prediction and optimization of tool wear on a22e...

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.

[email protected],

[email protected],

[email protected]

[email protected]

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



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.



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



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.



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.



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



Fig. 4. Surface interaction plot of Spindle Speed and Feed rate

Fig. 5. Surface interaction plot of Spindle Speed and Depth of cut



Fig. 6. Surface interaction plot of Spindle Speed and End Relief Angle

Fig. 7. Surface interaction plot of Feed rate and Depth of cut



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°



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



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

http://link.springer.com/search?facet-author=%22Yong+Huang%22

http://link.springer.com/search?facet-author=%22Steven+Y.+Liang%22

http://link.springer.com/journal/170/24/9/page/1

http://link.springer.com/search?facet-author=%22Yong+Huang%22

http://link.springer.com/search?facet-author=%22Steven+Y.+Liang%22

http://www.sciencedirect.com/science/article/pii/S004316480400376X

http://www.sciencedirect.com/science/article/pii/S004316480400376X

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