 HRC). They
performed 27 experiments and specified effects of
parameters like cutting speed, feed rate and depth of cut.
They found that the most important factor affecting the
components of the cutting forces was depth of cut.
Sahoo [4] (2011) was conducted an experimental study
to investigate the effects of cutting speed, feed rate and
depth of cut for minimum surface roughness for AISI
1040 steel when the material were machined on a CNC
turning machine (lathe). ANOVA software was use to
analysed the result for purpose of optimising the
parameter.
Yansong Guo et al.[5] (2012) were conducted an
experiments and seen the effects of an approach which
incorporates both energy consumption and surface
roughness for optimizing the cutting parameters in finish
turning. This was based on a surface roughness model,
they were optimized the cutting parameters for surface

finish with minimum energy consumption. The analysis
were done in two-step approach in determining the
optimal cutting parameters for finishing turning
operations for minimum energy consumption, by
specified a surface finish.
Kamal Hassan et al.[6] (2012) were conducted an
experiment and seen the effects of process parameters
on Material Removal Rate (MRR) in turning operation of
material C34000. The Cutting speed, feed rate and depth
of cut were taken cutting parameters in this experiment
and result were analysed ANOVA in MINITAB 16 @
software. The cutting speed and feed rate were most
dominated factor for MRR. The conclusion shows that
the MRR rate was increased by increasing the cutting
speed and feed rate.
Upinder Kumar Yadav et al. [7] (2012) were
conducted an experiment and optimized machining
parameters for surface roughness in CNC turning by
Taguchi method. The result shows that the feed rate and
cutting speed were most dominant factor for minimum
surface roughness and the surface roughness was
increased with increasing the feed rate & decreasing the
cutting speed.
Ilhan Asiltürk et al. [8] (2012) were conducted an
experiment and optimized machining parameters for
surface roughness in turning operation. Taguchi method
was used to found the optimal turning process
parameters. The result was analysed by both Taguchi
and RSM method and it shows that the feed rate was the
most significant factor for surface roughness.
Najiha M.S. et al. [9] (2013) were conducted an
experiment to determine the optimum cutting
parameters for the end milling process of Aluminium
alloy 6061T6 under wet cooling conditions. The
response surface methodology was used for optimizing
the surface roughness. The result shows that the feed
rate and the depth of cut was the most significant factors
for surface roughness.
M Saravana Kumar et al. [10] (2014) were conducted
an experiment to determine optimum micro milling
parameters of AL-6082 by using DOE concept. The
cutting speed, feed rate and depth of cur were chosen the
process parameter. The result shows that MRR was
decreased with decreasing the tool diameter, spindle
speed and feed rate.
Nisha Tamta et al. [11] (2015) were conducted an
experiment for optimum drilling machining process for
Surface roughness (Ra) using Taguch method on
material Aluminium Alloy 6082. The result shows that
the drilling depth was the most significant factor follow
by feed rate for roughness Ra.
K. Siva Kumar et al. [12] (2015) were conducted an
experiment for optimum cutting parameters in CNC End
milling of Aluminium Alloy 6082. RS methodology was
selected to optimize the surface roughness. The result
shows that the depth of cut was the influencing
parameter followed by Speed and feed for Ra.
Rishi raj singh et al. [13] (2016) were conducted an
experiment to determine optimum parameter for
minimum surface roughness in CNC end milling. The
response surface methodology was used for this
experiment. The result shows that cutting speed to be
the most significant and influential machining parameter
followed by feed rate. The surface roughness was
increased by increasing the feed but decreased by
increasing the cutting speed and nose radius.
Anurag Salankar et al. [14] (2017) were conducted an
experiment on extruded brass-lead alloy and inconel625
made of nickel and chromium due to their high strength,
wear resistance and fatigue resistance. The machining
was performed on the vertical milling centre using HSS
tool material. The result was compared by graphically
for different process parameters, performance
parameters evaluated and concluding optimizing process
parameters.
S. Sakthivelu et al. [15] (2017) were conducted an
experiment on Aluminium Alloy 7075 T6 in CNC milling
machine using High Speed Steel (HSS) cutting tool for
high quality of surface finish and metal removal rate. The
result shows that depth of cut was the most influencing
parameter for material removal rate followed by feed
and cutting speed.
3. EXPERIMENTAL DESIGN
3.1 Taguchi Method
The Taguchi Methods was developed by was Genichi
Taguchi. He was a Japanese engineer who began
working for the telecommunications company, Electrical
Communications Lab, a part of NT&T, in 1950’s. Taguchi
method is used evaluating and implementing
improvements in the products, optimization of the
objectives function. Optimization means determination
of best levels of control factors that maximize the signal-
to – noise ratio.

3.2 Signal- to- Noise Ratio
Signal-to-noise ratio is very important and useful
parameter in taking into account of goal and variation in
comparing two sets of samples. Signal–to- noise ratio is
the log functions of a given output. It is used for
optimization of objectives function. It is also help in data
analysis and prediction of results. Signal to noise ratio
formula are as follow-
 Smaller the better
( ∑ )
 Nominal the best
(̅ )
 Larger the better
( ∑ )
4. EXPERIMENTAL WORK
4.1 Workpiece Material
The material used in this research is Aluminium Alloy
6082. It is a medium strength alloy with highly corrosion
resistant. The Aluminium alloy 6082 has highest
strength of the 6000 series. It is mostly used in high
stress application, trusses, bridges, cranes and transport
application etc. The material properties are shown in the
above table.
Table -1: Material Properties
Density 2.71 g/cm3
Young modulus 71 GPa
UTS 140 to 330 MPa
Yield strength 90 to 280 MPa
Hardness Brinell 91 HB
Material composition
Al- 95.2 to 98.3%
Cr- 0.25% max
Cu- 0.1% max
Fe- 0.5% max
Mg- 0.6 to 1.2%
Mn- 0.4 to 1%
Si- 0.7 to 1.3% max
Ti- 0.1% max
Zn- 0.2% max
Fig -1: Workpiece
4.2 CNC Vertical machining Centre
CNC Vertical Machining Centre is an automatic machine
tool which is controlled by computer executes pre-
programmed commands. The machine is moved along
three x, y and z axis. The table is controlled in x and y
axis and machine spindle move along z axis. The G and M
codes are used to control the machine. These codes are
based on the three dimensional Cartesian coordinate
system. In CNC vertical machining centre the cutting tool
is hold on a vertical spindle and feed is provided by the
machine table.
Fig -2: Vertical Machining Centre

Table -2: VMC Specification
Machine Specification
M/C model no. TURBO-450-R40
Serial no. 6225/1215/66844
Capacity 450 W/hr
Spindle speed 6000 rpm
Temperature range 100 to 450 C
Frequency 50 HZ
Coolant System
Tank capacity 450 litter
Type of coolant Servo Cut-S
Coolant/water ratio 1:20
Coolant around spindle 50 lpm @ 1.8 bar
Coolant wash 60 lpm @ 4 bar
Lubrication System
Tank capacity 3 litter
Type of oil Servo way- H68
Max. working pressure 15 bar
4.3 Cutting Tool or End Mill
In this operation multipoint end milling cutter is used for
machining. The material of cutting tool is carbide. The
diameter of the cutting tool is 10 mm.
Fig -3: End Mill
4.4 Surface Roughness Tester
The portable stylus surface roughness tester is used to
measure the surface roughness. The stylus moves on the
peaks and valleys of the surface. The vertical motion of
the stylus is converted into electrical signal by the
transducer. This signal is in analog form. By using analog
to digital converter these signal are converted into
digital form and display the reading.
Fig - 4: Surface Roughness Tseter
4.5 Machining Parameter and Levels of
Experiment
Table -3: Levels of Experiment
Parameter
Levels
speed(N) in
rpm
Feed(f) in
mm/ min.
Depth of
Cut(d)
in mm
Level 1 800 100 0.5
Level 2 1200 150 1.0
Level 3 1600 200 1.5
The spindle speed, depth of cut and feed rate are chosen
the machining parameters. Also select the three levels of
experiment.
4.6 Experimental Data
By using Minitab software orthogonal array is prepared
and conduct the experiment, then with the help of
surface roughness tester measured the roughness. Then
we get the following experimental data.

Table -4: Experimental Data
Expt.
no.
Speed
(N)
Feed
(f)
Depth
of cut
(d)
Cutter
dia. (D)
Cutting
Speed
(V)
MRR
Avg.
Rough.
(Ra)
RMS Rough.
(Rq)
S/N
rpm mm/min mm mm m/min. mm3/sec mm mm db
1 800 100 0.5 10 25.132 08.33 0.93 1.15 0.6303
2 800 100 1.0 10 25.132 16.66 1.06 1.29 -0.5061
3 800 100 1.5 10 25.132 25.00 0.79 0.98 2.0475
4 800 150 0.5 10 25.132 12.50 1.78 2.38 -5.0084
5 800 150 1.0 10 25.132 25.00 1.82 2.26 -5.2014
6 800 150 1.5 10 25.132 37.50 1.97 2.44 -5.8893
7 800 200 0.5 10 25.132 16.67 3.11 3.91 -9.8552
8 800 200 1.0 10 25.132 33.33 3.97 4.96 -11.9758
9 800 200 1.5 10 25.132 50.oo 5.22 6.36 -14.3534
10 1200 100 0.5 10 37.698 08.33 0.91 1.12 0.8192
11 1200 100 1.0 10 37.698 16.67 0.60 0.72 4.4370
12 1200 100 1.5 10 37.698 25.00 1.79 2.30 -5.0571
13 1200 150 0.5 10 37.698 12.50 1.79 2.25 -5.0571
14 1200 150 1.0 10 37.698 25.00 0.69 0.86 3.2230
15 1200 150 1.5 10 37.698 37.50 0.78 0.96 2.1581
16 1200 200 0.5 10 37.698 16.67 1.89 2.36 -5.5292
17 1200 200 1.0 10 37.698 33.33 1.24 1.52 -1.8684
18 1200 200 1.5 10 37.698 50.00 1.52 1.84 -3.6369
19 1600 100 0.5 10 50.264 08.33 0.61 0.73 4.2934
20 1600 100 1.0 10 50.264 16.67 0.71 0.87 2.9748
21 1600 100 1.5 10 50.264 25.00 0.71 0.83 2.97483
22 1600 150 0.5 10 50.264 12.50 0.80 0.97 1.93820
23 1600 150 1.0 10 50.264 25.00 0.88 1.07 1.11035
24 1600 150 1.5 10 50.264 37.50 0.68 0.89 3.34982
25 1600 200 0.5 10 50.264 16.67 0.60 0.74 4.43697
26 1600 200 1.0 10 50.264 33.33 1.03 1.32 -0.25674
27 1600 200 1.5 10 50.264 50.00 0.83 1.03 1.61844

5. RESULT ANALYSIS
The experimental data are analysed in Minitab software
we get the following result.
Table -5: Response Table for Signal to Noise Ratios
Level N F D
1 -5.5680 1.4015 -1.4813
2 -1.1679 -1.0419 -0.8959
3 2.4933 -4.6023 -1.8653
Delta 8.0613 6.0038 0.9694
Rank 1 2 3
Table -6: Response Table for Average Roughness
Level N F D
1 2.2944 0.9011 1.3800
2 1.2456 1.2433 1.3333
3 0.7611 2.1567 1.5878
Delta 1.5333 1.2556 0.2544
Rank 1 2 3
It is clear that from both the table, the rank of spindle
speed, feed and depth of cut are 1, 2 and 3 respectively. It
means that the surface roughness is maximum affected
by speed follow by feed and depth of cut.
5.1 Effect of Machining Parameters on Surface
Roughness and S/N Ratio
Fig -5: Effects of machining parameter on surface
roughness
Fig -6: Effects of machining parameter on S/N Ratio
It is clear from figure -5 surface roughness decreases
with increasing spindle speed and increases with
increasing feed rate. Also from figure-6 it is clear that
S/N ratio increases with increasing spindle speed and
decreases with increasing feed rate.
5.2 Surface Plot with machining Parameters
Surface plot with variation of machining parameters are
given below.
Fig -7: Surface Plot with feed and speed
This figure shows that the surface is plot with the
variation of feed and depth of cut. Similarly figure -8 and
800
1200
0.0
1.5
3.0
100
1600
150
200
4.5
aR
f
N
N,fsvaRfotolPecafruS

figure -9 shows that the surface is plot with the variation
of other machining parameters.
Fig -8: Surface Plot with Speed and Depth of Cut
Fig -9: Surface Plot with Feed and Depth of Cut
6. CONCLUSIONS
In this research work we are selected the material
Aluminium alloy 6082 for optimization of machining
parameters spindle speed, feed and depth of cut for
minimum surface roughness in dry end milling
operation. Taguchi method is used to optimize these
parameters. According to L27 orthogonal array 27
experiments are performed. The experimental data is
analysed in Minitab software and we get the following
result.
Factor Rank
Speed 1
Feed 2
Depth of cut 3
 Speed, feed and depth of cut have rank 1, 2 and 3
respectively. It means that the surface roughness is
maximum affected by the speed followed by feed
and depth of cut.
 The surface roughness decreases with increasing
spindle speed and increases with increasing feed
rate.
 The optimum values of machining parameter for
minimum surface roughness in dry end milling
operation are speed 1600 rpm, feed 100 mm per
min. and depth of cut 1.0 mm.
REFERENCES
[1] R. Shetty, R. Pai, V. Kamath, S.S. Rao, Study on
surface roughness minimization in turning of
DRACs using surface roughness methodology
and Taguchi under pressured steam jet
approach, ARPN Journal of Engineering Applied
Science (ARPNJEAS), Vol.3, no. 1, 2008,59–67.
[2] Muammer Nalbant, Hasan Gokkaya, Ihsan
Toktas, Gokhan Sur., The experimental
investigation of the effects of uncoated, PVD-
and CVD-coated cemented carbide inserts and
cutting parameters on surface roughness in CNC
turning and its prediction using artificial neural
networks, Robotics and Computer-Integrated
Manufacturing (RCIM) 25 , 2009, pp.211–223.
[3] K. Bouacha, M.A. Yallese, T. Mabrouki and Rigal
J.F. Rigal, Statistical analysis of surface
roughness and cutting forces using response
surface methodology in hard turning of AISI
52100 bearing steel with CBN tool, International
Journal of Refractory Metals Hard Materials
(IJRMHM) 28, 2010, 349–361.
[4] P. Sahoo, Optimization of turning parameters for
surface roughness using RSM and GA, Advances
in Production Engineering & Management
(APEM), 6 (3), 2011, 197-208.
[5] Yansong Guo, Jef Loenders, Joost Duflou, Bert
Lauwers, Optimization of energy consumption
0.0
800
1200
0.0
1.5
3.0
0.5
1600
1.0
1.5
4.5
aR
d
N
N,dsvaRfotolPecafruS
100
150
0.0
1.5
3.0
0.5
200
1.0
1.5
4.5
aR
d
f
f,dsvaRfotolPecafruS

and surface quality in finish turning, Procedia
CIRP 1,2012, pp.529 – 534.
[6] Kamal Hassan, Anish Kumar, M.P.Garg,
Experimental investigation of Material removal
rate in CNC turning using Taguchi method,
International Journal of Engineering Research
and Applications (IJERA), Vol. 2, 2012, pp.1581-
1590.
[7] Upinder Kumar Yadav, Deepak Narang, Pankaj
Sharma Attri, Experimental Investigation and
Optimization Of Machining Parameters For
Surface Roughness In CNC Turning By Taguchi
Method, International Journal of Engineering
Research and Applications (IJERA) Vol. 2, Issue4,
2012, pp.2060- 2065.
[8] Asilturk, S. Neseli, Multi response optimisation
of CNC turning parameters via Taguchi method-
based response surface analysis, (ELSEVIER
Measurement 45), 2012, 785-794.
[9] Najiha M.S., M.M. Rahman, and A.R. Yusoff,
Modeling of The End Milling Process for
Aluminum Alloy Aa6061t6 Using Hss Tool,
International Journal of Automotive and
Mechanical Engineering (IJAME) Vol. 8,2013,pp.
1140-1150.
[10] M Saravana Kumar, T T M Kannan, S Giridharan
and P Vijaya Kumar, optimization of micro
milling parameters of al-6082 by anova
methodology, Internal journal of Mechanical
Engineering and Robotics Research (IJMERR)
Vol. 3, No. 4, 2014, 2278 – 0149.
[11] Nisha Tamta, R S Jadoun, Parametric
Optimization of Drilling Machining Process for
Surface Roughness on Aluminium Alloy 6082
Using Taguchi Method, SSRG International
Journal of Mechanical Engineering (SSRG-IJME)
Vol. 2, Issue 7,2015, 2348 – 8360.
[12] K. Siva Kumar and Bathina Sreenivasulu,
Optimization and Process Parameters of CNC
End Milling For Aluminium Alloy 6082,
International Journal of Innovations in
Engineering Research And Technology (IJIERT)
Vol. 2, Issue 1,2015, 2394-3696.
[13] Rishi Raj Singh, Dr. M.P. Singh and Sanjay Singh,
Optimization of machining parameters for
minimum surface roughness in end milling,
International Journal of Innovative Computer
Science & Engineering (IJICSE) Vol. 3, Issue 2,
2016, 28-34.
[14] Anurag Salankarand Manish Shete, A Review of
Optimization of Process Parameters of Face
Milling on Extruded Brass - Lead Alloy and
Inconel 625 on VMC850, International Journal of
Innovative Research in Science, Engineering and
Technology (IJIRSET) Vol 6, Special Issue 1,
2017, 2347 – 6710.
[15] S. Sakthivelu, T. Anandaraj and M. Selwin, Multi-
Objective Optimization of Machining
Conditionson Surface Roughness and MRR
during CNC End Millingof Aluminium Alloy 7075
Using Taguchi Design of Experiments, Mechanics
and Mechanical Engineering, Vol. 21, No. 1
(2017), 95–103.

Optimization of Machining Parameters Affecting Surface Roughness of Al6082 in Dry End Milling Operation on VMC

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