1. INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 –
International Journal of JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online) IJMET
Volume 4, Issue 2, March - April (2013), pp. 134-140
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INVESTIGATING THE EFFECT OF MACHINING PARAMETERS ON
SURFACE ROUGHNESS OF 6061 ALUMINIUM ALLOY IN END
MILLING
U. D. Gulhane*, M.P.Bhagwat, M.S.Chavan ,S.A.Dhatkar, S.U.Mayekar
Department of Mechanical Engineering,
Finolex Academy of Management and Technology, Ratnagiri,
Maharashtra 415612, India
*Corresponding author- Associate Professor, Dept. of Mechanical Engineering,
Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block,
Ratnagiri- (M.S.) 415639, India
ABSTRACT
Design of experiments is performed to analyse the effect of spindle speed, feed rate
and depth of cut on the surface roughness of 6061 Aluminium alloy. The results of the
machining experiments were used to characterise the main factors affecting surface
roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the
most significant parameter influencing the surface roughness in the end milling process.
Keywords: Surface roughness, DOE, ANOVA, 6061 Aluminium alloy.
INTRODUCTION
Milling process is one of the common metal cutting operations used for machining
parts in manufacturing industry. It is usually performed at the final stage in manufacturing a
product. The demand for high quality and fully automated production focuses attention on the
surface condition of the product, especially the roughness of the machined surface, because
of its effect on product appearance, function, and reliability. In the present work an
experimental investigation of milling on aluminium 6061 with HSS tool is carried out and the
effect of different cutting parameters on the surface roughness is studied.
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
The material used for the analysis is 6061 Aluminium alloy which is widely used in
wings of the aeroplane, wheels of the automobiles.
In this paper, L9 orthogonal array is employed to analyze experimental results of
machining obtained from 9 experiments by varying three process parameters viz. cutting
speed (A), depth of cut (B) and feed rate(C). ANOVA has been employed and compared with
Taguchi method.
METHODOLOGY
DOE techniques enable designers to determine simultaneously the individuals and
interactive effects of many factors that could affect the output results in any design. There are
three input parameters and three level. Full factorial experimental design will give rise to
total 33=27 experiments which is time consuming and lengthy procedure.
Fig 1: End- milling operation
Taguchi found out new method of conducting the design of experiments which
are based on well defined guidelines. This method uses a special set of arrays called
orthogonal array. This standard array gives a way of conducting the minimum number of
experiments which could give the full information of all the factors that affect the response
parameter instead of doing all experiments.
ANOVA was developed by Sir Ronald Fisher in 1930 and can be useful for
determining influence of any given input parameter for a series of experimental results by
design of experiments for machining process and it can be used to interpret experimental
data. ANOVA is statistical based objective decision making tool for detecting any differences
in average performance of groups of items tested. While performing ANOVA degrees of
freedom should also be considered together with each sum of squares. In ANOVA studies a
certain test error, error variance determination is very important. Obtained data are used to
estimate F value of Fisher Test (F-test). Variation observed (total) in an experimental
attributed to each significant factor or interaction is reflected in percent contribution (P),
which shows relative power of factor or interaction to reduce variation.
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3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
MATERIALS AND METHOD
The Rectangular 25 X 25 X 100 mm 6061 Aluminium Alloy specimens were used for
experimentation. Table 1 and 2 shows properties and composition of 6061 Aluminium Alloy
used for the study. Milling operation was carried out on SINGER UNIVERSAL MILLING
MACHINE by using HSS tool.
Table 1 Properties of 6061 Al
TENSILE TEST BHN
Ultimate Yield Modulus Of For 500
Stress Stress Elasticity Kg
(N/mm2) (N/mm2) (GPa)
251.66 202.92 56.1 79.57
Table 2 Composition of 6061 Al
Elements Al Si Fe Mg Ti Ca Cd B P Na Mn
% 98.81 0.475 0.178 0.49 0.0135 0.0027 0.001 0.0015 0.0014 0.0043 0.0005
Work piece was inserted in the jaw on the work bed and was tightened in the jaws
until they fixed the work piece such that top surface of the work piece will be perfectly
perpendicular to the tool axis. The milling was carried out for 9 different work pieces. For
each workpiece, all the three parameters, viz. cutting speed, depth of cut and feed rate, were
varied as shown in Table 3.
Table 3: Machining parameters and levels:
Machining Level 1 Level 2 Level 3
Parameters
Cutting speed
58 220 500
(Rev/min)
Depth of cut
0.4 0.8 1.2
(mm)
Feed rate
15.31 41.84 104.56
(mm/min)
The surface roughness of each specimen was tested on the surface roughness tester
(Mitutoyo Roughness tester SJ-400) for cut off value of 4.0 mm distance. The Ra value was
generated by the tester for each work piece.
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4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
RESULTS AND DISCUSSION
Table 4 shows experimental design matrix and surface roughness value (Ra) for 6061
Al, S/N ratio is calculated using Lower the better characteristics.
where, n = No of measurements in a trial/row
Yi = ith measured value in a run/row
Table 4 Experimental Design Matrix and Results
EXPT
NO. MILLING PARAMETERS
DEPTH FEED S/N
SPEED OF CUT RATE SURFACE RATIO MEAN
(RPM) (mm) (mm/min) ROUGHNESS
(µm)
1 58 0.4 15.32 2.54 -8.0967 2.54
2 58 0.8 41.84 2.78 -8.8809 2.78
3 58 1.2 104.56 2.97 -9.4551 2.97
4 220 0.8 15.32 2.06 -6.2773 2.06
5 220 1.2 41.84 2.86 -9.1273 2.86
6 220 0.4 104.56 3.4 -10.6296 3.4
7 500 1.2 15.32 1.58 -3.9731 1.58
8 500 0.4 41.84 1.81 -5.1536 1.81
9 500 0.8 104.56 2.54 -8.0967 2.54
Responses for Signal to Noise Ratios of Smaller is better characteristics is shown in
Table 5. Significance of machining parameters (difference between max. and min. values)
indicates that feed is significantly contributing towards the machining performance as
difference gives higher values. Plot for S/N ratio shown in Figure 1 explains that there is less
variation for change in depth of cut where as there is significant variation for change in feed
rate.
Table 5-Response Table for a) Signal to Noise Ratios and (b) means
(a) (b)
Level A B C Level A B C
1 -8.811 -7.96 -6.116 1 2.763 2.583 2.06
2 -8.678 -7.752 -7.721 2 2.773 2.46 2.483
3 -5.741 -7.519 -9.394 3 1.977 2.47 2.97
Delta 3.07 0.441 3.278 Delta 0.797 0.123 0.91
Rank 2 3 1 Rank 2 3 1
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5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
M a in E ff e c ts P lo t f o r S N r a tio s
D a ta M e a n s
Speed Fe e d
-6
-7
-8
Mean of SN ratios
-9
58 220 500 1 5 .3 3 4 1 .9 2 1 0 4 .9 0
Depth
-6
-7
-8
-9
0 .4 0 .8 1 .2
S ig n a l - to - n o is e : S m a l le r is b e tte r
M a in E f f e c t s P l o t f o r M e a n s
Da ta M e a n s
Speed Fe e d
3 .0 0
2 .7 5
2 .5 0
2 .2 5
Mean of Means
2 .0 0
58 220 500 1 5 .3 3 4 1 .9 2 1 0 4 .9 0
De pth
3 .0 0
2 .7 5
2 .5 0
2 .2 5
2 .0 0
0 .4 0 .8 1 .2
Fig. 2 Effect of cutting speed, Depth of cut and Feed rate on surface finish
Taguchi method cannot judge and determine effect of individual parameters on
entire process while percentage contribution of individual parameters can be well determined
using ANOVA. MINITAB software of ANOVA module was employed to investigate effect
of process parameters cutting speed, Depth of Cut and Feed rate.
Table 6-Analysis of Variance for S/N ratios
Adj
Source DF Seq SS Adj SS MS F P
A 2 18.0668 18.0668 9.0334 5.46 0.155
B 2 0.2926 0.2926 0.1463 0.09 0.919
C 2 16.121 16.121 8.0605 4.87 0.17
Residual
error 2 3.3098 3.3098 1.6549
Total 8 37.7902
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6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
Table 7-Analysis of Variance for Means
Source DF Seq SS Adj SS Adj MS F P
Speed 2 1.25362 1.25362 0.62681 5.4 0.156
Feed 2 1.24416 1.24416 0.62208 5.36 0.157
Depth 2 0.02816 0.02816 0.01408 0.12 0.892
Residual
Error 2 0.23209 0.23209 0.11604
Total 8 2.75802
Table 6 and 7 shows Analysis of variance for S/N ratio and mean. F value (5.46) of parameter
indicates that feed rate is significantly contributing towards machining performance. F value
(0.09) of parameter indicates that depth of cut is contributing less towards surface finish. It
can be observed rough surface for the specimen No. 6 (cutting speed, 220 rev/min; depth of
cut, 0.4 mm; feed, 104.90 mm/min.) and smooth surface for the specimen No. 7 (cutting
speed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/min.)
Fig3: Surface texture for the test (cutting Fig 4: Surface texture for the test (cutting
speed 500 rev/min, depth of cut 1.2 mm, speed 220 rev/min, depth of cut 0.4 mm,
feed 15.33 mm/min) feed 104.90 mm/min)
Fig5: Surface Roughness Profile For specimen 1
Cut off length = 4.0 mm, Ra= 2.54 µm
The pattern of impressions left by tool after the machining of workpiece is called as a
‘lay pattern’ and it is circular in end milling process. When the feed is high the pattern is
more prominent as the time available for traversing is less. When the feed is low the lay
pattern is not much emphasized as more time available for traversing. Hence we observed in
our experimentation that the contribution of feed is dominant amongst all three parameters in
surface roughness.
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7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
CONCLUSION
Taguchi method of experimental design has been applied for investigating the effect
of machining parameters on surface roughness. Results obtained from Taguchi method
closely matches with ANOVA. Best parameters found for finish machining are: cutting
speed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/rev. The parameters found for
rough machining are cutting speed, 220 rev/min; depth of cut, 0.4 mm; feed, 104.90 mm/min.
Feed is most influencing parameters corresponding to the quality characteristics of surface
roughness.
ACKNOWLEDGEMENT
Quality control department of Adler Mediequit PVT.LTD, Ratnagiri are gratefully
acknowledged.
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