30120130406029

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 6, November - December (2013), pp. 275-284
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)
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IJMET
©IAEME

PREDICTION AND OPTIMIZATION OF PROCESS PARAMETERS FOR
COPPER ALLOY TO CONTROL THE FLASH USING NON-TRADITIONAL
ALGORITHM
P. SHIVA SHANKAR
Department of Mechanical Engineering, University College of Engineering and Technology,
Mahatma Gandhi University, Nalgonda, Andhra Pradesh – 508001

ABSTRACT
Friction welding is a solid state bonding technique which utilizes the heat generated by the
faying surfaces. Friction welding is widely used for joining of similar and non- similar materials.
Friction welding process is generally a multi input and multi output process, so we require the
parameters to optimized for the sound weld. In present study the input variables are Friction
pressure(FT), Friction time (FT), Upset time (UT) and Upset pressure (UP) similarly the output
variables are considered to be Flash width (FW), Flash height (FH) and Flash thickness (FT).
Empirical relation is formulated using by design Expert. Integration of these techniques in order to
minimize the metal losses without sacrificing the Tensile Strength of the joints.
INTRODUCTION
Friction welding is a solid state welding process where the frictional heat is generated
between the two faying surfaces. The temperature is raised up to higher interface enough to cause the
two surfaces to be forged together under high forge pressure. In this friction welding we have two
types of principles i.e, continuous drive friction welding for the circular geometry and linear friction
welding for the other geometry like square, rectangular and etc. in continuous drive friction welding
one of the work piece is fixed in rotating spindle which is driven by the motor, while the other is
restricted form the rotation. the work piece in the spindle is rotated in a predetermined constant speed
of 1500 rpm and the other is brought together with in 3rd stage for the predetermined time or until a
preset amount of axial shortening takes place and then the Forge pressure is applied. Lastely the
rotation is stopped abruptly by the help of the braking force.

275


Fig 1: Steps involved in different phases

Fig 2: Different parameters variying with repect to the TIME
The input parameter that control the joints are Friction pressure (FP), Friction time (FT),
Upset time (UT) and Upset pressure (UP). The output variables are Flash width (FW), Flash height
(FH) and Flash thickness (FT). To get good quality of weld these parameters to be optimized.
Optimization of friction welding parameters will be time consuming if conventional technique is
employed, by concentrating on the single parameter whereas the other is kept constant. So the
optimization of the parameter in this work by Design of Experiment technique are used especially
TAGUCHI METHOD Orthogonal Array (L9). The main concentration of this study is to minimize
the loss of material due to flash by controlling the attributes like Flash width (FW), Flash height
(FH) and Flash thickness (FT) without compromising the Tensile Strength of the joint.
MIMUM [1] investigated the hardness variations and the microstructure at the interfaces of
steel welded joints. PAVENTHAN [2] investigated on the optimization of friction welding
parameters to get good tensile strength of dissimilar metals. ANANTHAPADMANABAN [3]
reported the experimental studies on the effect of friction welding parameters on properties of steel.
DOBROVIDOV [4] investigated the selection of optimum conditions for the friction welding of high
speed steel to carbon steel. SARALA UPADHYA [5] studied the mechanical behavior and
microstructure of the rotary friction welding of titanium alloy.
An extensive literature survey revealed that very few investigations was conducted on the
friction welding of copper alloys (Catridge Brass) and their weldability aspects using Design of
Experiments techniques. The aim of the present study is to minimize the loss of material due to flash

276


by controlling the attributes like Flash width (FW), Flash height (FH) and Flash thickness (FT)
without compromising the Tensile Strength of the joint.
All the above investigations were carried out on trial and other basis to attain optimum
welding strength. Hence in this investigation an attempt was made on similar non- ferrous metal
which has low co-efficient of friction (0.15) to optimize the friction welding parameters for
minimizing the loss of material using Taguchi Method, Simulated Annealing and Artificial Neural
Networks are involved in this study.
In order to determine the welding process parameter that produce the optimized Flash width
(FW), Flash height (FH) and Flash thickness (FT) in friction welding had applied the non-traditional
optimization technique called simulated annealing(SA). Artificial neural network (ANN) technique
is suitably integrated with simulated annealing (SA). Simulation results confirm the feasibility of this
approach and show a good agreement with experimental results.
EXPERIMENTAL PROCEDURE
MACHINE SPECIFICATIONS: The present investigation was performed on continuous drive
friction welding machine type FWT - 12 with a maximum load of 120 KN with cylinder area of 73
cm2 and 80cm2 by which the friction and forge pressure is applied with the help of support hydraulic
arrangement, speed ranging 1000- 3000 rpm, pressure 10 – 50 bar. The hydraulic system is
maintained by powerful servomotors driven by the hydraulic power pack. The speed of the friction
welding machine is controlled by the magnetic brakes which more effective of all braking systems.
MATERIAL: The material used in the present investigation was Copper Alloy: Cu Zn30. The major
alloying elements are copper (CU) – 70 % and Zinc (ZN) – 30%. CuZn30 has good mechanical and
physical properties according to ASTM material specifications. The physical properties like melting
range of solidus and liquidus are 915and 9550C respectively, Density 8.53 gm/cm @ 200C, Specific
Gravity 8.53, Co-efficient of Thermal Expansion – 0.0000199 /0C (20 – 2000C), Annealing
Temperature 425- 7500 C, Hot – Working temperature 725- 8500C, Hardness Rockwell F – 78,
Hardness HR30T – 43, Ultimate Tensile Strength 330 – 372 Mpa, Yield Strength – 150Mpa,
Modulus of Elasticity – 110 Gpa, Poisson’s ratio 0.375, Machinability – 30 %, Co- Efficient of
Friction - 0.15. The material holds fair to excellent corrosion resistance, excellent cold workability,
good hot formability, were used as the parent material in the study. From the literature survey the
predominant factor which has great influence the friction weld (FW) joints were identified. Trial
experiments were conducted to determine the working range of the parameters. The feasible limits
of the parameters were chosen in such a way that it is not effecting external defects. The important
parameters influencing are heating time 4 – 5 sec, heating pressure 10- 20 bar, upset time 3- 5 sec
and upset pressure 20-30 bar and were used to produce the welded joint of the given material. The
speed of spindle (RPM) is kept constant by 1500rpm which obtained in the previous study.
Theoretical Optimization was carried out in order to minimize the Flash width (FW), Flash
height (FH) and Flash thickness (FT) of the joint by Simulated Annealing. The process was
considered to be multi input and multi output variable process. Flash parameters play an important
role in determining properties of the weld. Theoretical and Experimental variations in the Flash
width (FW), Flash height (FH) and Flash thickness (FT) of the joint were also predicted.
Mathematical equation was formulated to represent the objective function and back propagation
neural network was designed and trained to have the relationship between the input parameters and
output parameters. The relationship obtained between input parameter and output parameters by
artificial neural networks (ANN) was optimized by using Simulated Annealing

277


TABLE 1: Input Parameters Range
Sl.no

Parameter

Range

1

Heating Pressure

10-20 bar

2

Heating Time

4 - 6 sec

3

Upsetting
Pressure

4

Upsetting Time

20- 30 bar
3- 5 sec

General Model
Process modeling and optimization are very important issues in welding. The ANN is used to
map the input/output relationships of the process. An objective function variable f can then be
defined as
F = W1(FW) + W2( FH) + W3(FT)
Where W1, W2, W3 are the weights for the normalized Flash width (FW), normalized Flash height
(FH) and normalized Flash thickness (FT) of the weld respectively. Experiments are conducted
according to the taguchi Orthogonal Array matrix (L9) were measured form the each set of data.
From the experimental data, the ANN is trained.
Parameters bounds of heating pressure, heating time, upsetting pressure and upsetting time
Heating Pressure
HPL ≤ HP ≤ HPU
Heating Time HTL ≤ HT ≤ HTU
Upsetting Pressure UPL ≤ UP ≤ UPU
Upsetting Time UTL ≤ UT ≤ UTU
Where the subscript ‘L’ and ‘U’ indicates the lower and upper boundaries respectively.

HP

FW

HT
FH
UP
FT
UT
Input layer

Hidden layer

Output layer

Fig 3: Configuration of the Back Propagation Network for Friction Welding

278


Simulated Annealing Algorithm
Traditionally the annealing process used in metal working involves heating the metal to a
high temperature and then letting it gradually cools down to reach a minimum stable energy state.
Based on the metropolis criterion, a search algorithm called “Simulated Annealing” was developed.
Simulated Annealing is a Monte Carlo approach used ffor minimizing multivariate functions. The
term Simulated Annealing derives from the analogous physical process of heating and cooling a
substance to obtain a strong crystalline structure. The Simulated
Annealing process lowers the
temperature by slow stage until the system “freezes” and no further changes occur. At each
temperature the simulation must process long enough for the system to reach steady state or thermal
equilibrium. It has been shown that the Simulated Annealing Algorithm processes several
advantages in comparisons with a traditional search algorithm. First the Simulated Annealing
algorithm does not required for most traditional search algorithm. This means that the Simulated
Annealing algorithm can be applied to all kinds of objective and constraint functions.
Next the Simulated Annealing algorithm with probabilistic hill- climbing characteristics can
find the global minimum more efficiently instead of becoming trapped in a local minimum, where
the objective function has surrounding barriers. Further Simulated Annealing algorithm search in
independent of initial conditions.
As the results, the Simulated Annealing algorithm has emerged as a general optimization tool
and has been successfully applied in many manufacturing tasks. This Simulated Annealing algorithm
simulates function value in a minimization problem.
The algorithm begins with an initial point X1( HP1, HT1, UP1 & UT1) and a high temperature
T, a second point X2 (HP2, HT2, UP2 & UT2) is created using Gaussian – Distribution and the
difference in the function values ( E) at these points is calculated. The point is accepted with a
probability exp ( E/T). This completes one iteration of the simulated annealing procedure. The
algorithm is terminated when a sufficient small temperature is obtained or a small enough change in
a function value is obtained.
Simulated annealing steps
Step 1 : choose an initial point X1, set Ts a sufficiently high value. Cooling rate Cr, set Te = 0
Step 2 : calculate a neighboring point using Gaussian Distribution
௡
X2 = ‫ݔ‬ଵ ൅ ߪ ቂ∑௡ ‫ݎ‬௜ െ ଶ ቃ
௜ୀଵ
Variance = σ = (maximum value of parameter – minimum value of the parameter) / 6
Where ‘n’ is number of random numbers, r1 is the random numbers.
Step 3: if E = E (X (t+1) – E(X (t)) > 0 i.e. if the difference in the fitness value is possible then, set Ts
= Ts * Cr, else create one random number ® in the range (0, 1). If the r ≤ exp (( E/Ts) set Ts = Ts * Cr
else go to step 2.
Where E – difference between two consecutive fitness values.
Step 4: if the temperature in small, terminate, else go to step 2.

279

CHOOSE AND INITIAL POINT ( FW1, FH1 &FT1)
SET INITIAL TEMPERATURE = T
COOLING RATE = Cr
SET FINAL TEMPERATURE = t

CALCULATE FITNESS f1 = f(FW1, FH1 &FT1)

CALCULATE NEIGHBOUR HOOD
FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜
௜ୀଵ
FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜
௜ୀଵ
FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜
௜ୀଵ

௡

െ ଶቃ
௡

െ ଶቃ

(FW , FH

NEIGHBOUR FITNESS f1 = f

௡

െ ቃ
ଶ

&FT )

f2 > f1

N
ACCEPT
f1 = f2
T = T x Cr

Y

r<e-(∆/T)

Y

T> t

PRINT RESULTS

Fig 4: Flow chart for the Simulated Annealing Algorithm

Table 3: Parameter level and their ranges
PARAMETER
LOW
MEDIUM
HIGH
10
Heating pressure
15
20
Heating time
Upsetting pressure

4
20

5
25

6
30

Upsetting time

3

4

5

The experiments were conducted by the design of experiment, Taguchi orthogonal Array
(L9), as this study is for four factors with three levels so L9 Orthogonal Array is suitable for the
experiment, which can complete the experimentation within 9 runs out of conducting 81 runs as for
full factorial method. So by this material is saved at the time of experimentation.

280


Table 4: The experimental results for the given input parameters and the output variables is
furnished as below
Factors

Heating
pressure

Heating
time

Upsetting
pressure

Upsetting
time

Flash
width

Flash
height

Flash
thickness

(bar)

(sec)

(bar)

(sec)

(mm)

(mm)

(mm)

1

10

4

20

3

9.5

5.2

4.2

2

10

5

25

4

10.5

5.5

4.9

3

10

6

30

5

16.4

5.7

8.2

4

15

4

25

5

9.8

5.3

6.0

5

15

5

30

3

11.4

5.3

6.2

6

15

6

20

4

12.8

5.0

6.6

7

20

4

30

4

11.5

4.2

6.0

8

20

5

20

5

11.1

4.2

6.5

9

20

6

20

3

6.8

4

4.1

RUNS

RESULTS AND DISCUSSION
Typical macrograph of the friction welded sample is shown in the figure 5. The metal loss in
friction welding joint in the form of flash was observed. Minimization of the loss of metal without
compromising the Tensile Strength of the welded joint. It is possible to validate the joints by
assessing the flash parameters such as Flash width (FW), Flash height (FH) and Flash thickness (FT).
The flash features are shown in the figure 6 below.

The friction welding is carried out by using Orthogonal Array L9 (3x4) for 4 parameters with
3 levels. The process parameters and the experimental results are shown in the tables mentioned
below:

281


TABLE 6: Experimental, predicted and percentage of errors for flash width, flash height and flash
thickness with Heating Pressure
HEATING PRESSURE V/S FLASH FEATURES
EXPERIMENTAL
VALUE(mm)

S.NO
1

10

12.13

2

15

3

20

PERCENTAGE OF ERRORS
(%)

PREDICTED VALUE(mm)

5.46

5.76

12.2897

5.4878

5.8405

11.33

5.2

6.26

11.3691

5.2320

6.2277

9.8

4.13

5.53

9.7902

4.1797

-1.317

5.5147

-0.51

-1.399

-0.345

-0.6164

-0.5147

0.0991

-1.2041

0.2754

thickness with Heating Time
HEATING TIME V/S FLASH FEATURES

1

HEATING
TIME
(SEC)
4

2
3

S.NO

EXPERIMENTAL VALUE
(mm)

PREDICTED VALUE
(mm)

PERCENTAGE OF
ERRORS (%)

10.26

4.9

5.4

10.3951

4.9249

5.4755

-1.317

5

11

5

5.387

11.0379

5.0308

5.8397

-0.345

6

12

4.9

6.3

11.9882

4.9594

6.2826

0.0981

-0.51
0.6166
1.2131

-1.399
0.5146
0.2760

UPSETTING PRESSURE V/S FLASH FEATURES

1

UPSETTING
PRESSURE
(MPA)
20

11.13

4.80

5.77

11.2764

4.8249

5.8501

-1.316

-0.52

-1.389

2

25

9.03

4.93

5

9.0617

4.9729

4.9742

-0.351

-0.6166

-0.5152

3

30

13.1

5.07

6.8

13.0870

5.1311

6.7812

0.0990

-1.2048

0.2752

S.NO

EXPERIMENTAL
VALUE(mm)

PREDICTED VALUE(mm)

PERCENTAGE OF
ERRORS (%)

UPSETTING TIME V/S FLASH FEATURES
S.NO

UPSETTING
TIME (SEC)

EXPERIMENTAL
VALUE(mm)

1

3

9.23

4.83

4.83

9.3515

4.8546

4.8975

-1.317

-0.51

-1.399

2

4

11.6

4.9

5.83

11.6401

4.9301

5.7999

-0.345

-0.616

-0.5146

3

5

12.43

5.07

6.9

12.4423

5.1315

6.8810

0.0991

-1.2131

0.2753

PREDICTED VALUE(mm)

PERCENTAGE OF ERRORS
(%)

Artificial neural network is trained using the trained ANN the theoretical prediction of the
flash was carried. The variations of the flash features with the process parameters like heating
pressure, heating time, upsetting pressure and upsetting time are inscribes in the tables below
respectively. The experimental and predicted results with percentage of errors between them are also
included in tables. The variation of flash features can be understood, from the above tables, it is
282


observed that the increase in the heating pressure has an effect in the flash features. Heating pressure
for the given heating time conditions the interface. If the heating pressure is increased the material
expelled. The variation of flash features and the heating time is, with increase in the heating time
increases the metal loss by way of increasing the dimensions of the formed flash. The duration of
heating is selected so as to ensure the cleaned contact of faying surfaces by friction. The temperature
at interface is increased to achieve the required softening for the joints. When heating time is too
short, the heating effect become irregular, and unbounded region increases. Increase3d heating time
results in increased metal loss. So an optimum level of heating time should be taken to minimize the
metal loss. Upsetting pressure in conjunction with surface speed, determine the thermal conditions
established in the weld region. Increase in the upsetting pressure increases the metal loss as the
amount of flash is more. It can be understood that theoretically predicted flash features by ANN are
almost closer to the experimental results. Good agreement between the predicted and the measured
values of the flash width, flash height and flash thickness of the weld is observed from the presented
tables. Hence the formulated ANN is reassembling the real process. By carrying out the experimental
trials on this input parameter, minimized metal loss can be obtained.
CONCLUSION
Friction welding of similar material CuZn30 copper alloy is successfully performed. By way
of conducting experiments on selecting the parameters by Taguchi design of Experiment and the
flash features are measured. From the experimental data, ANN is trained. Trained network predicts
the flash width, flash height and flash thickness are observed very closely. The percentage of
variation between the actual and predicted is around 1.52 %. The global optimization technique
called Simulated Annealing is applied to the network model to achieve optimized parameters though
the optimized input. Parameters minimize the flash features such as Flash Width – 9.03mm, Flash
Height – 4.13 mm and Flash Thickness – 4.83 mm.
Table 10: optimized parameters and the flash Features
Heating
pressure

Heating
time

Upsetting
pressure

Upsetting
time

(bar)

(sec)

(bar)

(sec)

20

5

25

3

Flash width
(mm)
9.03

Flash
height
(mm)

Flash
thickness
(mm)

4.13

4.83

ACKNOWLEDGEMENTS
The authors wish to acknowledge the support from the Mr. Yathin Thambe,Director- Friction
Welding Pvt. Ltd, Pune and sincere thanks to the Ramanandathirta Engineering College, Nalgonda
who permitted me to conduct the tests in their laboratories. Constructive comments and suggestions
from the referee are also acknowledge.
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