Post on 26-May-2018
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 53
150405-2727-IJMME-IJENS © October 2015 IJENS I J E N S
Prediction and Optimization of Tool Wear On A22E
(Bimetal Bearing Material) Using RSM and Genetic
Algorithm 1R.Babu,
2 Dr. D. S. Robinson Smart,
3 Dr. G.Mahesh,
4M. Shanmugam
1 Research Scholar, Department of Mechanical Engineering, Karunya School of Mechanical sciences, Karunya University,
Coimbatore, Tamil Nadu, India. 2.
Professor, Department of Mechanical Engineering, Karunya School of Mechanical sciences, Karunya University, Coimbatore,
Tamil Nadu, India. 3 Professor,
Department of Mechanical Engineering, Sree Sakthi Engineering
College, Coimbatore, Tamil Nadu, India. 4Deputy General Manager (Operations), Bimetal Bearings Limited, Coimbatore, Tamil Nadu, India.
1mailbabumail@gmail.com,
2smart@karunya.edu,
3doctorgmahesh@gmail.com
4shanmugam355@rediffmail.com
Abstract-- Tool wear is an important criterion in hard finish
facing operation, which increases the temperature on the work
piece, vibration, cutting force and decreases the surface
roughness. In this present study, spindle speed, feed rate, depth
of cut and end relief angle are taken as an input parameters.
Experiment is conducted on A22E Bimetal bearing material
using M42 HSS tool material in finish hard facing and tool wear
was measured using tool maker’s microscope. Design of
Experiments (DoE) methodology is used to conduct the
experiment. Response Surface Methodology is used to predict the
tool wear. The second order mathematical model in terms of
machining parameters was developed. The direct and interaction
effect of the machining parameters with tool wear were analyzed,
which helped to select process parameter in order to reduce tool
wear which ensures quality of the facing operation.
Index Term-- Spindle speed, Feed rate, Depth of cut, End Relief
angle, Tool wear, Response Surface Methodology (RSM),
Genetic Algorithm (GA).
1. INTRODUCTION
In the recent scenario, due to the competitive marketing
strategy the major goal of the manufacturing industry is to
increase productivity and product quality with lesser
production time. The quality of a product is strongly
associated with the condition of the cutting tool. Mainly
cutting tool plays a major role in manufacturing due to wear
and breakage, so tool wear is found to have a direct impact on
the quality of the product such as surface finish, dimensional
accuracy and cost of the finished product is increased due to
tool failure. The tool failure and the detection of tool failures
are essential to improve manufacturing quality and to increase
the productivity. The author [1] defined an empirical
relationship between tool life and cutting speed (VTn = C) and
C are coefficients.
Cutting tool life is one of the most important economic
considerations in metal cutting. In roughing operations, the
tool material, the various tool angles, cutting speed, and feed
rates are usually chosen to give an economical tool life.
Clearly, any tool or work material improvement that increases
tool life without causing unacceptable drops in production will
be beneficial. To form a basis for such improvements, efforts
have been made to understand the behavior of the tool, how it
physically wears, the wear mechanisms, and forms of tool
failure. Cutting parameters, work piece material, cutting tool
geometry have a crucial influence on the accomplishment of
desired product quality, tool wear, surface roughness, cutting
force and temperature rise in tool and work piece etc. During
turning and facing operation, friction between cutting tool and
work piece materials plays a key role in temperature rise, wear
etc. Among the above, tool wear has been found to be a most
influencing factor. CNC machining centers play a major role
in machining industry, even though spindle speed, feed rate,
depth of cut were already programmed before machining but
the machining performance and product quality are not
guaranteed up to standard level. Therefore, the optimum
turning and facing operation have to be accomplished.
The dynamic performance of rotating components is highly
influential on efficiency of any rotating machine. Bearings are
considered as the heart of rotating machinery [2]. Bearing play
a major role in industries and automobile sectors. A lot of
research is under processing by considering different
materials, tools etc and different combinations of machining
parameters. The authors [3] developed a methodology to
optimize hardened 52100 bearing steel using a low CBN
content insert to optimize the crater wear rate. The author
suggested that adhesion is the main wear mechanism. Flank
tool wear model [4] in finish turning of hardened 52100
bearing steel, tool performance is evaluated by considering the
parameters such as cutting speed, feed, and depth of cut. The
analysis of variance was carried out to investigate the cutting
conditions, and suggested that cutting speed plays a dominant
role in determining the tool performance. The authors [5]
developed a mathematical model by considering cutting
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parameters using CBN cutting tools in finish hard turning
operation of hardened 52100 bearing steel. The authors
suggest that the adhesion is the dominant wear mechanism
within the range of cutting conditions. The performance of
PCBN tool in the finish turning of GCr15 bearing steel was
investigated to determine the influence of the work piece
hardness on changes in cutting temperature and tool wear
characteristics [6]. An experimental investigation has been
done to clarify the effects of tool nose radius and tool wear on
residual stress distribution of hard turning of bearing steel (JIS
SUJ2) by using CBN tools with different nose radius, results
the tool nose radius affects the residual stress distribution
significantly[7]. The authors [8] conducted a simulation
analysis of high speed hard turning of AISI 52100 bearing
steel to study the cutting parameters of speed, feed, cutter
geometry and work piece hardness results cutting force and
feed force increase with increasing feed, tool edge radius,
negative rake angle and work piece hardness. The authors [9]
conducted an experiment to study the hard turning by using
CBN tool of AISI 52100 bearing steel. The main objectives
focus on tool wear and forces. The relationship between
cutting parameters are analyzed using RSM by considering
cutting speed, feed rate and depth of cut and machining output
variables such as surface roughness, cutting forces were
analyzed and modeled. The depth of cut plays a major role
that influence cutting forces as compared to the feed rate and
cutting speed. The authors [10] suggested that the cutting
speed is the most influencing factor which effects tool life of
turning of hardened 100Cr6 and PCBN cutting tools. The
experiment was conducted using CBN insert of finish turning
of hardened 52100 bearing steel. The objective of this study is
to present a analytical methodology to find the CBN tool flank
wear rate [11]. An orthogonal hard turning tests were
conducted to study the effects of flank tool wear and cutting
parameters (cutting speed and feed rate), on white and dark
layer formation in hardened AISI 52100 bearing steel, using
PCBN inserts [12]. An extensive study has been performed to
investigate the tool-wear mechanisms of CBN cutting tools in
finish turning of the X155CrMoV12 (AISI D2) cold work
steel, X38CrMoV5 (AISI H11) hot work steel, 35NiCrMo16
hot work steel and 100Cr6 bearing steel (AISI 52100). A large
variation in tool-wear rate has been observed in machining of
these steels [13]. The main objective of the present study is to
investigate the effects of process parameters (cutting speed,
feed rate and depth of cut) on performance characteristics (tool
life, surface roughness and cutting forces) in finish hard
turning of AISI 52100 bearing steel with CBN tool. The
optimization was carried out using ANOVA and RSM. The
results show that feed rate and cutting speed strongly influence
the surface roughness and tool life [14]. The authors [15]
concluded that the cutting force was 50% larger and feed and
thrust forces were 100% larger when turning AISI 52100 ball
bearing steel of hardness 63 HRC as compared to turning the
same material having hardness 32 HRC. The authors [16]
conducted an experiment to model the relationship between
tool wear and cutting parameters in turning processes. An
experiment was conducted to clarify the effects of tool nose
radius and tool wear on residual stress distribution in hard
turning of bearing steel JIS SUJ2. The results obtained in this
study shows that the tool nose radius affects the residual stress
distribution significantly [17]. The authors [18] conducted an
experiment to find the CBN tool wear in hard turning, results
adhesion is the dominant wear mechanism in turning hardened
52100 bearing steel with a hardness of 62 HRC using a low
CBN content tool. An experimental study was carried out by
using CBN tool during the turning of AISI 52100 bearing
steel. The flank wear rate is low when turning at low cutting
speed; the flank wear rate becomes quite large and sensitive to
the feed rate [19]. The model was proposed and conducted an
experiment in turning hardened 52100 bearing steel using the
KD050 low CBN content insert. The CBN tool crater wear
rate is formulated and validated over a wide range of cutting
conditions [20]. The authors [21] presented a predictive model
based on the artificial neural network (ANN). An experiment
was conducted on hard machining of 52100 bearing steel, and
the numerical results shows that more compressive residual
stress in both axial and circumferential direction of the
machined surface were obtained if higher values of the feed
rate were chosen. The authors [22] proposed a model and
validated through experimentally in finish turning of hardened
52100 bearing steel using a low CBN content tool, results
suggest that adhesion is the dominant wear mechanism within
the range of conditions that were investigated. An
experimental investigation was carried out by using PCBN
inserts.58 to machine finish hard turning of AISI 52100
bearing steel and suggested that the chamfer angle has a great
influence on the cutting forces and tool stresses. All cutting
force components increase with an increase of the chamfer
angle [23]. An experimental investigation has been conducted
to clarify the effects of tool nose radius and tool wear on
residual stress distribution in hard turning of bearing steel JIS
SUJ2 and suggested that the tool wear increases, the residual
stress machined surface increases [24].
Various researchers have developed the surface roughness
predictive models for the conventional hard turning of bearing
materials. From the literature sources, it is found that the
machining of A22E (BIMETAL BEARING MATERIAL)
metal matrix composite is an important area of research, there
is a limited research available in hard turning and facing
operation of bearing materials on surface roughness.
BIMETAL Bearings are primarily used in engines of
automobiles. The bearings are meant to reduce the friction
between the moving parts of the engine crankshaft or crank
shaft. The construction of Bimetal metal bearing consists of
three layer structure consisting of strong steel backing. The
back provides bearing rigidity and its press fit under severe
conditions of increased temperature and cycling loads, a thin
inter layer and lining of an aluminium alloy. Bimetal bearings
are most often are produced as a combination of the following
materials:
Steel with white metals
Steel with sintered bronze
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Steel with cast bronze
Steel with aluminium alloys
Fig. 1. Aluminium alloy bearing (BIMETAL Bearing)
In the present study, an attempt has been made to investigate
the tool wear by considering the process parameters such as
spindle speed, feed rate, depth of cut and end relief angle in
hard facing operation using Response surface Methodology
(RSM) approach. This methodology helps to obtain best
possible cutting conditions and tool geometry in dry facing of
A22E bearing material using M42 HSS tool material. The
mathematical model is developed using Design Expert 6.0
package and also been tested by the analysis of variance test
(ANOVA).
III. INFLUENCE OF END RELIEF ANGLE ON SINGLE
POINT CUTTING TOOL
The angle between front surface of the tool & line normal to
base of the tool is known as End relief angle. End relief angle
is used to minimize physical interference. If the relief angles
are too large, the cutting edge will be weakened and in danger
of breaking. If the cutting relief angle is too small, it causes
the wear on the flank of the tool, thereby, significantly
reducing the tool life. In general, for hard and tough material,
the relief angle should be 6 to 8 degrees for HSS tools and 5 to
7 degrees for carbide tools. For medium steels, mild steels,
cast iron, the relief angle should be 8 to 12 degrees for HSS
tools and 5 to 10 degrees for carbide tools. For ductile
materials such as copper, brass, bronze and aluminium, ferritic
malleable iron, the relief angle should be 12 to 16 degrees for
HSS tools and 5 to 14 degrees for carbide tools. The authors
finally concluded that larger relief angle generally tend to
produce a better surface finish [25].
Fig. 2. Single point cutting tool
IV. OPTIMIZATION BY USING GA
A Genetic Algorithm (GA) is a search technique used in
computing to find true or approximate solutions to
optimization and search problems. Genetic algorithms are
categorized as global search heuristics. Genetic algorithms are
a particular class of evolutionary algorithms that use
techniques inspired by evolutionary biology such as
inheritance, mutation, selection, and crossover. The evolution
usually starts from a population of randomly generated
individuals and happens in generations. In each generation, the
fitness of every individual in the population is evaluated,
multiple individuals are selected from the current population
(based on their fitness), and modified (recombined and
possibly mutated) to form a new population.
The most important components in a GA consist of:
Individual - Any possible solution
Population - Group of all individuals
Search Space-All possible solutions to the problem
Chromosome-Blueprint for an individual
Trait - Possible aspect (features) of an individual
Allele - Possible settings of trait (black, blond, etc.)
Locus - The position of a gene on the chromosome
Genome - Collection of all chromosomes for an individual
V. RESPONSE SURFACE METHODLOGY (RSM)
Response Surface Methodology (RSM) is a collection of
mathematical and statistical techniques that are useful for the
modelling and analysis of problems in which a response of
interest is influenced by several variables and the aim is to
optimize this response [26]. The RSM method was well
adapted to develop an analytical model for the complex
problem.
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Table I
PROCESS FACTORS AND THEIR LEVELS
Table II
EXPERIMENTAL DESIGN MATRIX AND RESPONSE FACTORS
Run Spindle Speed
(A)
rpm
Feed Rate (B)
mm/rev
Depth of Cut
(C)
mm
End Relief Angle
(D)
degree
Tool Wear(T)
(Observed Value) mm
1 500 0.06 1.6 14 0.131
2 500 0.06 1.2 10 0.156
3 600 0.12 1.4 12 0.254
4 500 0.1 1.2 10 0.103
5 700 0.06 1.2 14 0.142
6 600 0.08 1.4 12 0.032
7 700 0.1 1.2 14 0.122
8 600 0.08 1.4 16 0.145
9 600 0.08 1.4 12 0.058
10 500 0.06 1.6 10 0.126
11 700 0.1 1.2 10 0.163
12 600 0.08 1.4 8 0.068
13 700 0.06 1.6 14 0.112
14 700 0.1 1.6 14 0.147
15 500 0.1 1.6 10 0.139
16 600 0.08 1.4 12 0.058
17 500 0.1 1.2 14 0.138
18 400 0.08 1.4 12 0.147
19 700 0.06 1.6 10 0.036
20 500 0.1 1.6 14 0.211
21 700 0.1 1.6 10 0.105
22 600 0.08 1.4 12 0.029
23 600 0.08 1.8 12 0.057
24 600 0.06 1.4 12 0.084
25 700 0.06 1.2 10 0.038
26 800 0.06 1.4 12 0.104
27 600 0.08 1.4 12 0.142
28 600 0.04 1.4 12 0.128
29 500 0.06 1.2 14 0.248
30 600 0.08 1 12 0.157
With this analytical model, an objective function with
constraint can be created, and computation time can be saved.
The RSM seeks for the relationship between design variable
and response through statistical fitting method. The response,
tool wear (T) can be expressed as function of process
Variables Unit Coded Variable Level
Lowest Low Centre High Highest
-2 -1 0 +1 +2
Spindle Speed rpm 400 500 600 700 800
Feed Rate mm/rev 0.04 0.06 0.08 0.1 0.12
Depth of Cut mm 1 1.2 1.4 1.6 1.8
End Relief Angle Degree 8 10 12 14 16
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parameters. The end relief angle (D), spindle speed (A), feed
rate (B) and depth of cut (C) as shown in Eq. (1)
Tool wear (T) = ψ(Diu, Aiu, Biu, Ciu) + eu-----(1)
where,
ψ - response surface,
eu - residual,
u - no of observations in the factorial
experiment
iu - represents level of the ith factor in the
uth observation.
When the mathematical form of ψ is unknown, this function
can be approximated satisfactorily within the experimental
region by polynomials in terms of process parameter variable.
The selected design matrix, is a three-level, four factor central
composite rotatable factorial design (CCD) consisting of 30
sets of coded conditions. The ranges of all the parameters were
fixed by conducting trial runs. This was performed by varying
one of the parameters while retaining the rest of them as
constant values. The upper limit of a given parameter was
coded as (+2) and the lower limit was coded as (–2).The
intermediate levels of -1, 0, +1 of all the variables have been
calculated by interpolation. Thus, all the 30 experimental runs
to allow the estimation of the linear, quadratic and two way
interactive effects of the process parameters.
Xi = 2(2X − (Xmax + Xmin)) ------------- (2)
(Xmax − Xmin)
Where,
Xi - The required coded value of a variable X
X - Is any value of the variable from Xmin to
Xmax.
Xmin - Is the lower limit of the variable
Xmax - Is the upper limit of the variable
The intermediate values coded as −1, 0, and 1.
VI. EXPERIMENTAL SETUP
The test specimen Bimetal Bearing of size 95 mm diameter
and thickness 2 mm are selected for experimental purpose.
The outer side of bimetal bearing consists of steel and inner
side is made up of aluminum. Bimetal bearing is softest and it
consists of 6 - 20% tin, 1% copper, 2- 4% silicon and highly
strengthened by nickel and other elements. These type of
bearings used in the passenger cars with low and medium load
gasoline engines. The experiments were conducted on special
type of CNC lathe with M42 HSS single point facing tool
under dry condition. The machining operations were carried
out as per the conditions provided by the design matrix. The
tool wear was measured using tool maker’s microscope on the
flank surface of the single point turning tool specimen. The
machining of Bimetal Bearing experimental set-up is shown in
Fig.2.
Fig. 2. MACHINING OF BIMETAL BEARING AND TOOL.
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Table III ANOVA TABLE FOR THE PREDICTION OF SPINDLE VIBRATION
Analysis of variance table [Partial sum of squares - Type III]
Sum of
Mean F p-value
Source Squares df Square Value Prob > F
Model 0.072 14 5.173E-003 3.11 0.0184 significant
A-Spindle Speed 9.322E-003 1 9.322E-003 5.60 0.0318
B-Feed Rate 6.370E-003 1 6.370E-003 3.83 0.0693
C-Depth of cut 3.825E-003 1 3.825E-003 2.30 0.1502
D-End Relief angle 0.012 1 0.012 7.28 0.0165
AB 4.865E-003 1 4.865E-003 2.92 0.1079
AC 4.556E-005 1 4.556E-005 0.027 0.8708
AD 3.306E-005 1 3.306E-005 0.020 0.8898
BC 4.064E-003 1 4.064E-003 2.44 0.1389
BD 1.785E-003 1 1.785E-003 1.07 0.3167
CD 1.563E-006 1 1.563E-006 9.391E-004 0.9760
A^2 5.808E-003 1 5.808E-003 3.49 0.0814
B^2 0.026 1 0.026 15.77 0.0012
C^2 2.703E-003 1 2.703E-003 1.62 0.2218
D^2 2.635E-003 1 2.635E-003 1.58 0.2274
Residual 0.025 15 1.664E-003
Lack of Fit 0.016 10 1.621E-003 0.93 0.5724 not significant
Pure Error 8.745E-003 5 1.749E-003
Cor Total 0.097 29
VII. DEVELOPMENT OF MATHEMATICAL MODEL
The analysis is carried out with the experimental data using
Design Expert V 9.0.4 software of state ease. The model
is checked for its adequacy using ANOVA (analysis of
variance). Table VIII shows ANOVA table for the prediction
of tool wear (T). It is observed from the Table VIII that the
model is significant and the lack of fit is not significant which
infers the significance of the model. Values of Prob> F less
than 0.05 indicate the model terms as in significant and the
values greater than 0.10 indicate the model terms as not
significant. The Model F-value of 3.11 implies that the model
is significant. There is only a 0.25% chance that an F-
value this large could occur due to noise. The "Lack of Fit F-
value" of 0.93 implies the Lack of Fit is not significant relative
to the pure error. There is a 12.05% chance that a "Lack of Fit
F-value" this large could occur due to noise. Non-significant
lack of fit is good. The Fig.5 shows the Predicted Vs Actual
model. The regression equation obtains from the Design
Expert software in terms of actual factors are given:
Tool Wear (T) = +2.43364 -2.43646E-003* A -19.19687* B -
1.03552* C -0.023240* D +8.71875E-003 * A * B -8.43750E-
005 * A * C -7.18750E-006 * A * D +3.98438 * B * C -
0.26406 * B * D +7.81250E-004 * C * D +1.45521E-006 * A2
+77.31771 * B2 +0.24818 * C
2+2.45052E-003 * D
2.
Where,
A = Spindle speed in rpm
B = Feed rate in mm/rev
C = Depth of cut in mm
D = End Relief angle in degree
VIII. RESULTS AND DISCUSSION
A mathematical model was developed by DoE based
Response Surface Methodology (RSM) to predict the tool
wear by relating it with process parameters such as spindle
speed, feed rate, depth of cut and end relief angle. The residual
plot is shown in Fig.3.
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The interaction effect of these process parameters on tool wear
were discussed below. Fig. 4 shows the interaction effect of
feed rate and spindle speed on tool wear. As the increase in
spindle speed from 600 rpm to 800 rpm the tool wear value is
low, whereas the feed rate has the
inverse relationship on the tool wear. Therefore, higher
spindle speed and feed rate between 0.06mm to 1mm has to be
chosen as low tool wear. Fig.5 shows the interaction effect of
spindle speed and depth of cut on tool wear. It is evidenced
from the figure that at lower spindle speed and higher depth of
cut has a significant influence. The tool wear increases when
the spindle speed is higher and low depth of cut. Fig. 6 shows
the interaction effect of spindle speed and relief angle on tool
wear. From the Figure
Fig. 3. Residuals plot
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Fig. 4. Surface interaction plot of Spindle Speed and Feed rate
Fig. 5. Surface interaction plot of Spindle Speed and Depth of cut
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Fig. 6. Surface interaction plot of Spindle Speed and End Relief Angle
Fig. 7. Surface interaction plot of Feed rate and Depth of cut
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Fig. 8. Surface interaction plot of Feed rate and Relief angle
Fig. 9. Surface interaction plot of depth of cut and relief angle
is noted that the both spindle speed and relief angle are
influencing the tool. The tool wear is significantly higher
between 12° to 16° and also the similar result is observed
between 400 rpm to 500 rpm. From the result, the industry
preferring to fix the relief angle between 8° to 10°. Fig. 7
shows the interaction effect of feed rate and depth of cut on
tool wear. The tool wear is low when the feed rate is low and
depth of cut is higher. From the Fig.8 the higher feed rate and
higher relief angle, increases the tool wear whereas lower feed
rate and lower relief angle decrease the tool wear. It is
interesting to observe that if the relief angle between 8° to 10°
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and depth of cut between 1.4 mm to 1.6 mm will produce
lower tool wear as shown in Fig.9.
VIII. EVALUATION OF GA RESULTS
In this present study, the optimization wear was carried out to
minimize the cutting parameter based on Genetic Algorithm
methodology. The minimization of tool wear by using GA can
be expressed by the equation
Minimize: T (A, B, C, D)
Within ranges of cutting parameters,
400 rpm ≤ A ≤ 800 rpm
0.04 mm/rev ≤ B ≤ 0.12 mm/rev
1 mm ≤ C ≤ 1.8 mm
8 ° ≤ D ≤ 16°
The number of the initial population size, the type of selection
function, the crossover rate, mutation rate and the generations
to be considered to get the best optimal results. The Table 4
shows the GA operators. By solving the
optimization problem using MAT LAB 7.0, GA predicts the
optimum tool wear as 0.0180 mm for the machining of
Bimetal bearing in the selected cutting condition range. The
GA-predicted tool wear value (best fitness function) is
expected to be lower than the minimum tool wear value of the
experimental and regression models.
Table IV GA PARAMETERS
Parameters
setting Parameters
setting Population size 100
Scaling function Rank
Scaling function Rank
Function Stochastic uniform
Mutation function Gaussian
Mutation rate 0.1
Crossover function Scattered
Crossover rate 1.0
Generations 1000
IX. VALIDATION OF THE MODEL
Table II shows that a regression model developed using CCD
by the RSM of DoE. Fig.10 shows the comparison of
predicted vs experimental value of Tool wear from RSM of
DoE. Table 5 shows the comparison of predicted vs
experimental value of Tool wear. The GA-predicted optimum
conditions were further validated with physical measurements.
The percentage of error is found to be within ±2 % which
shows the validity of the model. The experimental results of
tool wear with the optimum cutting parameters (as predicted
by GA) shows the good agreement. Figure 10 shows the
performance of fitness value with generation and the best
individual performances of variables in coded form.
Fig. 10. PREDICTED VS EXPERIMENTAL VALUE
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Fig. 10. shows the performance of fitness value with generation and the best individual performances of variables in coded form.
Table V OPTIMIZED PROCESS PARAMETER PREDICTED BY GA
X. CONCLUSION
From the regression model developed using Central
Composite Design (CCD) by the Response Surface
Methodology (RSM) of Design of Experiments (DoE), the
experimental investigation has been done for facing operation
considering, the factors such as Spindle Speed, Feed Rate,
Depth of cut, and End relief angle to predict tool wear of
Bimetal Bearing, the following conclusions are drawn:
The Spindle Speed and Feed rate are the most important
parameters to be considered for tool wear compared to the
other factors such as depth of cut and end relief angle. The
better tool life was obtained at the end relief angle (8°-10°)
and spindle speed (600 rpm to 800 rpm). The tool wear is
minimum value at the region of feed rate (0.06mm - 0.1
mm).The tool wear is minimum at the region of depth of cut
(1.4 mm – 1.6 mm). Further GA predicts the optimum tool
wear as 0.0180 mm for the machining of Bimetal bearing in
the selected cutting condition range. The confirmatory result
shows good arguments for experimental vs predicted value.
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Trial Spindle
Speed
(A)
Feed
rate
(B)
Depth of
cut
(C)
End relief
angle
(D)
Confirmatory test for
Surface roughness
%
error
Predicted
GA model
Experimental Value
rpm mm/rev mm Degree µm µm
1 700 0.08 1.6 12 0.114 0.113 0.87
2 500 0.06 1.2 8 0.084 0.081 0.35
3 600 0.10 1.4 10 0.138 0.136 1.44
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 65
150405-2727-IJMME-IJENS © October 2015 IJENS I J E N S
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