Keywords: Al 6351-T6 alloy,
Surface roughness, Chip thickness, Grey relational analysis
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.
The
thickness of the chips on milling operation changes continuously and they have
a complex shape which depends from many different factors. In order to simplify
the matter it’s better to talk about the average thickness of the chips: this
is a parameter which give an idea about the cutting force and how much the milling
cutter and the milling machine are stressed.
It
is common knowledge that a chip produced through a milling process is not of
uniform thickness. Assuming climb milling, the chip is thicker towards its
beginning than its end. Every chip has a maximum thickness at a single point
and gets gradually thinner from there. Given a constant spindle speed and
feedrate, the thickness of a chip is a function of its length; the longer the
chip, the thicker the chip.
The
ideal average chip thickness for a particular insert depends on the insert’s
edge preparation. Compared to the sharp angle formed by the rake and flank
faces of a typical turning insert, a milling insert’s cutting edge generally
has a small chamfer to protect against the shock of repeated material entry.
Chips
should be at least as thick as this edge protection (typically in the form of a
T-land or hone) to properly dissipate heat. Chips that are too thin indicate
that the cutting action and the heat it generates is constrained to a
relatively small portion of the insert edge. This can lead to premature
cratering, thermal cracking or flank wear. Chips that are too thick indicate
high cutting forces that could overwhelm and break the insert.
1.1.
Background literature
Sreenivasulu
and Rao (2012) applied Taguchi method and GRA to optimize drilling parameters
for surface roughness and roundness error simultaneously. Joshi and Kothiyal (2013) investigated the SR response on CNC milling by
Taguchi technique. Surface finish is analyzed, which shows the percentage
contribution of each influencing factor.
Nair
and Govindan (2013) conduct the study on application of Principal Component
analysis (PCA) coupled with Taguchi method to solve correlated multi-attribute
optimization of CNC end milling operation.PCA has been proposed to eliminate
correlation between the responses and to estimate uncorrelated quality indices
called principal components.
Ahmad,
Sharma and Mittal (2014) studied the machining parameters like depth of cut,
cutting speed, feed rate and tool diameter are optimized with multiple
performance characteristics, and concluded that the S/N ratio with Taguchi‟s
parameter design is a simple, systematic, reliable and more efficient tool for
optimizing multiple performance characteristics of CNC milling process
parameters.
Kumar
and Thirumurugan (2012) had studied The end milling of titanium alloys, for the
investigation of the optimum parameters that could produce significant good
surface roughness whereby reducing tooling cost and concluded that The
significant factors for the surface roughness in milling CP Ti Grade 2 were the
spindle speed and the tool grade, with contribution of 30.347 and 29.933
respectively.
Sreenivasulu (2014) had conducted the study to deals with optimization of
surface roughness and delamination damage on GFRP material during end milling
using grey- based taguchi method. From the results of ANOVA, it was concluded
that cutting speed and DOC are the most significant factors. PR.
Periyanan,
Natarajan and Yang (2011) had carried out experiment to focus the taguchi
technique for the optimization in microend milling operation to achieve maximum
metal removal rate & result shows that the optimal combination as medium
cutting speed, high feed rate and high depth of cut.
Pandey
et al. (2013) conducted experiments to perform the parametric optimization of
CNC end milling machine tool in varying condition. Results showed that cutting
speed and feed are the powerful control parameters for the material removal
rate and depth of cut as powerful factors for controlling the surface finish of
Mild Steel.
Barman
and Sahoo (2009) had conducted an experimental study of fractal dimension
characteristics of surface profile produced in CNC milling and optimization of
machining parameters based on Taguchi method. It is also observed with increase
in spindle speed the fractal dimension increases.
Chawale
et al. (2013) had conducted to study experimentally the influence of depth of
cut, cutting speed, and feed and work piece material type on cutter temperature
during milling process. It was concluded that, the cutting speed is most
contributory factor, work material is second important factor and Feed rate is
third important factor.
Abhishek
et al. (2008) had conducted study for the multiple response optimization of end
milling parameter using grey based taguchi method. The feed rate was identified
as the most influential process parameter on surface roughness.
2. EXPERIMENTATION
In
any experimental work, it is difficult to consider all these factors that
affect the quality and productivity. From the literature it is observed that
the parameters such as depth of cut, spindle speed and feed rate are the three
predominant cutting parameters influencing on quality and productivity in any
machining operation. These three cutting parameters are considered to
investigate their affect on surface roughness.
The
design of experiments technique permits us to carry out the modeling and
analysis of the influence of process variables (design factors) on the response
variables. In the present experiment spindle speed (A, rpm), feed rate (B,
mm/min) and depth of cut (C, mm) have been selected as design factors (their
levels are selected by considering the specifications of machine and from
previous literature) while other parameters have been assumed to be constant
over the experimental domain. The dimensions of the work piece are 300mm X 50mm
X 25mm.
In
this study, the experiments were carried out on a CNC vertical machining center
(KENT and ND Co. Ltd, Taiwan make) to perform 10mm s lots on Al 6351-T6 alloy
work piece by K10 carbide, four flute end milling cutter as shown in Figure 1.
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) which are shown in Figure2, 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 Test-211 series (Mitutoyo) shown in Figure 1. The
experimentation has been conducted through the following step by step
procedure.
Step 1:
The work piece is clamped in milling-vice on the working table of CNC milling
machine using T-clamps, bolts, jigs and fixture.
Step 2:
Select the suitable CNC end milling cutter and their axis is selected. The
selected cutter is fixed in the main spindle using different collets.
Step 3:
Performing milling operation on specimens in involving various combinations of
input parameters such as cutting speed, feed and depth of cut. A manual part
programming is developed for CNC end milling.
Step 4:
Measured the surface roughness with the help of a Surf Test-211 series at five
places on each slot then average of them in μm was tabulated.
Step 5:
Measured the chip thickness 3 times for different chips randomly collected
during the machining operation as per L9 orthogonal array with the help of
Digital micrometer (Least Count 0.001 mm, Mitutoyo make) and average reading
depicted in the table.2.
Figure 1:
Experimental setup, Measurement of surface roughness using Surf Test-211 series
Figure 2: Camera images of collected chips with different
machining conditions
Table 2:
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 3:
Experimental plan as per Taguchi L9 orthogonal array and measured responses
|
Exp.No. |
Machining
Parameters |
Surface
Roughness (Ra) µm |
Chip
Thickness mm |
||
|
Spindle
Speed (A) |
Feed Rate
(B) |
Depth of
Cut (C) |
|||
|
1 |
1 |
1 |
1 |
0.166 |
0.125 |
|
2 |
1 |
2 |
2 |
0.216 |
0.140 |
|
3 |
1 |
3 |
3 |
0.233 |
0.230 |
|
4 |
2 |
1 |
2 |
0.145 |
0.180 |
|
5 |
2 |
2 |
3 |
0.165 |
0.190 |
|
6 |
2 |
3 |
1 |
0.170 |
0.210 |
|
7 |
3 |
1 |
3 |
0.190 |
0.100 |
|
8 |
3 |
2 |
1 |
0.240 |
0.130 |
|
9 |
3 |
3 |
2 |
0.225 |
0.220 |
3. GREY RELATIONAL ANALYSIS
The
validity of traditional statistical analysis techniques is based on assumptions
such as the distribution of population and variances of samples. Nevertheless
sample size will also affect the reliability and precision of the results
produced by traditional statistical analysis techniques. J. Deng argued that
many decision situations in real life do not conform to those assumptions, and
may not be financially or pragmatically justified for the required sample size.
Making
decisions under uncertainty and with insufficient or limited data available for
analysis is actually a norm for managers in either public or private sectors.
To address this problem, J. Deng developed the grey system theory, which has
been widely adopted for data analysis in various fields.
The
grey relational analysis introduced in the following is a method in grey system
theory for analyzing discrete data series. A procedure for the grey relational
analysis consists of the following steps.
1.
Generate reference data series.
= (d01, d02, ..., d0m)
In general, the reference data Series
consists of m values representing the most favored responses.
2.
Generate comparison data series.
= (di1, di2, ..., dim)
Where i = 1,2,..., k. k is the number of scale
items. So there will be k comparison data series and each comparison data
series contains m values.
3. Compute the difference data series Δi.
Δi = (|d01
– di1| , |d02 – di2| , ..., |d0m –
dim|)
4. Find
the global maximum value Δmax and minimum value Δmin in the difference data
series.
Δmax =∀imax (max Δi)
Δmin =∀imin (min Δi)
5. Transform each data point in
each difference data series to grey relational coefficient. Let γi(j)
represents the grey relational coefficient of the jth data point in the ith difference
data series, then
γi(j)
= Eq .1
Where Δi(j) is the jth
value in Δi difference data series. ς is a value between 0 and 1.
The coefficient ς is used to compensate the effect of Δmax should Δmax be an extreme value in the
data series. In general the value of ς can be set to 0.5.
6. Compute grey relational
grade for each difference data series. Let Γi represent the grey relational grade for
the ith scale item and assume that data points in the series are of
the same weights 1, then
=
Eq
.2
The magnitude of Γi reflects the
overall degree of standardized deviance of the ith original data
series from the reference data series. In general, a scale item with a high
value of Γ indicates that the respondents, as a whole, have a high degree of
favored consensus on the particular item.
7. Sort Γ values into either descending or ascending order to
facilitate the managerial interpretation of the results. This is brief procedure for the grey
relational analysis. Now discuss in detailed the Grey theory and method as
follows:
Table3: Grey Relational Analysis calculations
as per eqs.1&2
|
Experiment
No. |
Comparability
sequence |
Grey
Relational Coefficient |
Grey Relational Grade |
||
|
Ra |
Ct |
Ra |
Ct |
||
|
1 |
0.7789 |
0.8076 |
0.6934 |
0.7221 |
0.7077 |
|
2 |
0.2526 |
0.6923 |
0.4008 |
0.6190 |
0.5099 |
|
3 |
0.0736 |
0.0000 |
0.3505 |
0.3333 |
0.3419 |
|
4 |
1.0000 |
0.3846 |
1.0000 |
0.4482 |
0.7241 |
|
5 |
0.7894 |
0.3076 |
0.7036 |
0.4193 |
0.5614 |
|
6 |
0.7368 |
0.1538 |
0.6551 |
0.3714 |
0.5132 |
|
7 |
0.5263 |
1.0000 |
0.5135 |
1.0000 |
0.7567 |
|
8 |
0.0000 |
0.7692 |
0.0000 |
0.6842 |
0.5087 |
|
9 |
0.1578 |
0.0769 |
0.3725 |
0.3513 |
0.3619 |
Table.4: Average
grey relational grade for factors and levels of the experiment
|
Factor/Level |
1 |
2 |
3 |
|
A |
0.5198 |
0.5995 |
0.5424 |
|
B |
0.5968 |
0.5266 |
0.4056 |
|
C |
0.5996 |
0.5319 |
0.5533 |
4. RESULTS&DISCUSSIONS
According
to the performed experiment design it is clearly observed from Table 3 and Figure
3 that the milling process parameters setting of experiment no. 7 has the
highest grey relational grade. Thus the experiment no7 gives the best
multi-performance characteristics among the 9 experiments.
The
response table of Taguchi method was employed here to calculate the average
grey relational grade for each factor level. The procedure was to group the
relational grades firstly by factor level for each column in the orthogonal
array and then to average them.
Since
the grey relational grades represented the level of correlation between the
reference and comparability sequences, the larger grey relational grade means
the comparability sequence exhibits a stronger correlation with reference
sequence. Therefore, the comparability sequence has a larger value of grey
relational grade for average surface roughness and chip thickness.
Based
on this premise the study selects the level that provides the largest average
response. In Table 4, A3 B1 C3 shows the largest value of grey relational grade
for factors A, B and C respectively. Therefore A3 B1 C3 is the condition for
the optimal parameter combination of the milling to minimize average surface
roughness and chip thickness.
The
influence of each cutting parameter can be more clearly presented by means of
the grey relational grade graph. It shows the change in the response, when the
factors go for their level 1 to level 3. The response graph for the milling parameters
are drawn from data of Table 4 and depicted in Figure 4, in this figure, the
greater values average grey relational grades give the low surface roughness
and chip thickness.
Figure 3:
Graph for grey relational grade
Figure 4:
Grey relational grade graphs for milling parameters
5. CONCLUSIONS
The
Grey relational analysis based on an orthogonal array of the Taguchi methods
was a way of optimizing the process parameters in milling for Al6351 alloy. The
analytical results summarized as follows:
1.
From the response table of the average grey relational grade, it is found that
the largest value of the GRA for the spindle speed of 1000 rpm, the feed rate
of 50 mm/min and depth of cut 0.7 mm. It is the recommended levels of the
controllable parameters for the process of milling as the minimization of
average surface roughness and chip thickness.
2.
The order of the importance of influential factors based on the Taguchi
response table in sequence is chip thickness, spindle speed and feed rate.
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