Improvement in surface quality with high production rate using taguchi method and gra

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME
138
IMPROVEMENT IN SURFACE QUALITY WITH HIGH
PRODUCTION RATE USING TAGUCHI METHOD & GRA
NEERAJ KUMAR1
, VIJAY KUMAR2
1
Assistant Professor, Dept. of Mechanical Engg., SBTC Jaipur, India,
2
Assistant Professor, Dept. of Mechanical Engg., VIER, Vadodara, India
ABSTRACT
Metal matrix composites possess significantly improved properties including high specific
strength, damping capacity, specific modulus and wear resistance compared to unreinforced alloys.
There has been an increasing interest in composites containing low density and low cost
reinforcements with good properties. Among these reinforcements SiC is found to be chemically
compatible with aluminum forming a sufficiently strong bond with the matrix. There are several
methods of manufacture of MMCs. Of these, the stir casting method is very popular due to its unique
mixing property.
In the present investigation an attempt is made to evaluate the effect of certain cutting
variables on surface roughness and material removal rate in end milling of aluminium alloy (Al
6061) – SiC metal matrix composites. The experiments are conducted based on four factors, three
levels. The results are analyzed by Taguchi design method. The equation to the response surface is
developed by software package MINITAB-14. The goodness of fit is examined using the Analysis of
Variance (ANOVA). The graphs of S/N ratios shows that the best surface finish is associated with
the lowest level of SiC percentage in the metal matrix followed by the lowest level of speed, medium
level of feed and medium level of depth of cut. Also maximum material removal rate is obtained at
highest level of depth of cut followed by medium level of feed rate and SiC percentage and low level
of speed. Gray relation results shows the best parameters for combination of high surface finish and
high material removal rate are medium level of SiC followed by low level of speed medium level for
feed rate and high level of depth of cut.
Key words: ANOVA, Composite Material, Taguchi Method, S/N Ratio.
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 10, October (2014), pp. 138-147
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139
1. INTRODUCTION
Aim of end milling process is to removing material by two continuous motions. Those of the
tool and work pieces basically the tool has rotating motion (spindle speed) and the work pieces are
linear ones. For this study SiC percentage, spindle speed, feed rate and depth of cut taken as input
parameters and surface roughness and material removal rate as response parameters. J.A. Ghani et al.
[1] applied Taguchi method to optimize cutting parameters in end milling when machining hardened
steel AISI H13 with TiN coated P10 carbide insert tool. They take speed, feed rate and depth of cut
as cutting parameter and use an orthogonal array, signal-to-noise (S/N) ratio and analysis of variance
(ANOVA) to analyze the effect of these milling parameters. They concluded that the optimal
combination for low resultant cutting force and good surface finish are high cutting speed, low feed
rate and low depth of cut. E. Kılıckap et al. [2] Studied on tool wear and surface roughness in
machining of homogenized SiCp reinforced aluminium metal matrix composite. They select SiCp
aluminium MMC as working material and Two types of K10 cutting tool (uncoated and TiN-coated).
They show that in dry turning condition, tool wear is mainly affected by cutting speed and tool wear
is proportional to cutting speed. Tool wear was lower when coated cutting tool was used in
comparison to uncoated one. According to their analysis higher cutting speeds and lower feed rates
produced better surface quality. T.Tamizharasan et al. [3] performed experiment for effects of
geometrical parameters of cutting tool insert and analysis of output responses such as surface
roughness and Material Removal Rate (MRR) during machining AISI 1045 steel. They use Analysis
of Variance (ANOVA) to study the contribution of individual parameter on the output quality
characteristics. Amit Joshi et al. [4] performed experiment on CNC Vertical End Milling machine.
They take spindle speed, depth of cut, feed rate as cutting parameter and Material Removal Rate as
response. Experimental plan is performed by a Standard Orthogonal Array. The results of analysis of
variance (ANOVA) indicate that the proposed mathematical model can be adequately describing the
performance within the limit of factors being studied. The optimal set of process parameters has also
been predicted to maximize the MRR. They concluded that High feed rate, high depth of cut and
high spindle speed lead to higher value of resultant Material removal rate for the specific test range,
The depth of cut is the most dominant factor for material removal rate out of others two factors i.e.,
spindle speed & feed rate.
2. MATERIAL SELECTION
The work piece material chosen for this experimental study is aluminium silicon carbide
composite material. The main properties of composite materials are higher strength-to-density ratios,
higher stiffness-to-density ratios, better fatigue resistance, better elevated temperature properties,
better wear resistance, reduced density, higher strength and lower expansion ratio [5].
Among all the available manufacturing processes for composite casting stir casting is
generally accepted as a particularly promising route because of its simplicity, flexibility and
applicability to large quantity production [6]. In a stir casting process, the reinforcing particles are
distributed into molten matrix by mechanical stirring. Stir casting is suitable for manufacturing
composites with up to 30% volume fractions of reinforcement [7]. So for this research material is
casted by stirring casting process. The casting setup for the casting is as shown in fig 1 & 2 below.

140
Fig.1: Stir casting setup Fig.2: Stirrer in pit furnace
Table 1: Chemical Composition of Al 6061 alloy
Component Al Mg Si Fe Cu Zn Ti Mn Cr Oth.
Amount
(%)
96.35 1.2 0.8 0.7 0.15 0.25 0.15 0.15 0.35 0.05
3. EXPERIMANTAL SETUP AND METHODOLOGY
The experiments are conducted on a CNC Vertical machine centre as shown in fig 3. Which
have Spindle speed 8000 rpm, cutting feed rate 5-10000 mm/min, and main motor power of 7.5 kW,
Table dimension 700*430 mm, Maximum loading capacity 500 kg and Maximum tool diameter 125
mm. After performing the machining process, surface roughness values of the machined surfaces are
recorded by using a Hommel surface roughness tester as shown in fig 4. Material removal rate is
determined by noting the time, length, width and depth of cut of work piece.
Fig.3: Cosmos V60 Vertical Machine Centre Fig.4: Hommel surface roughness tester
3.1 TAGUCHI TECHNIQUES
Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method
based on “ORTHOGONAL ARRAY” experiments which gives much reduced “variance” for the
experiment with “optimum settings” of control parameters. Thus the combination of Design of
Experiments with optimization of control parameters to obtain BEST results is achieved in the
Taguchi Method. "Orthogonal Arrays" reduce the number of experiments and Signal-to-Noise ratios
(S/N) serve as objective functions for optimization [8].The Taguchi method is best used when there
are an intermediate number of variables (3 to 50) control parameter and their levels are as shown in
table 2.

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp.
Table
The selection of L27 array is beca
The L27 orthogonal array is shown in Table 3.
3.2 ANNOVA METHOD
ANOVA is a statistically based, objective de
the average performance of groups of items tested. ANOVA helps in formally testing the
significance of all main factors and their interactions by comparing the mean square against an
estimate of the experimental errors at specific confi
4. RESULTS AND DISCUSSION
For this experiment surface roughness and material removal rate are taken as output variable
and their values determines by experiment are as shown below
Mechanical Engineering and Technology (IJMET), ISSN 0976
6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME
141
Table-2: Control parameters and their levels
array is because of its suitability for four factors
array is shown in Table 3.
Table-3: L27 Orthogonal Array
ANOVA is a statistically based, objective decision-making tool for finding
estimate of the experimental errors at specific confidence levels [10].
RESULTS AND DISCUSSION
and their values determines by experiment are as shown below.
Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
© IAEME
use of its suitability for four factors with three Levels [9].
making tool for finding any differences in

4.1 ANALYSIS OF SURFACE ROUGHNESS
The surface roughness value for any product should be low so smaller the better condition is
used to determine the Taguchi S/N ratio.
S/N ratio ൌ െ10logଵ଴ሺ
ଵ
୬
∑ y୧
ଶ
ሻ.
Where i=1 to n & n = number of replications
The values of S/N ratio for surface roughness are as shown in table 4.
Table
The optimum level of parameters are decided by calculating average value of S/N ratio
corresponding to each level of parameters of surface r
Table-5: Response table of S/N ratio
From the S/N ratio response
identified by selecting the highest difference value from each factor. In this case, the most significant
factor that has an effect on surface roughness are SiC % (A)
142
4.1 ANALYSIS OF SURFACE ROUGHNESS
value for any product should be low so smaller the better condition is
used to determine the Taguchi S/N ratio.
n = number of replications
/N ratio for surface roughness are as shown in table 4.
able-4: S/N Ratio for surface roughness
corresponding to each level of parameters of surface roughness which are as shown in T
Response table of S/N ratio for each level of each factor
From the S/N ratio response as shown in Table, the best combination of parameters can be
surface roughness are SiC % (A) followed by speed (B) feed rate C) and
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value for any product should be low so smaller the better condition is
oughness which are as shown in Table 5.
for each level of each factor
, the best combination of parameters can be
speed (B) feed rate C) and

depth of cut (D). Hence optimum condition can be represented by A
surface roughness main effect plot is dr
fig 5.
Fig.5
To determining the contribution of parameters on surface roughness analysis of variance is
performed and outcomes are tabulated in T
Table-6:
This results shows that the SiC %
spindle speed by 13.67%, feed rate
significant parameter contributes to improve surface finish in the
rate and depth of cut only have small effects towards surface finish.
4.2 ANALYSIS OF MATERIAL REMOVAL RATE
For better productivity the material removal rate should be maximum so Taguchi larger the
better condition is used for this analysis
S/
Where i=1 to n & n = number of replications
143
depth of cut (D). Hence optimum condition can be represented by A1B1C2D2
surface roughness main effect plot is drawn by using MINITAB14 software which is as shown
Fig.5: Main effect plot for S/N ratio
and outcomes are tabulated in Table 6.
: Analysis of variance for surface roughness
This results shows that the SiC % contribution the most by 59.79% and this is followed by
%, feed rate 6.73% and depth of cut 3.44%. This proves that SiC% is the most
significant parameter contributes to improve surface finish in the process while spindle speed, feed
rate and depth of cut only have small effects towards surface finish.
4.2 ANALYSIS OF MATERIAL REMOVAL RATE
d for this analysis.
/N ratioሺηሻ ൌ െ10logଵ଴ሺ
1
n
෍
1
y୧
ଶሻ
n = number of replications
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2.From the response of
awn by using MINITAB14 software which is as shown in
% and this is followed by
%. This proves that SiC% is the most
process while spindle speed, feed

The values of S/N ratio for surface roughness are as shown in T
Table 7:
corresponding to each level of parameters of surface roughness which are as shown in
Table-8: Response table of S/N ratio for each level of each factor
factor that has an effect on material removal rate are depth
SiC% (A) and speed (B). Hence optimum condition can be represented by A
response of surface roughness main effect plot is drawn by using MINITAB14
fig.6.
144
face roughness are as shown in Table 7.
: S/N ratio for Material removal rate value
corresponding to each level of parameters of surface roughness which are as shown in
factor that has an effect on material removal rate are depth of cut (D) followed by fee
Hence optimum condition can be represented by A
response of surface roughness main effect plot is drawn by using MINITAB14
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corresponding to each level of parameters of surface roughness which are as shown in Table 8.
D) followed by feed rate (C),
Hence optimum condition can be represented by A2B1C3D3.From the
response of surface roughness main effect plot is drawn by using MINITAB14 software as shown in

Fig.
To determining the contribution of parameters on material removal rate analysis of variance
is performed and outcomes are tabulated in T
Table-9: Analysis of variance for
This results shows that the
by feed rate by 20.89%, SiC percentage
most significant parameter contributes to improve
rate, SiC percentage and spindle speed,
5. GRAY RELATION ANALYSIS
GRA is effective tool for solving the complicated interrelationship among the designated
performance characteristics. It also
problems. In gray relational analysis the complex multiple response optimization problem can be
simplified into optimization of single
145
Fig.6: Main effect plot for S/N ratio of MRR
and outcomes are tabulated in Table 9 below.
Analysis of variance for material removal rate
the Depth of cut contribute the most by 72.17
percentage 2.36% and speed 1.69%. This proves that
most significant parameter contributes to improve material removal rate in the process while
percentage and spindle speed, only have small effects towards material removal rate
5. GRAY RELATION ANALYSIS
performance characteristics. It also provides an efficient solution to multi-input and discrete data
simplified into optimization of single response gray relational grade [11].
© IAEME
material removal rate
.17% and this is followed
. This proves that depth of cut is the
in the process while feed
material removal rate.
input and discrete data

Fig.7: Graphical representations of steps to determine GRG
For a product good surface finish and high material removal rate required hence, equal
weights are used here for determining weighted gray relational grades for MRR and roughness.
Based on this combination of weights gray relational coefficient of the individual quality
characteristics and ranks of the experiments are determined. The gray relational grades found for
various experiments are listed in Table
Table
From Table 10, it is found that input parameters used for experiment number 12 gives
optimal result among all input parameters used for experiments.
146
Graphical representations of steps to determine GRG
good surface finish and high material removal rate required hence, equal
on of weights gray relational coefficient of the individual quality
various experiments are listed in Table 10.
Table-10: Gray relational grades for MRR and Ra
, it is found that input parameters used for experiment number 12 gives
optimal result among all input parameters used for experiments.
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Graphical representations of steps to determine GRG
good surface finish and high material removal rate required hence, equal
on of weights gray relational coefficient of the individual quality
MRR and Ra
, it is found that input parameters used for experiment number 12 gives

147
6. CONCLUSION
1. For surface roughness, the best level of input parameters are percentage of SiC5%, speed
2500 rpm, and feed rate 75 mm/min depth of cut 1 mm.
2. The best-input parameters for MRR are percentage of SiC 10%, speed 2500 rpm, feed rate
100 mm/min and depth of cut 1.5 mm.
3. The most significant factor that has an effect on surface roughness are SiC % (59.79%)
followed by speed (13.67%), feed rate (6.73%) and depth of cut (3.44%).
4. The most significant factor that has an effect on material removal rate are depth of cut
(72.17%) followed by feed rate (20.89%), SiC% (2.36%) and speed (1.69%).
5. Optimum parameters according to GRA are 10% SiC, speed 2500 rpm, feed rate, 100
mm/min depth of cut, 1.5 mm
REFERENCE
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