Reddy Sreenivasulu
R.V.R&J.C.College of Engineering (Autonomous),
INDIA.
Email: rslu1431@gmail.com
Submission: 25/05/2016
Revision: 03/06/2016
Accept: 10/06/2016
ABSTRACT
In
any machining operations, quality is the important conflicting objective. In
order to assure for high productivity, some extent of quality has to be
compromised. Similarly productivity will be decreased while the efforts are
channelized to enhance quality. In this study,
the experiments were carried out on a CNC vertical machining center to perform 10mm slots on Al 6351T6 alloy work
piece by K10 carbide, four flute end milling cutter. Furthermore the cutting
speed, the feed rate and depth of cut are regulated in this experiment. Each
experiment was conducted three times and the surface roughness and chip
thickness was measured by a surface analyser of Surf Test211 series (Mitutoyo)
and Digital Micrometer (Mitutoyo) with least count 0.001 mm respectively. The
selection of orthogonal array is concerned with the total degree of freedom of
process parameters. Total degree of freedom (DOF) associated with three
parameters is equal to 6 (3X2).The degree of freedom for the orthogonal array
should be greater than or at least equal to that of the process parameters.
There by, a L9 orthogonal array having degree of freedom equal to (91= 8) 8
has been considered .But in present case each experiment is conducted three times,
therefore total degree of freedom (9X31=26) 26 has been considered. Finally,
Analysis of variance (ANOVA) was conducted to compare the predicted values with
the experimental values confirm whose effectiveness in the analysis of average
of surface roughness and chip thickness.
Keywords: CNC
End milling, Al 6351T6 alloy, Taguchi method, S/N Ratio, ANOVA
1. INTRODUCTION
Milling
is the most extensively used machining process which may be employed in at
least one stage of fabrication in manufacturing industries. In the present days
CNC milling machines are commonly used as they possesses versatility,
flexibility and allows manufacture of products in shorter time at reasonable
cost and good surface finish. End milling is one of the important milling operations,
which is commonly used in manufacturing industries due to its capability of
producing complex geometric surfaces with reasonable accuracy and surface
finish.
In
end milling process, surface finish and material removal rate are two important
aspects, which require attention both from industry personnel as well as in
Research and Development, because these two factors greatly influence machining
performance. CNC machines are most suitable to achieve high quality products in
shorter time and to produce products at minimum cost.
2. LITERATURE STUDY
Recently, several attempts have been
made to determine the optimal machining parameters for achieving minimum
surface roughness from offline adjustment or online adaptive control. These
attempts are categorized into six major portions: machine tool, workpiece and
tool properties, cutting, thermal and dynamic parameters.
Abhang and Hameedullah (2011),
utilized the regression modeling in turning process of En31 steel using
response surface methodology (RSM) with factorial design of experiments. A
firstorder and secondorder surface roughness predicting models were developed
by using the experimental data and analysis of the relationship between the
cutting conditions and response (surface roughness).
Further, Reddy et al. (2014),
carried out studies on application of aluminum alloy 6351 towards aerospace
structures and its allied infrastructure. In this context considering Al 6351
which was used for making pressure vessel cylinders is now utilized for
aircraft structures. In this investigation the tensile strength on circular rod
specimen of Al 6351 is determined by applying the loads on universal testing
machine with various dimensions.
Krishna, Reddy and Hussain (2014),
carried out studies on friction stir welding (FSW) which is a solid state
welding and is gaining more applications in various industries due to better
quality of the joint as it has no negative effect on parent metal.
Balla and Patil (2012) described use
and steps of Taguchi design of experiments and orthogonal array to find a
specific range and combinations of turning parameters like cutting speed, feed
rate and depth of cut to achieve optimal values of response variables like
surface finish, tool wear, material removal rate in turning of Brake drum of FG
260 gray cast iron Material.
Naidu, Vishnu and Raju (2014) also
applied taguchi based optimization for surface roughness during end milling of
EN 31 steel in their work. Taguchi orthogonal array is designed with three
levels of milling parameters and different experiments are done using L9
orthogonal array, containing four columns which represents four factors and
nine rows which represents nine experiments to be conducted and value of each
parameter was obtained.
Avinash (2013), considered in his
paper an experimental plan based on Taguchi’s technique including L9 orthogonal
array with four factors and three levels for each variable and studying the
contribution of each factor on surface roughness. The experiments were
conducted on 1040 MS material on CNC vertical milling machine using carbide
inserts. The analysis of mean and variance technique is employed to study the
significance of each machining parameter on the surface roughness.
Korat and Agarwal (2012), conducted
an experimental study to optimize the effects of cutting parameters on surface
finish and MRR of EN24/AISI4340 work material by employing Taguchi techniques.
The orthogonal array, signal to noise ratio and analysis of variance were
employed to study the performance characteristics in turning operation. Maurya,
Diwaker (2012) studied on CNC end milling, influence of various machining
parameters like, tool feed (mm/min), tool speed (rpm), tool diameter (mm) and
depth of cut (mm).
Benardos and Vosniakos (2002)
predicted surface roughness in CNC face milling using neural networks and
Taguchi’s design of experiments. The surface roughness and dimensional
deviation have been predicted by measuring cutting forces and vibrations in
turning process by Risbood, Dixit and Sahasrabudhe (2003).
The cutting speed, feed rate and
depth of cut were chosen as an input and the workpiece was steel bars. It was
observed that the length and the diameter of the steel bar have insignificant
effect on the surface roughness compared to the cutting speed, feed rate and
depth of cut.
With the reduction of feed rate the
chip removal action is improved and high quality surface is obtained. An
adaptive neural networkbased fuzzy inference system is presented by Hoa et al.
(2009) for the prediction of the surface roughness in the end milling process
using hybrid Taguchigenetic learning algorithm. The material of 6061 aluminium
alloy has been used as a workpiece.
The spindle speed, feed rate and
depth of cut were selected as the machining parameters. Experimental results
have shown that the optimal prediction error of the hybrid Taguchigenetic
learning algorithm HTGLAbased on adaptivenetwork based fuzzy inference system
ANFIS approach is 4.06% which outperforms the optimal prediction errors 4.65% and
4.17% obtained, respectively, by using Matlab toolbox.
Based on the literature survey
performed, venture into this research was amply motivated by the fact that a
little research has been carried out towards optimization of chip thickness, it
was observed that major works concentrated on surface roughness. So in this
work chip thickness also considered to obtain optimal levels of process
parameters during end milling of Aluminum6351T6 alloy.
3. INTRODUCTION TO TAGUCHI DESIGN METHOD
Taguchi Method was proposed by Dr.
G. Taguchi in the year 1950. This method explores the concept of quadratic
quality loss function and uses a statistical measure of performance called
signaltonoise (S/N) ratio. In Taguchi method (PHILLIP, 2005), the process
parameters are divided into two groups such as control factors and noise
factors.
The control factors are the
controllable parameters which affect the process significantly whereas noise
factors are the variables that affect the process and are either uncontrollable
or more expensive to control. Signal represents the effect on the average
response while the noise is a measure of the influence on the deviation from
the average response.
The S/N ratio is the ratio of the
mean (Signal) to the standard deviation (Noise), which indicates the scattering
around a target value. This ratio helps to identify the optimum level of
process parameters. The combination of parameters with the highest S/N ratio
will be the optimum setting of process parameters.
A high S/N ratio is desirable as the
signal level is much higher than the random noise level that leads to best
performance. The calculation of S/N ratio depends on the quality
characteristics of the product or process to be optimized. The equation for
calculating S/N ratios for “smaller is better” is given below.

(1) for smaller is better
Where, yi = average of surface
roughness and chip thickness in the i th test and n = number of replications.
The orthogonal arrays are used to
find parameters which will improve the performance which will improve the
performance of a product or process. Another application of OA technique is to
find less expensive, alternative product design, material or production method
which will provide performance equivalent to that of a corresponding existing
alternative. The orthogonal arrays used by Taguchi approach allow the study of
simultaneous effects of several factors efficiently and providing better
results using smaller number of experimental runs.
The selection of orthogonal array is
concerned with the total degree of freedom (DOF) of process parameters. Total
degree of freedom (DOF) associated with three parameters is equal to 6
(3X2).The degree of freedom for the orthogonal array should be greater than or
at least equal to that of the process parameters. There by, a L9 orthogonal
array having degree of freedom equal to (91= 8) 8 has been considered .But in
present case each experiment is conducted three times, therefore total degree
of freedom (9X31=26) 26 has been considered finally.
4. EXPERIMENTATION AS PER TAGUCHI DESIGN METHOD:
A plan of experiments based on
Taguchi technique has been used to acquire the data. An orthogonal array,
signal to noise (S/N) ratio and analysis of variance (ANOVA) are employed to
investigate the machining characteristics of Al 6351T6 alloy material using
K10 carbide end mill.
Finally, confirmation test have been
carried out to compare the predicted values with the experimental values to
confirm its effectiveness in the analysis of average surface roughness and chip
thickness. Machining parameters and their levels tabulated in Table 1 and
Experimental plan as per Taguchi L9 orthogonal array and measured responses are
depicted in table 2.
Figure 1: Experimental setup
(CNC
Vertical Machining Center, KENT INDIA Co, Ltd, Taiwan)
In this study, the experiments were
carried out on a CNC vertical machining center (KENT and ND Co. Ltd, Taiwan
make shown in Figure 1) to perform 10mm slots on Al 6351T6 alloy work piece of
size 300mm X 50mm X 25mm by K10 carbide, 4 flute end milling cutter.
Furthermore the cutting speed (rpm), the feed rate (mm/min) and depth of cut
(mm) are regulated in this experiment. Each experiment was conducted three
times and the chips are collected and measured the chip thickness (mm) with
Digital Micrometer (Least Count 0.001mm, Mitutoyo make shown in Figure 3) which
are shown in Figure 2, finally surface roughness is measured at five places on
each slot then average of them in μm is considered by a surface analyser of
Surf Test211 series (Mitutoyo) shown in Figure 4.
Figure 2: Camera images of collected chips
(average) with different machining
conditions
Figure 3: Measurement of chip
thickness using Mitutoyo 29334030 Digital Micrometer
Figure 4: Measurement of surface
roughness using Surf Test211 series (Mitutoyo)
Factors and levels fixed from
previous literature and specifications of the equipment and availability of
measuring apparatus.
Table1: Machining parameters and
their levels
Symbol 
Factors 
Units 
Level 1 
Level 2 
Level 3 
A 
Spindle Speed 
rpm 
600 
800 
1000 
B 
Feed Rate 
mm/min 
50 
100 
150 
C 
Depth of Cut 
mm 
0.3 
0.5 
0.7 
Table 2:
Experimental plan as per Taguchi L9 orthogonal array and measured responses
Exp.No. 
Machining Parameters 
Average Surface Roughness (Ra) µm 
Average Chip Thickness (Ct) mm 
S/N Ratio 

Spindle
Speed (A) rpm 
Feed
Rate (B) mm/min 
Depth of Cut (C) mm 

1 
1 
1 
1 
0.166 
0.125 
16.6574 
2 
1 
2 
2 
0.216 
0.140 
14.7980 
3 
1 
3 
3 
0.233 
0.230 
12.7088 
4 
2 
1 
2 
0.145 
0.180 
15.7329 
5 
2 
2 
3 
0.165 
0.190 
14.9945 
6 
2 
3 
1 
0.170 
0.210 
14.3771 
7 
3 
1 
3 
0.190 
0.100 
16.3733 
8 
3 
2 
1 
0.240 
0.130 
14.2887 
9 
3 
3 
2 
0.225 
0.220 
13.0529 
*In
the table.2 above the S/N ratios calculated from eq.1
5. RESULTS AND DISCUSSIONS:
After performing the experiments,
responses are recorded using suitable apparatus and tabulated in Table 2 then
using design of experiment software for example minitab@17.3, S/N ratios for
smaller is the better condition determined by giving input data and calculated
the means of s/n ratios corresponding levels of parameters and depicted the
values in Table 3 for S/N ratios, from this table, rankwise priority of
parameters to influence the process obtained i.e. A3BIC2 (rank wise) to minimize the responses
during machining of Al 6531T6 material identified experimentally.
Table 3: Means of S/N Ratio
Level 
Spindle
Speed(rpm) A 
Feed Rate
(mm/min) B 
Depth of
cut(mm) C 
1 
14.72 
16.25 
15.11 
2 
15.03 
14.69 
14.53 
3 
14.57 
13.38 
14.69 
Delta 
0.46 
2.87 
0.58 
Rank 
3 
1 
2 
From measured responses
experimentally, minimum surface roughness obtained at experiment 4 corresponding level of factors
areA2B1C2 and minimum chip thickness obtained at experiment 7 corresponding
level of factors are A3B1C3.
Figure 5: Main effects plot for
S/N Ratio
Figure 6: Main effects plot for
Data Means
For main effect plots for S/N ratios
and data means are drawn (Shown in Fig.5 and Fig.6), from this analyzed that
optimum combination of levels of process parameters A2B1C1 obtained by 800 rpm
spindle speed, 50 mm/min feed rate and 0.3 mm depth of cut gives good results
to get optimum values of responses.
5.1.
Analysis of Variance (ANOVA)
ANOVA is a particular form of statistical
hypothesis testing heavily used in the analysis of experimental
data. A test result (calculated from the null hypothesis and the sample) is called
statistically significant if it is deemed unlikely to have occurred by
chance, assuming the truth of the null hypothesis.
The ANOVA table organized as
follows:
The first column is entitled Source
of Variation and delineates the between treatment and error or residual
variation. The total variation is the sum of the between treatment and error
variation. The second column is entitled Sums of Squares (SS) and is
computed by summing the squared differences between each treatment (or group)
mean and the overall mean. The squared differences are weighted by the sample
sizes per group (nj).
The error sum of squares (SSE) and
is computed by summing the squared differences between each observation and its
group mean (i.e., the squared differences between each observation in group 1
and the group 1 mean, the squared differences between each observation in group
2 and the group 2 mean, and so on).
The double summation (SS) indicates
summation of the squared differences within each treatment and then summation
of these totals across treatments to produce a single value. The total sum of
squares (SST) and is computed by summing the squared differences between each
observation and the overall sample mean.
In an ANOVA, data are organized by
comparison or treatment groups. If all of the data were pooled into a single
sample, SST would reflect the numerator of the sample variance computed on the
pooled or total sample. SST does not figure into the F statistic directly.
However, SST = SS + SSE, thus if two sums of squares are known, the third can
be computed from the other two. The third column contains degrees of
freedom. The between treatment degrees of freedom is df1 = k1.
The error degrees of freedom is df2 =
N  k. The total degrees of freedom is N1 (and it is also true that (k1) +
(Nk) = N1). The fourth column contains Mean Squares (MS) which are
computed by dividing sums of squares (SS) by degrees of freedom (df), row by
row. Specifically, MS=SS/ (k1) and MSE=SSE/ (Nk). Dividing SST/ (N1)
produces the variance of the total sample. The F statistic is in the rightmost
column of the ANOVA table and is computed by taking the ratio of MS/MSE.
For confirmation of above
experimental study, ANOVA for raw data of response was obtained by design of
experiments software for example Minitab @17.3 and the values are tabulated in Table
4 and 5.
Table 4: ANOVA for Surface Roughness
Symbol 
Cutting Parameters 
DO F 
SS 
MS 
F 
Remarks 
A 
Spindle Speed (rpm) 
2 
0.005606 
0.002803 
8.40* 
significant 
B 
Feed Rate (mm/min) 
2 
0.003398 
0.001699 
5.09 * 
significant 
C 
Depth of Cut (mm) 
2 
0.000028 
0.000014 
0.04 
Insignificant 
Error 

20 
0.000668 
0.000334 


Total 

26 
0.009698 



*Significant,
if F exp > F table, from table at 95%confidence level F critical = 3.49
Table5: ANOVA for Chip Thickness
Symbol 
Cutting Parameters 
DO F 
SS 
MS 
F 
Remarks 
A 
Spindle Speed (rpm) 
2 
0.002906 
0.001453 
1.70 
Insignificant 
B 
Feed Rate (mm/min) 
2 
0.012006 
0.006003 
7.04 * 
significant 
C 
Depth of Cut (mm) 
2 
0.001006 
0.000503 
0.59 
Insignificant 
Error 

20 
0.001706 
0.000853 


Total 

26 
0.017622 



*Significant, if F exp > F table, from table
at 95%confidence level F critical = 3.49
From “table 4” ,surface roughness is
more influenced by spindle speed and feed rate in an order but depth of cut is
less influenced during end milling of Al 6351T6 alloy material.
From “table 5”, chip thickness is
more influenced by feed rate, spindle speed is less influenced and depth of cut
not influenced during end milling of Al 6351T6 alloy material.
From minitab@17 interaction of
process parameters on measured responses (both surface roughness and chip
thickness separately) are obtained for various levels of machining parameters
considered during experimentation.
From interaction plot(Figure 7) for
surface roughness, it is observed that interaction of B, C factors with factor
A for response Ra shows that feed rate and depth of cut both are of level 3, level
1 and level 2 caused to higher, moderate and lower effects on surface roughness
with respect to spindle speed, interaction of A, C factors with factor B for
response Ra shows that spindle speed and depth of cut both are of level 2,level
3 and level 1 caused to higher, moderate and lower effects on surface roughness
with respect to feed rate and interaction of A,B factors with factor C for
response Ra shows that feed rate and depth of cut both are of level 1,level 2
and level 3 caused to higher, moderate and lower effects on surface roughness
with respect to depth of cut.
Figure 7:
Interaction plot for surface roughness 
Figure 8:
Interaction plot for chip thickness 
From interaction plot for chip
thickness roughness, it is observed that interaction of B, C factors with
factor A for response Ra shows that feed rate and depth of cut both are of
level 2,level 1 and level 3 caused to higher, moderate and lower effects on
surface roughness with respect to spindle speed, interaction of A, C factors
with factor B for response Ra shows that spindle speed and depth of cut both
are of level 3,level 1 and level 2 caused to higher, moderate and lower effects
on surface roughness with respect to feed rate and interaction of A,B factors
with factor C for response Ra shows that feed rate and depth of cut both are of
level 3,level 2 and level 1 caused to higher, moderate and lower effects on
surface roughness with respect to depth of cut.
5.2.
Confirmation Experiment
Once the optimal level of machining
parameters is selected the final step is to predict and verify the improvement
of the experiment no.4 by setting the optimal level of the machining parameters
800rpm as spindle speed, 50mm/min as feed rate and 0.5mm depth of cut. The
estimated response value using the optimum level of the machining parameters
can be calculated as,
(2)
Where ᵞm is the total mean of the
response (average surface roughness and chip thickness) value, ᵞj is the mean
of the response value at the optimum level and q is the number of machining
parameters that significantly affects the multiple performance characteristics.
Based on equation (2) the estimated surface roughness and chip thickness values
for the optimal machining parameters can be obtained. Table.6 and Table.7 shows
the results of the confirmation experiment using the optimal machining parameters.
Table 6: Mean
of measured average Surface Roughness and Chip Thickness
responses
Mean Surface Roughness (µm) 
Mean Chip Thickness (mm) 

Level 
A 
B 
C 
A 
B 
C 
1 
0.205 
0.167 
0.192 
0.165 
0.135 
0.155 
2 
0.16 
0.207 
0.195 
0.193 
0.153 
0.180 
3 
0.218 
0.209 
0.196 
0.150 
0.220 
0.173 
Table 7: Optimal values of individual machining
characteristics
machining characteristics 
Optimal combination of parameters 
Significant parameters(at 95% confidence level) 
Predicted optimum value 
Experimental value 
Average Surface Roughness(Ra) 
A2B1C2 
A,B 
0.1326
µm 
0.145 µm 
Average Chip thickness (Ct) 
A2B1C2 
B 
0.135mm 
0.18mm 
6. CONCLUSIONS
On comparing the signal to noise
ratio, rankwise priority of parameters to influence the process obtained
i.e. A3BIC2 (rank wise) to minimize the
responses during end milling of aluminium 6351T6 alloy, the feed rate have to
be maintained at higher level, spindle speed maintained at moderate level and
depth of cut in lower level yields better surface roughness and minimum chip
thickness. Average surface roughness (Ra) is greatly reduced from 0.145 µm to
0.1326 µm and the chip thickness (Ct) is also reduced from 0.18 mm to 0.135 mm.
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