Journal of Minerals and Materials Characterization and Engineering, 2012, 11, 790-799
Published Online August 2012 (http://www.SciRP.org/journal/jmmce)
Study on Factors Effecting Weld Pool Geometry of Pulsed
Current Micro Plasma Arc Welded AISI 304L Austenitic
Stainless Steel Sheets Using Statistical Approach
Kondapalli Siva Prasad1*, Chalamalasetti Srinivasa Rao2, Damera Nageswara Rao3
1Department of Mechanical Engineering, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam, India
2Department of Mechanical Engineering, Andhra University, Visakhapatnam, India
3Centurion University of Technology & Management, Odisha, India
Received March 27, 2012; revised May 5, 2012; accepted June 2, 2012
ABSTRACT
Pulsed current Mic ro Plas ma Arc W elding is used to joint thin sheets of AISI 304L sheets, which are used in manufac-
turing of metallic bellows and diaphragms. In this article the effects of pulsing current parameters on weld pool geome-
try namely front width, back width, front height and back height of pulsed current micro plasma arc welded AISI 304L
stainless steel sheets was analyzed. Four factors, five levels, central composite design was used to develop mathematical
models, incorporating pulsed current parameters and weld pool geometry. The mathematical models have been devel-
oped by Response Surface Method. The ad equacy of the models was checke d by ANOVA technique. Variation of out-
put responses with input process variables are discussed. By using the developed mathematical models, weld pool ge-
ometry parameters can be predicted.
Keywords: Pulsed Current; Micro Plasma Arc Welding; Mathematical Model; AIS I 304l St ai nl ess St eel ; Weld Poo l
Geometry; ANOVA
1. Introduction
Austenitic Chromium-Nickel stainless steels had gath-
ered wide acceptance in the fabrication of components
which require high temperature resistance and corrosion
resistance [1], such as metallic bellows used for fabrica-
tion of expa nsion joints, which are used in aircraft, aero-
space and petroleum industry, in which they are sub-
jected to high temperature and corrosive environment.
The present paper focuses on bellow manufacturing in
which a thin sheet is to fold round in shape an d the edges
has to be weld e d longitudinally.
The plasma welding process was introduced to the
welding industry in 1964 as a method of bringing better
control to the arc welding process in lower current ranges
[2]. Today, plasma retains the original advantages it
brought to the indu stry b y provid ing an adv anced lev el of
control and accuracy to produce high quality welds in
both miniature and pre precision applications and to pro-
vide long electrode life for high production requirements
at all levels of amperage. Plasma welding is equally
suited to manual and automatic applications. It is used in
a variety of joining operations ranging from welding of
miniature components to seam welding to high volume
production welding and many others.
Pulsed current MPAW involves cycling the welding
current at selected regular frequency. The maximum
current is selected to give adequate penetration and bead
contour, while the minimum is set at a level sufficient to
maintain a stable arc [3,4]. This permits arc energy to be
used effectively to fuse a spot of controlled dimensions
in a short time produ cing the weld as a series of overlap-
ping nuggets. By contrast, in constant welding current,
the heat required to melt the base material is supplied
only during the peak current pulses allowing the heat to
dissipate into the base material leading to narrower Heat
Affected Zone (HAZ). Advantages include improved
bead contours, greater tolerance to heat sink variations,
lower heat input requirements reduced residual stresses
and distortion, refinement of fusion zone microstructure
and reduced width of HAZ.
From the literature review [5-12] it was understood
that many researchers studied the influence of plasma arc
welding process parameters on bead geometry using sta-
tistical techniques like Taguchi, Response Surface Tech-
nique, Artificial Neural Network, Genetic Algorithm.
However in all the works reported so far researchers have
concentrated on materials of higher thickness; but not
*Corresponding author.

K. S. PRASAD ET AL. 791
much effort was made to develop mathematical models
to predict the same especially when welding thin stainless
steel sheets in a flat position. An attempt is made to cor-
relate important pulsed current MPAW process parame-
ters to weld pool geometry of SS 304L stainless steel
sheets by developing mathematical models using statis-
tical tools.
2. Experimental Setup
2.1. Materials and Methodology
AISI 304L stainless steel sheets of 100 × 150 × 0.25 mm
are welded autogenously with square butt joint without
edge preparation. The chemical composition of AISI 304 L
stainless steel sheet procured from Salem Steel Plant,
India is given in Table 1. High purity argon gas (99.99%)
is used as a shielding gas and a trailing gas right after
welding to prevent absorption of oxygen and nitrogen
from the atmosphere. The welding has been carried out
under the welding conditions presented in Table 2. From
the literature four important factors of pulsed current
MPAW as presented in Table 3 are chosen. A large
number of trail experiments are carried out using 0.25
mm thick AISI 304 L stainless steel sheets to find out the
feasible working limits of pulsed current MPAW process
parameters. Due to wide range of factors, it was decided
to use four factors, five levels, rotatable central compos-
ite design matrix to perform the number of experiments
for investigation. Table 4 indicates the 31 set of coded
conditions used to form the design matrix. The first six-
teen experimental conditions (rows) have been formed
for main effects. The next eight experimental conditions
are called as corner points and the last seven experimen-
tal conditions are known as center points. The method of
designing such matrix is dealt elsewher e [13,14]. For the
convenience of recording and processing the experimen-
tal data, the upper and lower levels of the factors are
coded as +2 and –2, respectively and the coded values of
any intermediate levels can be calculated by using the
expression [15].

 
maxminmax min
22
i
XXXXXX

 

(1)
Table 1. Chemical composition of AISI 304L stainless steel sheets (wt%).
C Si Mn P S Cr Ni Mo Ti N
0.021 0.35 1.27 0.030 0.001 18.10 8.02 -- -- 0.053
Table 2. Welding conditions.
Power source Secheron micro plasma arc machine
Model number PLASMAFIX 50E
Polarity DCEN
Mode of operation Pulse mode
Electrode 2% thoriated tungsten electrode
Electrode diameter 1 mm
Plasma gas 95% argon & 5% hydr o g e n
Plasma gas flow rate 6 Lpm
Shielding gas Argon
Shielding gas flow rate 0.4 Lpm
Purging gas Argon
Purging gas flow rat e 0.4 Lpm
Copper nozzle diameter 1 mm
Nozzle to plate distance 1 mm
Welding speed 260 mm/min
Torch position Vertical
Operation type Automatic
Table 3. Important factors and their levels.
Levels
SI No. Input factor Units –2 –1 0 +1 +2
1 Peak current Amps 6 6.5 7 7.5 8
2 Back current Amps 3 3.5 4 4.5 5
3 Pulse No’s/sec 20 30 40 50 60
4 Pulse width % 30 40 50 60 70

K. S. PRASAD ET AL.
792
Table 4. Design matrix and experimental results.
SI No. Peak current
(Amps) Back current (Amps) Pulse (No/sec) Pulse width (%) Front width (mm) Back width (mm) Front height (mm) Back height (mm)
1 –1 –1 –1 –1 1.448 1.374 0.0609 0.0498
2 1 –1 –1 –1 1.592 1.522 0.0588 0.0458
3 –1 1 –1 –1 1.383 1.324 0.0630 0.0490
4 1 1 –1 –1 1.504 1.442 0.0569 0.0439
5 –1 –1 1 –1 1.454 1.401 0.0581 0.0453
6 1 –1 1 –1 1.487 1.418 0.0595 0.0466
7 –1 1 1 –1 1.469 1.378 0.0599 0.0468
8 1 1 1 –1 1.462 1.402 0.0578 0.0448
9 –1 –1 –1 1 1.529 1.451 0.0599 0.0470
10 1 –1 –1 1 1.591 1.508 0.0571 0.0441
11 –1 1 –1 1 1.520 1.447 0.0572 0.0441
12 1 1 –1 1 1.562 1.506 0.0552 0.0423
13 –1 –1 1 1 1.442 1.372 0.0605 0.0474
14 1 –1 1 1 1.384 1.306 0.0590 0.0456
15 –1 1 1 1 1.506 1.430 0.0600 0.0470
16 1 1 1 1 1.420 1.356 0.0584 0.0464
17 –2 0 0 0 1.521 1.451 0.0598 0.0468
18 2 0 0 0 1.580 1.514 0.0569 0.0439
19 0 –2 0 0 1.452 1.380 0.0575 0.0445
20 0 2 0 0 1.427 1.358 0.0564 0.0434
21 0 0 –2 0 1.596 1.527 0.0582 0.0453
22 0 0 2 0 1.466 1.397 0.0564 0.0434
23 0 0 0 –2 1.400 1.337 0.0636 0.0516
24 0 0 0 2 1.461 1.384 0.0602 0.0472
25 0 0 0 0 1.531 1.462 0.0606 0.0476
26 0 0 0 0 1.581 1.512 0.0597 0.0467
27 0 0 0 0 1.523 1.452 0.0607 0.0477
28 0 0 0 0 1.519 1.450 0.0606 0.0476
29 0 0 0 0 1.504 1.432 0.0607 0.0477
30 0 0 0 0 1.501 1.433 0.0576 0.0446
31 0 0 0 0 1.401 1.332 0.0597 0.0456
where Xi is the required coded value of a parameter X.
The X is any value of the parameter from Xmin to Xmax,
where Xmin is the lower limit of the parameter and Xmax is
the upper limit of the parameter.
2.2. Measurement of Weld Pool Geometry
Three metallurgical samples were cut from each joint,
with the first sample being located at 25 mm behind the
trailing edge of the crater at the end of the weld and
mounted using Bakelite. Sample preparation and mounting
was done as per ASTM E 3-1 standard. The transverse
face of the samples were surface grounded using 120 grit
size belt with the help of belt grinder, polished using
grade 1/0 (245 mesh size), grade 2/0 (425 mesh size) and
grade 3/0 (515 mesh size) sand paper. The specimens
were further polished by using aluminum oxide initially
and the by utilizing diamond paste and velvet cloth in a
polishing machine. The polished specimens were macro-
etched by using 10% Oxalic acid solution to reveal the
geometry of the weld pool (Figure 1) [16]. Several criti-

K. S. PRASAD ET AL. 793
cal parameters, such as front width, back width, front
height and back height of the weld pool geometry (Fig-
ure 2) [16] are measured. The weld pool geometry was
measured using Metallurgical Microscope (Make: Dewin-
ter Technologie, Model No. DMI-CROWN-II) at 100×
magnification.
3. Developing Mathematical Models
In most RSM problems [17-19], the form of the relation-
ship between the response (Y) and the independent vari-
ables is unknown. Thus the first step in RSM is to find a
suitable approximation for the true functional relation-
ship between the response and the set of independent
variables.
Usually, a low order polynomial is some region of the
independent variables is employed. If the response is
well modeled by a linear function of the independent
variables then the approximating function in the first
order model.
oii
bx Yb

(2)
Figure 1. Typical weld pool geometry [20].
100 X
o iiijjj
Yb bxbxx
Figure 2. Macrographs of weld pool.
If interaction terms are added to main effects or first
order model, then we have a model capable of represent-
ing some curvature in the response function.

 


2
oiiiii ijij
Ybbx bxbxx
(3)
The curvature, of course, results from the twisting of
the plane induced by th e interaction term βijxixj.
There are going to be situations where the curvature in
the response function is not adequately modeled by
Equation (3). In such cases, a logical model to consider is

 


12
2
344
13 34
FW1.50857 0.015380.00629
0.031870.011540.02007
0.030440.02469
XX
(4)
where bii represents pure second order or quadratic effects.
Equation (4) is a second order response surface model.
Using MINITAB 14 statistical software package, the
significant coefficients were determined and final models
were developed using only theses coefficients to estimate
front width, back width, front height and back height of
the weld poo l geo metry.
Front Widt h ( FW)
X
XX
XX XX
 


12
34
2
413
BW1.143900 0.017040.00462
0.032120.00871
0.020240.03006
(5)
Back Width (BW)
X
X
XX
XXX




12
2
342
2
334
FH0.059943 0.0009420.000317
0.0000250.0006000.000704
0.0006170.000800
XX
(6)
Front Height (FH)
X
XX
XXX
 


12
2
342
2
4
BH0.046786 0.009460.000396
0.0000040.0007040.000670
0.000692
XX
(7)
Back Heig h t (BH)
X
XX
X
 


(8)
where X1, X2, X3 and X4 are the coded values of front
width, back width, front height and back height respec-
tively.
4. Checking the Adequacy of the Developed
Models
The adequacy of the developed models was tested using
the analysis of variance technique (ANOVA). As per this
technique, if the calculated value of the Fratio of the de-
veloped model is less than the standard Fratio (from F-
table) value at a desired level of confidence (say 99%),
then the model is said to be adequate within the confi

K. S. PRASAD ET AL.
JMMCE
794
5. Results & Discussion dence limit. ANOVA test results are presented in Table
5 for all the models. From the table it is understood that
the developed mathematical models are found to be ade-
quate at 99% confidence level. Coefficient of determina-
tion “R2” is used to find how close the predicted and ex-
perimental values lie. The value of “R2” for the above
developed models is found to be about 0.84, which indi-
cates good correlation exists between the experimental
values and predicte d values.
The mathematical models developed above can be em-
ployed to predict the geometry of weld pool geometry
dimensions and their relationships for the range of pa-
rameters used in the investigation by substituting their
respective values in coded form. Based on these models,
the effects of the process parameters on the weld pool
geometry dimensions are computed and plotted as de-
picted in Figures 7-10.
Figures 3-6 indicate the scatter plots for weld pool
geometry parameters of the weld joint and reveals that
the actual and predicted values are close to each other
with in the specified limits.
5.1. Effect of Peak Current on Weld Pool
Geometry Parameters
Confirmation tests are carried out at different condi-
tions to check the accuracy of the developed models. The
details of confirmation tests are presented in Table 6.
Front width and back width decreases with peak current
up to 6.5 Amperes and thereafter increases, where as
front height and back height increases up to 6.5 Amperes
and thereafter decreases. At lower peak currents up to 6.5
Amperes, the heat input is less and hence low melting
rate of the parent metal leading to lower front width and
back width. When peak current increases beyond 6.5
Amperes the heat input also increases and hence high
melting rate of paren t metal leading to higher front width
and back width.
From Table 6 it is very clear that the developed model
holds good for set of input parameters other than that
specified in design matrix. However it is important that
the developed model is valid within the range of speci-
fied weld input parameters. The experimental and pre-
dicted values of weld pool geometry parameters and er-
ror % is presented in Table 7.
Figure 4. Scatter plot of back width. Figure 3. Scatter plot of front width.
Figure 6. Scatter plot of back height.
Figure 5. Scatter plot of front height.

K. S. PRASAD ET AL. 795
Table 5. ANOVA table.
Front Width
Source DF Seq SS Adj SS Adj MS F P
Regression 14 0.100167 0.100167 0.007155 6.36 0.000
Linear 4 0.034205 0.034205 0.008551 7.60 0.001
Square 4 0.025671 0.025671 0.006418 5.70 0.005
Interaction 6 0.040291 0.040291 0.006715 5.96 0.002
Residual Error 16 0.018013 0.018013 0 .001126
Lack-of-Fit 10 0.000298 0.000298 0.000030 0.01 1.000
Pure Error 6 0.017716 0.017716 0.002953
Total 30 0.118180
Back Width
Source DF Seq SS Adj SS Adj MS F P
Regression 14 0.098374 0.098374 0.007027 6.18 0.000
Linear 4 0.034072 0.034072 0.008518 7.49 0.001
Square 4 0.026461 0.026461 0.006615 5.82 0.004
Interaction 6 0.037841 0.037841 0.006307 5.55 0.003
Residual Error 16 0.018191 0.018191 0 .001137
Lack-of-Fit 10 0.000509 0.000509 0.000051 0.02 1.000
Pure Error 6 0.017682 0.017682 0.002947
Total 30 0.116565
Front Height
Source DF Seq SS Adj SS Adj MS F P
Regression 14 0.000092 0.000092 0.000007 5.43
0.001
Linear 4 0.000032 0.000032 0.000008 6.71 0.002
Square 4 0.000038 0.000038 0.000009 7.85 0.001
Interaction 6 0.000021 0.000021 0.000004 2.97 0.038
Residual Error 16 0.000019 0.000019 0 .000001
Lack-of-Fit 10 0.000012 0.000012 0.000001 0.92 0.570
Pure Error 6 0.000008 0.000008 0.000001
Total 30 0.000111
Back Height
Source DF Seq SS Adj SS Adj MS F P
Regression 14 0.000102 0.000102 0.000007 5.54 0.001
Linear 4 0.000037 0.000037 0.000009 7.05 0.002
Square 4 0.000041 0.000041 0.000010 7.87 0.001
Interaction 6 0.000024 0.000024 0.000004 2.99 0.037
Residual Error 16 0.000021 0.000021 0 .000001
Lack-of-Fit 10 0.000012 0.000012 0.000001 0.78 0.656
Pure Error 6 0.000009 0.000009 0.000002
Total 30 0.000123
Where SS = sum of squares; M S = mean square s; DF = degree of freedom; F = fis her’s ratio.

K. S. PRASAD ET AL.
796
Table 6. Confirmation test results.
Weld pool geometry parameters (mm)
Experimental Predicted
Peak
current
(Amperes)
Back
current
(Amperes)
Pulse rate
(pulses/
second)
Pulse
width
(%) Front
Width Back Width Front Height Back Height Front Width Back Width Front Height Back Height
2 2 2 2 1.1921.2460.0560.0341.1851.234 0.054 0.029
0 2 2 2 1.2801.3200.0620.0521.2761.311 0.056 0.048
2 0 2 2 1.2041.2320.0640.0381.1981.225 0.058 0.033
2 2 0 2 1.4761.4240.0560.0301.4701.410 0.053 0.026
Table 7. Comparison of experimental and predicte d value s.
Front width (mm) Back width (mm) Front height (mm) B a c k h e ig h t ( mm)
SI
No. Experimental Predicted Error (%) Experimental PredictedError (%) Experimental PredictedError (%) Experimental PredictedError (%)
1 1.448 1.446 0.138 1.374 1.380 –0.4350.0609 0.0615–0.9760.0498 0.0499 –0.200
2 1.592 1.593 –0.063 1.522 1.519 0.1970.0588 0.0592–0.6760.0458 0.0466 –1.717
3 1.383 1.384 –0.072 1.324 1.315 0.6840.0630 0.06201.6130.0490 0.0486 0.823
4 1.504 1.503 0.067 1.442 1.447 –0.3460.0569 0.0580–1.8970.0439 0.0448 –2.009
5 1.454 1.457 –0.206 1.401 1.399 0.1430.0581 0.0584–0.5140.0453 0.0459 –1.307
6 1.487 1.482 0.337 1.418 1.418 0.0000.0595 0.05841.8840.0466 0.0453 2.870
7 1.469 1.465 0.273 1.378 1.385 –0.5050.0599 0.05980.1670.0468 0.0465 0.645
8 1.462 1.463 –0.068 1.402 1.396 0.4300.0578 0.0580–0.3450.0448 0.0453 –1.104
9 1.529 1.532 –0.196 1.451 1.453 –0.1380.0599 0.0593 1.0120.0470 0.0466 0.858
10 1.591 1.596 –0.313 1.508 1.510 –0.1320.0571 0.0573–0.3490.0441 0.0440 0.227
11 1.520 1.526 –0.393 1.447 1.456 –0.6180.0572 0.0584–2.0550.0441 0.0450 –2.000
12 1.562 1.562 0.000 1.506 1.505 0.0660.0552 0.05461.0990.0423 0.0418 1.196
13 1.442 1.444 –0.139 1.372 1.375 –0.2180.0605 0.0594 1.8520.0474 0.0461 2.820
14 1.384 1.387 –0.216 1.306 1.312 –0.4570.0590 0.0596–1.0070.0456 0.0461 –1.085
15 1.506 1.509 –0.199 1.430 1.429 0.0700.0600 0.05931.1800.0470 0.0463 1.512
16 1.420 1.423 –0.211 1.356 1.358 –0.1470.0584 0.0578 1.0380.0464 0.0458 1.310
17 1.521 1.518 0.198 1.451 1.446 0.3460.0598 0.0604–0.9930.0468 0.0474 –1.266
18 1.580 1.579 0.063 1.514 1.514 0.0000.0569 0.05660.5300.0439 0.0436 0.688
19 1.452 1.450 0.138 1.380 1.376 0.2910.0575 0.0578–0.5190.0445 0.0449 –0.891
20 1.427 1.425 0.140 1.358 1.357 0.0740.0564 0.0565–0.1770.0434 0.0433 0.231
21 1.596 1.593 0.188 1.527 1.524 0.1970.0582 0.05741.3940.0453 0.0445 1.798
22 1.466 1.465 0.068 1.397 1.395 0.1430.0564 0.0575–1.9130.0434 0.0445 –2.472
23 1.400 1.405 –0.356 1.337 1.341 –0.2980.0636 0.0633 0.4740.0516 0.0510 1.176
24 1.461 1.451 0.689 1.384 1.375 0.6550.0602 0.0609–1.1490.0472 0.0481 –1.871
25 1.531 1.509 1.458 1.462 1.439 1.5980.0606 0.05991.1690.0476 0.0468 1.709
26 1.581 1.509 4.771 1.512 1.439 5.0730.0597 0.0599–0.3340.0467 0.0468 –0.214
27 1.523 1.509 0.928 1.452 1.439 0.9030.0607 0.05991.3360.0477 0.0468 1.923
28 1.519 1.509 0.663 1.450 1.439 0.7640.0606 0.05991.1690.0476 0.0468 1.709
29 1.504 1.509 –0.331 1.432 1.439 –0.4860.0607 0.0599 1.3360.0477 0.0468 1.923
30 1.501 1.509 –0.530 1.433 1.439 –0.4170.0576 0.0599–3.8400.0446 0.0468 –4.701
31 1.401 1.509 –7.157 1.332 1.439 –7.4360.0597 0.0599–0.3340.0456 0.0468 –2.564

K. S. PRASAD ET AL. 797
Figure 7. Main effects for front width.
Figure 8. Main effects for back width.
Figure 9. Main effects for front height.

K. S. PRASAD ET AL.
798
Figure 10. Main effe cts for back he i g ht.
5.2. Effect of Back Current on Weld Pool
Geometry Parameters reason for effect of pulse width on weld pool geometry
parameters is same as that of pulse rate. From 30% to
50% pulse width, the interval between pulse widths is
high and hence the heat input which enters the system at
a moment increases thereby increasing the front width
and back width. When the pulse width increase beyond
50% heat input which enters the system at a moment
decreases thereby decreasing the front width and back
width.
Front width, back width, front height increases up to 4
Amperes and thereafter decreases, where as back height
increases up to 3.5 Amperes and thereafter decreases. As
the back current is helpful in maintaining continuous arc
during welding, when the back current is low i.e. up to 4
Amperes, front width, back width and front height in-
creases due to higher and dominating peak current which
generates large amount of heat. When the back current is
increased beyond 4 Amperes, it balances the heat input
leading to lower heat input and hence front width, back
width and front height decreases.
From Figures 3-6, it was understood that a peak cur-
rent of 6.5 Amperes, back current of 3.5 Amperes, pulse
rate of 40 pulses/sec and pulse width of about 40% is
found to produce opti mum resu lts.
6. Conclusion
5.3. Effect of Pulse Rate on Weld Pool Geometry
Parameters A five level, four factor full, factorial design matrix based
on the central composite rotatable design technique was
used for the develop ment of mathematical models to pre-
dict the weld pool geometry parameters for AISI 304 L
stainless sheets welded by pulsed current micro plasma
arc welding process. The prediction results using mathe-
matical models are very close to the experimental results.
Peak Current is the most dominating factor out of the
selected parameters, since as peak current increases heat
input increases leading to wider front and back widths
and narrow front and back heights. For a peak current of
6.5 Amperes, back current of 3.5 Amperes, pulse rate of
40 pulses/second and pulse width of 40% the optimal
weld pool geometry parameters can be achieved. The
mathematical models are developed considering only
four factors and five levels (peak current, back current,
pulse rate and pulse width). However one may consider
more number of factors and their levels to improve the
mathematical model.
Front width and back width decreases up to 50 pulses/
second and thereafter increases, where as front height
increases up to 40 pulses/second and thereafter decreases
and back height increases up to 30 pulses/second and
thereafter decreases. This may be due to difference in
heat input caused by variation of pulse rate. From 20 to
50 pulses/second, the interval between pulses is low and
hence the heat input which enters the system at a moment
decreases thereby decreasing the front width and back
width. When the pulse rate increase beyond 50 pulses/sec
heat input which en ters the system at a moment increases
thereby increasing the front width and back width.
5.4. Effect of Pulse Width on Weld Pool
Geometry Parameters
Front width and back width increases up to 50% and
thereafter decreases, where as front height and back
height decreases up to 60% and thereafter increases. The

K. S. PRASAD ET AL. 799
7. Acknowledgements
The authors would like to thank Shri. R. Gopla Krishnan,
Director, M/s Metallic Bellows (I) Pvt Ltd., Chennai for
his support to carry out experimentation work.
REFERENCES
[1] T. D. Clark, “Department of Mechanical Engineering,”
Mater’s Thesis, Brigham Young University, Provo, 2005.
[2] M. Balasubramanian, V. Jayabalan and V. Balasubrama-
nian, “Effect of Process Parameters of Pulsed Current
Tungsten Inert Gas Welding on Weld Pool Geometry of
Titanium Welds,” Acta Metallurgica Sinica (English Let-
ters), Vol. 23, No. 4, 2010, pp. 312-320.
[3] M. Balasubramanian, V. Jayabalan and V. Balasubrama-
nian, “Optimizing the Pulsed Current Gas Tungsten Arc
Welding Parameters,” Journal of Materials Science &
Technology, Vol. 22, No. 6, 2006, pp. 821-825.
[4] G. M. Reddy, A. A. Gokhale and K. P. Rao, “Weld Mi-
crostructure Refinement in a 1441 Grade Aluminium-
Lithium Alloy,” Journal of Materials Science, Vol. 32,
No. 5, 1997, pp. 4117-4126.
doi:10.1023/A:1018662126268
[5] D. K. Zhang and J. T. Niu, “Application of Artificial
Neural Network Modeling to Plasma Arc Welding of
Aluminum Alloys,” Journal of Advanced Metallurgical
Sciences, Vol. 13, No. 1, 2000, pp. 194-200.
[6] S.-C. Chi and L.-C. Hsu, “A Fuzzy Radial Basi s Function
Neural Network for Predicting Multiple Quality Charac-
teristics of Plasma Arc Welding,” Joint 9th IFSA World
Congress and 20th NAFIPS International Conference,
Vancouver, 25-28 July 2001, pp. 2807-2812.
[7] Y. F. Hsiao, Y. S. Tarng and W. J. Huang, “Optimization
of Plasma Arc Welding Parameters by Using the Taguchi
Method with the Grey Relational Analysis,” Journal of
Materials and Manufacturing Processes, Vol. 23, No. 1,
2007, pp. 51-58. doi:10.1080/10426910701524527
[8] K. Siva, N. Muragan and R. Logesh, “Optimization of
Weld Bead Geometry in Plasma Transferred Arc Hard-
facing Austenitic Stainless Steel Plates Using Genetic
Algorithm,” The International Journal of Advanced Manu-
facturing Technology, Vol. 41, No. 1-2, 2008, pp. 24-30.
doi:10.1007/s00170-008-1451-3
[9] A. K. Lakshinarayana, V. Balasubramanian, R. Varaha-
moorthy and S. Babu, “Predicted the Dilution of Plasma
Transferred Arc Hardfacing of Stellite on Carbon Steel
Using Response Surface Methodology,” Metals and Ma-
terials International, Vol. 14, No. 6, 2008, pp. 779-789.
doi:10.3365/met.mat.2008.12.779
[10] V. Balasubramanian, A. K. Lakshminarayanan, R. Vara-
hamoorthy and S. Babu, “Application of Response Sur-
face Methodolody to Prediction of Dilution in Plasma
Transferred Arc Hardfacing of Stainless Steel on Carbon
Steel,” International Journal of Iron and Steel Research,
Vol. 16, No. 1, 2009, pp. 44-53.
[11] E. Taban, A. Dhooge and E. Kaluc, “Plasma Arc Welding
of Modified 12% Cr Stainless Steel,” Materials and Ma-
nufacturing Processes, Vol. 24, No. 6, 2009, pp. 649-656.
doi:10.1080/10426910902769152
[12] N. Kahraman, M. Taskin, B. Gulenc and A. Durgutlu,
“An Investigation into the Effect of Welding Current on
the Plasma Arc Welding of Pure Titanium,” Kovove Ma-
ter, Vol. 48, No. 3, 2010, pp. 179-184.
[13] N. Srimath and N. Muragan, “Prediction and Optimiza-
tion of Weld Bead Geometry of Plasma Transferred Arc
Hardfacing Valve Seat Rings,” European Journal of Sci-
entific Research, Vol. 51, No. 2, 2011, pp. 285-298.
[14] D. C. Montgomery, “ Design and Analysis of Experiments,”
3rd Edition, John Wiley & Sons, New York, 1991.
[15] G. E. P. Box, W. H. Hunter and J. S. Hunter, “Statistics
for Experiments,” John Wiley & Sons, New York, 1978.
[16] J. Ravindra and R. S. Parmar, “Mathematical Model to
Predict Weld Bead Geometry for Flux Cored Arc Weld-
ing,” Journal of Metal Construction, Vol. 19, 1987, pp.
45-52.
[17] W. G. Cochran and G. M. Cox, “Experimental Designs,”
John Wiley & Sons Inc., London, 1957.
[18] T. B. Barker, “Quality by Experimental Design,” ASQC
Quality Press, Milkwakee, 1985.
[19] W. P. Gardiner and G. Gettinby, “Experimental Design
Techniques in Statistical Practice,” Horwood, Chichester,
1998.
[20] K. S. Prasad, Ch. S. Rao and D. N. Rao, “Prediction of
Weld Pool Geometry in Pulsed Current Micro Plasma
Arc Welding of SS304L Stainless Steel Sheets,” Interna-
tional Transaction Journal of Engineering Management
& Applied Sciences & Technologies, Vol. 2, No. 3, 2011,
pp. 325-336.