Enhancing the Submersible Pump Rotor Performance by Taguchi Optimization Technique For Turning Cutting Parameters

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 3 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 701
ENHANCING THE SUBMERSIBLE PUMP ROTOR PERFORMANCE BY
TAGUCHI OPTIMIZATION TECHNIQUE FOR TURNING CUTTING
PARAMETERS
S. Om Prakash1, S. Pathmasharma2
1Assistant Professor (Senior Grade), Department Mechanical Engineering, United Institute of Technology,
Tamil Nadu, India
2Assistant Professor, Department of Mechanical Engineering, United Institute of Technology, Tamil Nadu, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Due to their low hysteresis losses silicon steel is
one of the most suitable materials for rotors. Silicon stamping
stacked together by pressure die casting process to form a
rotor. However, their uneven surface roughness creates
excessive static friction between rotor and stator. As the
friction increases the pump does not rotate which leads to the
pump failure. This necessitates a process optimization when
machining silicon steel stampings with HSS tool. This project
outlines an experimental study to achieve this by employing
Taguchi techniques. Combined effects of three cutting
parameters, namely cutting speed, feed rate and depth of cut
on performance measure surface roughness (Ra), were
investigated by employing an orthogonal array, signal-to-
noise ratio, and analysis of variance.
Key Words: Surface Roughness (Ra); orthogonalarrays;
Analysis of variance (ANOVA)
1. INTRODUCTION
Surface roughness is an important measure of the
technological quality of a product and a factor that greatly
influences manufacturing cost. The mechanism behind the
formation of surface roughnessisverydynamic,complicated,
and process dependent; it is very difficult to calculate its
value through theoretical analysis. Therefore, machine
operators usually use “trial and error” approaches to set-up
turning machine cutting conditions in order to achieve the
desired surface roughness. Obviously, the “trial and error”
method is not effective and efficient and theachievement ofa
desirable value is a repetitive and empirical process that can
be very time consuming.The dynamic natureandwidespread
usage of turning operations in practice have raiseda need for
seeking a systematic approach thatcanhelptoset-upturning
operations in a timely manner and also to help achieve the
desired surface roughness quality. [1]
Developments in cutting tools and machine tools in the last
few decades have made it possible to cut materials in their
hardened state [2]. High speed steel is considered to be one
of the most suitable tool materials for machining stampings
because of their high density. This high density affords it
incredible durability and hardness and shock and vibration
resistance while still allowing for its machinability into tools
and drill bits.
Surface finish is an important parameter in manufacturing
engineering. It is a characteristic that could influence the
performance of mechanical parts and production costs [3].
2. TAGUCHI METHOD
Taguchi techniques have been used widely in engineering
design [4]. The main trust of the parameter design consist a
plan of experiments with the objective of acquiring data in a
controlled way, executing these experiments and analysing
data, in order to obtain information about the behaviour of
the given process. The treatment of the experimental results
is based on the analysis of variance (ANOVA).
The use of the parameter design of the Taguchi method to
optimize a process withmultipleperformancecharacteristics
includes the following steps [6]
Identify the performance characteristics and select process
parameter to be evaluated. Determine the number of levels
for the process parameter and possible interactionsbetween
the process parameters.
 Select the appropriate orthogonal arrayandassignment
of process parameters
 Conduct the experiments based on the arrangement of
the orthogonal array.
 Calculate S/N ratio.
 Analyze the experimental results using the S/N ratio
and ANOVA.
 Select the optimal levels of process parameters.
 Verify the optimal process parameters through the
conformation test.
3. EXPERIMENTAL PROCEDURE
3.1. EQUIPMENT AND MATERIALS
The goal of this experimental work was to improve
the performance of the rotor by investigate the effects of
cutting parameters on surface roughness. In order for this,

cutting speed, feed rate and insert radius were chosen as
process parameters. The air gap between rotor and stator is
kept constant therefore depth of cut is not used in this paper.
The work material was silicon steel in the form of roundbars
with 70mm diameter and100mmcuttinglength.Siliconsteel
Stampings have a thickness of 0.4mm was stacked together
by pressure die casting process to form a rotor. Copper was
used in die casting process. The chemical composition of
silicon steel which was used in experiments as shown in
table 1
The turning tests were conducted in dry conditions on CNC
lathe having maximum spindle speed of 3500rpm and
maximum power of 15KW.
Table - 1: Chemical Composition of Silicon Steel
Fe C Si Mn P S Cr
96.52 .0408 2.33 .359 0.026 0.004
0.052
1
Mo Al Cu Ti V Pb
.0215 .216 .416 .0136 .0007 .0027
Table - 2: Assignment of the Levels to the Factors
Symbol Cutting parameters Level 1 Level 2 Level 3
A
B
C
Cutting speed(rpm)
Feed rate (mm/rev)
Insert radius (mm)
360
0.12
0.1
610
0.22
0.2
860
0.32
0.3
3.2. SELECTION OF CUTTING PARAMETERS AND
THEIR LEVELS
The initial cutting parameters were as follows: insert radius
of 0.2mm, feed rate of 0.25mm/rev and cutting speed of
550rev/min. Three levels were specified for each parameter
as given in table 2. The parameter levels were chosen within
the intervals recommended by thecuttingtool manufacturer.
4. DETERMINATION OF OPTIMAL CUTTING
PARAMETERS
4.1. DESIGN OF EXPERIMENTS
The orthogonal array chosen was L9, which has 9 rows
corresponding to the number of parameter combinations
(26 degrees of freedom), with 4 columns at three levels as
shown in table 2. The first column wasassignedtothecutting
speed, the second column to the feed rate,thethirdcolumnto
the depth of cut, and fourth column to the error.
Table - 3: Orthogonal Array L9 of Taguchi
Test
No.
Column
Cutting
Speed
Feed
Rate
Depth
of Cut
Error
1 1 1 1
2 1 2 2
3 1 3 3
4 2 1 1
5 2 2 2
6 2 3 3
7 3 1 1
8 3 2 2
9 3 3 3
Surface roughness measurements were performed by using
Kosaka Leap SE1200 sampling length of 2.3mm.
4.2. ANALYSIS OF SIGNAL-TO-NOISE (S/N) RATIO
There are three categories of performancecharacteristics, i.e.
the lower-the-better, the high-the-better, and the nominal-
the-better. To obtain optimal machining performance, the
lower-the-better performance characteristic for surface
roughness should be taken for obtaining optimal machining
performance. TheS/N ratioforlower-the-betterperformance
is specified by the following equation [1].
S/NL = -10log10 [1/n
2
1
(1/ )
n
i
i
y

 ] (1)
Where n, is the number of observations andyistheObserved
data.
Table 4 shows the experimental resultsforsurfaceroughness
and the corresponding S/N ratio using Eq. (1). For example,
the mean S/N ratio for cutting speed at level 1, 2 and 3 canbe
calculated by averaging the S/N ratios for the experiments1-
3, 4-6, and 7-9, respectively. The mean S/N ratio for each
level of the cutting parameters is summarized and called the
mean S/N response table for surface roughness (Table 5).In
addition the mean S/N ratio for nine experiments is also
calculated and listed in Table 5.
Table – 4: Experimental Results for Surface Roughness &
S/N Ratio
Test
No.
Cutting Parameter Level
Performance Measure
A B C
Cutting
Speed
Feed
Rate
Insert
Radius
Measured
Surface
Roughness
Calculated S/N
Ratio for Surface
Roughness
1 360 0.12 0.1 2.235 -6.98
2 360 0.22 0.2 4.546 -13.15
3 360 0.32 0.3 7.342 -17.32
4 530 0.12 0.2 2.432 -7.72
5 530 0.22 0.3 1.354 -2.63
6 530 0.32 0.1 0.912 0.8
7 860 0.12 0.3 1.846 -5.32
8 860 0.22 0.1 2.026 -6.13
9 860 0.32 0.2 4.428 -12.92

Table – 5: Response table mean S/N ratio for surface
roughness factor and significant interaction
Symbol
Cutting
Parameter
Mean S/N Ratio
Level 1 Level 2 Level 3 Max-min
A Cutting Speed -12.48 -3.18 -8.127 9.3
B Feed Rate -6.68 -7.3 -9.81 3.13
C Insert Radius -4.106 -11.26 -8.424 7.159
Total mean S/N ratio = -7.93
Table - 6: ANOVA Table for Surface Roughness
Source of
Variation
Degrees of
Freedom
Sum of
Squares
Mean
Square
F Ratio
Contribution
(%)
Cutting
Speed
2 129.5 64.75 4.23 50.93
Feed Rate 2 16.21 8.10 0.53 6.375
Depth of
Cut
2 77.94 38.97 2.55 30.65
Error 2 30.6 15.3 - 12.03
Total 8 254.27 - - 100
4.3. ANALYSIS OF VARIANCE
The purpose of the ANOVA is to investigate which of the
process parameters significantly affect the performance
characteristics. This is accomplished by separating the total
variability of the S/N ratios, which is measured by the sumof
the squared deviations from the total mean of the S/N ratio,
into contributions by each of the process parameters andthe
error. First, the total sum of the squared deviations SST from
the total mean of the S/N ratio η can be calculated as [5]
SST =
1
a
i
 2
.
1
( ..)
n
i
j
 

 (2)
Where, n is the number of experiments in orthogonal array
and ηi is the mean S/N ratio for jth experiment.
The total sum of the squared deviations SST is decomposed
into two sources: the sum of the squared deviations SSP due
to each process parameter and the sum of the squared error
SSe. SSP can be calculated as:
SSP = n
2
.
1
( ..)
a
i
i
 

 (3)
Where p represents one of the experiment parameters, i the
level number of this parameter p. a repetition of each level of
the parameter p. The sum of squares from error parameters
SSe is
SSe = SST – SSA - SSB – SSC (4)
The total degrees of freedom is DT = n - 1, where the degrees
of freedom of the tested parameter Dp = a - 1. The variance of
the parameter tested is VP = SSP/DP. Then, the F-value for
each design parameter is simply the ratio of the mean of
squares deviations to the mean of the squared error (FP =
VP/Ve). The corrected sum of squares SP can be calculated as:
SP = SSP - DP VP. (5)
The percentage of contribution C can be calculated as:
C= SP / SST (6)
Statistically, there is a tool called the F-test named after
Fisher to see which process parameters have a significant
effect on the performance characteristic.InperformingtheF-
test, the mean of the squared deviations SSm due to each
process parameter needs to be calculated. The mean of the
squared deviations SSm is equal to the sum of the squared
deviations SSd divided by the number of degrees of freedom
associated with the process parameter. Then, the F-value for
each process parameter is simply a ratio of the mean of the
squared deviations SSm to the mean of the squared error SSe.
Usually the larger the F-value, the greater the effect on the
performance characteristic due to the change of the process
parameter.
Table 6 shows the results of ANOVA for surface roughness. It
shows that the only significant factorforsurfaceroughnessis
cutting speed, which explains 50.93% of the total variation.
The next largest contribution comes from insert radius with
30.65%. The change of the feed rate in the range given by
Table 2 has an insignificant effect on surface roughness.
Therefore, based on the S/N and ANOVA analyses, the
optimal cutting parameters for surface roughness are the
cutting speed at level 2, the feed rate at level 3, and the insert
radius at level 1.
4.4. CONFIRMATION TESTS
Once the optimal level of the process parameters is selected,
the final step is to predict and verify the improvement of the
performance characteristic using the optimal level of the
process parameters. The estimated S/N ratio η using the
optimal level of the process parameters can be calculated as
[6]
 = ηn +
1
( )
q
i n
i
 

 (7)
Where, ηn is the total mean of the S/N ratio, qi, is the mean
S/N ratio at the optimal level, and q is the number of the
process parameters that significantly affect the performance
characteristic. The estimated S/N ratio using the optimal
cutting parameters for surface roughness can then be
obtained and the corresponding surface roughness can also
be calculated by using Eq. (1).Table 7 showstheresultsofthe
confirmation experiment using the optimal cutting

parameters of surface roughness. Good agreement between
the predicted machining performance and actual machining
performance is shown. The increase of the S/N ratiofrom the
initial cutting parameters to the optimal cutting parameters
is 8.348 dB. The improvement of the S/N ratio for the
individual performance characteristic is shown in Table 7.
Based on the result of the confirmation test, the surface
roughness is decreased 2.615 times, in the foregoing
discussion; the experimental results confirm the prior
parameter design for theoptimal cuttingparameterswith the
multiple performance characteristics in turning operations.
Table - 6: Confirmation Test
Initial Cutting
Parameter
Optimal Cutting Parameters
Prediction Experiment
Level A2B2C2 A3B2C1 A3B2C1
Surface Roughness
(μm)
2.954 0.87 1.13
S/N Ratio(dB) -9.408 1.20 -1.06
Improvement of S/N ratio = 8.348
CONCLUSIONS
The following conclusions can be drawn based on the results
of the experimental study on turning silicon steel stampings
with HSS tools:
 Taguchi’s robust orthogonal array design method is
suitable to analyze the surface roughness (Turning)
problem as described in this paper.
 It is found that the parameter design of the Taguchi
method provides a simple, systematic, and efficient
methodology for the optimization of the cutting
parameters.
 The interaction cutting speed/insert radius is the most
important of the other analyzed parameters. The
remaining parameters cutting speed/feed rateandfeed
rate/insert radius have no significant influence on the
surface roughness.
 In turning, use of lower cutting speed (510 rpm),
medium feed rate (0.22 mm/rev) and low insert radius
(0.1 mm) are recommended to obtain better surface
roughness for the specific test range.
 The improvement of surface roughness from initial
cutting parameters to the optimal cutting parametersis
about 261.5%.
 The static friction was reduced by implementing the
obtained parameters.
Further study could consider morefactors(e.g.,temperature,
depth of cut, tool life, etc.) in the research to see how the
factors would affect surface roughness.
REFERENCES
[1] Julie Z.Zhang, Joseph C.Chen, E. Daniel Kirby, Surface
roughness optimisation in an end-milling operation
using the Taguchi design method. J Mater Process
Technol 2007; 184: 233-239.
[2] Ersan Aslan, Necip Camuscu, Burak Birgoren, Design
optimization of cutting parameters when turning
hardened AISI 4140 steel (63 HRC) with Al2O3 + TiCN
mixed ceramic tool Materials and Design. Materials and
Design 2007; 28: 1618-1622.
[3] J. Paulo Davim, A note on the determination of optimal
cutting conditions for surface finish obtained in turning
using design of experiments, J Mater Process Technol
2001; 116: 305-308.
[4] M.Nalbant, H.Gokkaya, G.Sur, Application of Taguchi
method in the optimization of cutting parameters for
surface roughness in turning.MaterialsandDesign2007;
28: 1379-1385.

Enhancing the Submersible Pump Rotor Performance by Taguchi Optimization Technique For Turning Cutting Parameters

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Enhancing the Submersible Pump Rotor Performance by Taguchi Optimization Technique For Turning Cutting Parameters