1. Journal of Materials Processing Technology 176 (2006) 230–239
Effect of flux cored arc welding process parameters
on duplex stainless steel clad quality
T. Kannana,1, N. Muruganb,∗
a Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore 641006, Tamil Nadu, India
b Department of Mechanical Engineering, Coimbatore Institute of Technology, Coimbatore 641014, Tamil Nadu, India
Received 9 December 2004; received in revised form 1 March 2006; accepted 1 March 2006
Abstract
The main problem faced in duplex stainless steel cladding is the selection of the optimum combination of process parameters for achieving the
required quality of clad. This paper highlights an experimental study carried out to analyse the effects of various flux cored arc welding (FCAW)
process parameters on important clad quality parameters in duplex stainless steel cladding of low carbon structural steel plates. The experiments
were conducted based on four-factor five level central composite rotatable design with full replications technique and having mathematical models
developed using multiple regression method. The effects of the input process parameters on clad quality parameters have been presented in graphical
form, which helps in selecting welding process parameters to achieve the desired clad quality quickly.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Mathematical models; Duplex stainless steels; FCAW; Clad quality parameters
1. Introduction
Most of the engineering applications require both high
strength and corrosion resistant materials for long term relia-
bility and performance. Often the strength can best be achieved
by the use of steels which do not possess the required corrosion
resistance. A possible materials solution to providing structural
components which combine the attributes of high strength and
corrosion resistance is to clad the surface of the steel with a
metallurgically compatible corrosion resistant alloy. The char-
acteristics desirable in such a cladding alloy are reasonable
strength, weldability to the steel, resistance to general and local-
ized corrosion attack, and good corrosion fatigue properties [1].
A candidate material for cladding which has excellent corrosion
resistance and weldability is duplex stainless steel [2]. These
have chloride stress corrosion cracking resistance and strength
significantly greater than that of the 300-series austenitics [3].
In recent years, weld cladding processes have been devel-
oped rapidly and are now applied in numerous industries such
∗ Corresponding author. Tel.: +91 422 2574071; fax: +91 422 2575020.
E-mail addresses: kannan kct@yahoo.com (T. Kannan),
drmurugan@yahoo.com (N. Murugan).
1 Tel.: +91 422 2669589; fax: +91 422 2669406.
as chemical and fertilizer plants, nuclear and steam power plants,
food processing and petrochemical industries, etc. The biggest
difference between welding a joint and cladding is the per-
centage dilution [4] illustrated in Fig. 1. The composition and
properties of cladding are strongly influenced by the dilution
obtained. Control of dilution is important in cladding, where
typically low dilution is desirable. When the dilution is low,
the final deposit composition will be closer to that of the filler
metal and the corrosion resistance of the cladding will also be
maintained.
Various welding processes employed for cladding are
shielded metal arc welding (SMAW), submerged arc welding
(SAW), gas tungsten arc welding (GTAW), plasma arc weld-
ing (PAW), gas metal arc welding (GMAW), flux cored arc
welding (FCAW), electroslag welding (ESW), oxy-acetylene
welding(OAW)andexplosivewelding[5].Amongtheprocesses
employed for weld cladding, FCAW is readily accepted by the
industries [6] due to the following features.
• High deposition rates, especially for out-of-position welding.
• More tolerant of rust and mill scale than GMAW.
• Simpler and more adaptable than SAW.
• Less operator skill required than GMAW.
• High productivity than SMAW.
• Good surface appearance.
0924-0136/$ – see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.jmatprotec.2006.03.157

2. T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239 231
Table 1
Chemical composition of base metal and filler wire used
Material Elements (wt.%)
C Si Mn P S Al Cr Mo Ni N2 Cu
IS: 2062 0.150 0.160 0.870 0.015 0.016 0.032 – – – – –
E2209T1-4/1 0.023 0.760 1.030 0.024 0.002 – 23.14 3.05 9.22 0.13 0.09
Fig. 1. Weld bead geometry.
• Good radiographic standard quality.
• Minimum electrode wastage.
This paper highlights an experimental study carried out
to analyse the effects of various FCAW process parameters
on important clad quality parameters in duplex stainless steel
cladding of low carbon structural steel plates. The experiments
were conducted based on four-factor five level central composite
rotatable design with full replications technique and mathemat-
ical models developed using multiple regression method. The
developed mathematical models have been checked for their
adequacy and significance.
2. Experimental work
The experiments were conducted using UNIMACRO 501C programmable
welding machine using DC electrode positive (DCEP). Test pieces of size
200 mm × 150 mm × 20 mmwerecutfromlowcarbonstructuralsteel(IS:2062)
plate and its surfaces were ground to remove oxide scale and dirt before cladding.
Flux cored duplex stainless steel welding wire (E2209T1-4/1) of 1.2 mm diam-
eter was used for depositing the weld beads. Chemical composition of the base
metal and welding wire is given in Table 1. CO2 gas at a constant flow rate of
18 L/min was used for shielding. The experimental setup used consisted of a
travelling carriage with a table for supporting the specimens. The carriage speed
wascontinuouslyadjustablefrom6 cm/minto72 cm/min.Theweldingtorchwas
held stationary in a frame mounted above the work table, and it was provided
with an attachment for both up and down movement and angular movement for
setting the required nozzle-to-plate distance and welding torch angle, respec-
tively. The experiments were conducted by laying three beads using stringer
bead technique with a constant overlap of 40%. An interpass temperature of
150 ◦C was maintained during all the cladding experiments. The experimental
setup is shown in Fig. 2.
Fig. 2. Experimental setup.
3. Experimental design procedure
The experimental design procedure used for this study is shown in Fig. 3
and important steps are briefly explained below.
3.1. Identification of factors and responses
The chosen factors were welding current (I), welding speed (S), nozzle-to-
plate distance (N), and welding torch angle (T). In this study, forehand welding
(push angle) technique was used. The chosen responses were weld bead width
(W), average depth of penetration (P), average height of reinforcement (R), and
percentage dilution (D). The chosen input and output parameters of FCAW are
shown in Fig. 4.
3.2. Finding the limits of the process variables
The working ranges of all selected factors were fixed by conducting trial
runs. This was carried out by varying one of the factors while keeping the rest
of them at constant values [7]. The working range of each process parameters
was decided upon by inspecting the bead for a smooth appearance without any
visible defects such as surface porosity, undercut, etc. The upper limit of a factor
was coded as +2 and the lower limit was coded as −2. The coded values for
intermediate values were calculated using the following Eq. (1)
Xi =
2[2X − (Xmax + Xmin)]
Xmax − Xmin
(1)
Table 2
Welding parameters and their levels
Parameter Unit Notation Factor levels
−2 −1 0 +1 +2
Welding current A I 200 225 250 275 300
Welding speed cm/min S 20 30 40 50 60
Nozzle-to-plate distance mm N 22 24 26 28 30
Welding torch angle with reference to vertical ◦ T 20 15 10 05 00

3. 232 T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239
Fig. 3. Experimental design procedure.
Fig. 5. Typical cladded plate (Trial Nos. 14 and 29).
where Xi is the required coded value of a variable X, X the any value of the
variable from Xmin to Xmax, Xmin the lower limit of the variable and Xmax the
upper limit of the variable. The chosen levels of the selected process parameters
with their units and notations are given in Table 2.
3.3. Development of design matrix
The design matrix chosen to conduct the experiment was a central compos-
ite rotatable design. This design matrix [8] comprised of a full replication of
24 = (16) factorial design plus seven center points and eight star points which is
shown in Table 3. All welding variables at the intermediate levels (0) constituted
the center points and the combination of each welding variables at either its high-
est value (+2) or lowest value (−2) with other three variables of the intermediate
levels (0), constituted the star points. Thus the 31 experimental runs allowed the
estimation of the linear, quadratic and two-way interactive effects of the process
parameters on clad quality parameters.
3.4. Conducting the experiments as per the design matrix
The experiments were conducted at the Welding Engineering Research Cen-
tre in Coimbatore Institute of Technology, India. In this work, 31 deposits were
made using cladding condition corresponding to each treatment combination of
parameters shown in Table 3 at random. At the end of each run, settings for all
four parameters were disturbed and reset for the next deposit. This was essential
to introduce variability caused by errors in experimental settings [9]. A typical
cladded plate is shown in Fig. 5.
3.5. Recording the responses
To measure the weld bead geometry, transverse sections of each weld over-
lays were cut using power hacksaw from the mid-length position of the welds,
and the end faces were machined. Specimen end faces were polished and etched
using a 2% nital solution and the bead profiles were traced using a reflective type
optical profile projector at a magnification of 10 and then the bead dimensions
such as penetration, reinforcement and bead width were measured. The areas
of base metal melted and the weld metal forming reinforcement were measured
with the help of a digital planimeter and the percentage dilution was calcu-
lated. The measured weld bead dimensions and calculated percentage dilution
are given in Table 3.
Fig. 4. Chosen factors and responses for FCAW process.

4. T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239 233
Table 3
Design matrix and the observed values of clad quality parameters
Trial no. Design matrix Clad quality parameters
I S N T W (mm) P (mm) R (mm) D (%)
01 −1 −1 −1 −1 29.50 0.61 4.97 07.86
02 +1 −1 −1 −1 36.62 0.73 5.00 12.10
03 −1 +1 −1 −1 24.20 0.63 4.23 11.35
04 +1 +1 −1 −1 28.00 0.77 4.27 11.98
05 −1 −1 +1 −1 30.00 0.57 5.00 06.54
06 +1 −1 +1 −1 34.98 0.67 5.29 08.82
07 −1 +1 +1 −1 25.59 0.58 4.18 09.69
08 +1 +1 +1 −1 29.51 0.70 4.20 11.16
09 −1 −1 −1 +1 28.34 0.73 5.00 08.97
10 +1 −1 −1 +1 34.50 0.97 5.10 13.75
11 −1 +1 −1 +1 24.00 1.00 4.00 18.52
12 +1 +1 −1 +1 27.80 1.20 4.34 20.58
13 −1 −1 +1 +1 29.26 0.60 5.08 07.46
14 +1 −1 +1 +1 34.80 0.80 5.28 09.14
15 −1 +1 +1 +1 25.30 0.97 4.00 18.00
16 +1 +1 +1 +1 27.70 1.00 4.20 14.80
17 −2 0 0 0 20.15 0.40 3.98 05.86
18 +2 0 0 0 31.00 1.07 4.90 16.48
19 0 −2 0 0 39.53 0.70 5.68 05.31
20 0 +2 0 0 23.10 1.00 3.63 17.35
21 0 0 −2 0 25.10 0.83 4.32 11.71
22 0 0 +2 0 28.00 0.63 4.81 09.01
23 0 0 0 −2 30.20 0.56 4.17 10.54
24 0 0 0 +2 26.00 0.87 4.87 13.98
25 0 0 0 0 27.88 0.70 4.55 10.33
26 0 0 0 0 29.42 0.83 4.34 13.60
27 0 0 0 0 28.00 0.77 4.50 10.73
28 0 0 0 0 27.90 0.87 4.50 11.71
29 0 0 0 0 29.20 0.83 4.32 13.76
30 0 0 0 0 27.80 0.79 4.58 10.99
31 0 0 0 0 27.80 0.80 4.57 10.67
W, width; P, penetration; R, reinforcement; D, dilution %; I, welding current; S, welding speed; N, nozzle-to-plate distance; T, welding torch angle.
3.6. Development of mathematical models
The response function representing any of the clad quality parameters can
be expressed using Eq. (2)
Y = f(X1, X2, X3, X4) (2)
where Y is the response (e.g. weld bead width), X1 the welding current (I) (A),
X2 the welding speed (S) (cm/min), X3 the nozzle-to-plate-distance (N) (mm)
and, X4 the welding torch angle (T) (◦).
The second order response surface model [10] for the four selected factors
is given by Eq. (3)
Y = βo +
4
i=1
βiXi +
4
i=1
βiiX2
i +
4
i = 1
i<j
βijXiXj (3)
The above second order response surface model equation could be expressed as
follows:
Y = β0 + β1I + β2S + β3N + β4T + β11I2
+ β22S2
+ β33N2
+ β44T2
+β12IS + β13IN + β14IT + β23SN + β24ST + β34NT (4)
where β0 is the free term of the regression equation, the coefficients β1, β2, β3
and β4 are linear terms, the coefficients β11, β22, β33, and β44 the quadratic
terms, and the coefficients β12, β13, β14, β23, β24, and β34 the interaction
terms.
The values of the coefficients of the polynomial Eq. (4) were calculated [11]
using following Eqs. (5)–(8)
β0 = 0.142857 Y − 0.035714 (XiiY) (5)
βi = 0.041667 (XiY) (6)
βii = 0.03125 (XiiY) + 0.035714 (XiiY) − 0.035715 Y (7)
βij = 0.0625 (XijY) (8)
The coefficients were calculated using QA six sigma software (DOE-PCIV) and
the same was verified by using the software SYSTAT 10.2. After determining
the coefficients, the mathematical models were developed. The insignificant
coefficients were eliminated without affecting the accuracy of the developed
model by using t-test. This was done by back elimination technique, which is
available in QA six sigma software (DOE-PCIV) and the same was verified
by using the software SYSTAT 10.2. The significant coefficients are given in
Table 4. The final mathematical models were constructed by using only these
coefficients. The developed final models with welding variables in coded form
are given below.
Bead width (W) (mm) = 27.775 + 2.494I − 3.244S + 0.415N − 0.610T
−0.303I2 + 1.066S2 + 0.316T2 − 0.616IS.
Average depth of penetration (P) (mm) = 0.764 + 0.104I + 0.074S − 0.048N +
0.110T + 0.021S2 + 0.061ST.

5. 234 T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239
Table 4
Estimated values of the significant coefficients of the models
Coefficient Clad quality parameters
W P R D
βo 27.775 0.764 4.535 11.702
β1 2.494 0.104 0.128 1.466
β2 −3.244 0.074 −0.475 2.730
β3 0.415 −0.048 0.054 −1.037
β4 −0.610 0.110 0.052 1.608
β11 −0.303 – – –
β22 1.066 0.021 0.053 –
β33 – – – –
β44 0.316 – – –
β12 −0.616 – – −0.751
β13 – – – −0.593
β14 – – – –
β23 – – −0.052 –
β24 – 0.061 – 1.482
β34 – – – –
Average height of reinforcement (R) (mm) = 4.535 + 0.128I − 0.475S +
0.054N + 0.052T + 0.053 S2 − 0.052SN.
Percentage dilution (D) = 11.702 + 1.466I + 2.730S − 1.037N + 1.608T −
0.751IS − 0.593IN + 1.482ST.
It was found that the reduced models are better than the full models because
the reduced models have higher values of R2 (adjusted) and lesser values of
standard error of estimates than that of full models. The values of R2 (adjusted)
and standard error of estimates for full and reduced models are given in
Table 5.
3.7. Checking the adequacy of the developed models
The adequacies of the developed models were tested using the analysis of
variance (ANOVA) technique [12]. As per this technique, if the calculated F-
ratio values for the developed models do not exceed the standard tabulated
values of F-ratio for a desired level of confidence (95%) and the calculated R-
ratio values of the developed models exceed the standard tabulated values of
R-ratio for a desired level of confidence (95%), then the models are said to be
adequate within the confidence limit. These conditions were satisfied for all the
developed models, which are given in Table 6. The validity of these models,
were again tested by drawing scatter diagrams as shown in Fig. 6a–d which
show the observed and predicted values of clad quality parameters.
3.8. Conducting the conformity test
Conformity tests were conducted using the same experimental setup to con-
firm the results of the experiment and demonstrate the reliability of the predicted
values. The conformity tests show the accuracy of the models developed, which
is above 96%. This is shown in Table 7.
4. Results and discussions
The models developed above can be used to predict the clad
qualityparametersbysubstitutingthecodedvaluesoftherespec-
tive process parameters. The responses calculated from these
models for each set of coded welding parameters are represented
in graphical form in Figs. 7–16. Also by substituting the values
of the desired clad quality parameters, the values of the process
parameters, in coded form can be obtained.
4.1. Direct effects of process parameters on clad quality
parameters
4.1.1. Direct effect of welding current (I) on clad quality
parameters
Fig. 7 shows all the clad quality parameters W, P, R, and
D increase with increase in welding current. This is due to the
increase in welding current density and the weight of wire fused
per unit of time [13]. Also with increase in welding current the
arc becomes stiffer and hotter which penetrates more deeply and
melting more base metal.
4.1.2. Direct effect of welding speed (S) on clad quality
parameters
From Fig. 8, it is evident that the clad quality parameters R
and W decrease with increase in welding speed but P and D
Table 5
Comparison of R2 values and standard error of estimates for full and reduced models
Clad quality parameter R2 values Standard error of estimates
Full models Reduced models Full models Reduced models
Width (W) 0.856 0.889 1.552 1.365
Penetration (P) 0.816 0.839 0.075 0.070
Reinforcement (R) 0.884 0.900 0.165 0.153
%Dilution (D) 0.793 0.836 1.707 1.515
Table 6
Analysis of variance for testing adequacy of the models
Parameter 1st order Terms 2nd order Terms Lack of fit Error terms F-ratio R-ratio Whether model is adequate
SS d.f. SS d.f. SS d.f. SS d.f.
W 414.845 4 48.00 10 26.016 10 12.499 6 1.249 15.870 Adequate
P 0.732 4 0.010 10 0.073 10 0.018 6 2.477 20.149 Adequate
R 6.164 4 0.400 10 0.368 10 0.066 6 3.353 42.719 Adequate
D 318.38 4 56.142 10 34.20 10 12.24 6 1.677 13.119 Adequate
F-ratio (10, 6, 0.05) = 4.09; SS, sum of squares; R-ratio (14, 6, 0.05) = 3.96; d.f., degrees of freedom.

6. T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239 235
Table 7
Comparison of predicted and actual values of clad quality parameters
Process parameter in coded form Predicted values of clad quality parameters Actual values of clad quality parameters Error (%)
I S N T W P R D W P R D W P R D
−0.11 −0.22 0.09 −0.3 28.9 0.72 4.7 10.6 28.6 0.67 4.8 10.3 −1.04 −2.86 2.13 −2.80
−0.79 −0.35 0.94 1.02 27.3 0.72 4.8 10.3 27.7 0.71 4.6 10.2 1.47 −1.39 −4.17 −0.97
−0.66 0.03 0.90 1.03 26.3 0.77 4.6 11.5 26 0.78 4.7 11.8 −1.14 1.30 2.17 −2.61
%Error = actual value−predicted value
predicted value × 100.
Fig. 6. Scatter diagram of (a) weld bead width model; (b) depth of penetration model; (c) percentage dilution model; and (d) height of reinforcement model.

7. 236 T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239
Fig. 7. Effect of welding current on clad quality parameters.
increases with increase in welding speed. Decrease in R and W
can be obviously attributed to the reduced heat input per unit
length of weld bead as welding speed is increased and less filler
metal is applied per unit length of the weld [13]. The percentage
dilution of base metal in the pool increases with the increase
in welding speed, since the weight of deposited metal per unit
of length decreases with the cross section of the bead decreases
very little. With low welding speed the arc is almost vertical
and in this instance the weld pool cushions the effect of arc and
prevents deeper penetration.
4.1.3. Direct effect of nozzle-to-plate distance (N) on clad
quality parameters
It is evident from Fig. 9 that P and D decrease slightly
with increase in nozzle-to-plate distance but W and R increases
Fig. 8. Effect of welding speed on clad quality parameters.
Fig. 9. Effect of nozzle-to-plate distance on clad quality parameters.
with increase in nozzle-to-plate distance. Increase in nozzle-
to-plate distance increases the circuit resistance, which reduces
the welding current. This decrease of welding current reduces
the penetration of arc and hence reduces the dilution. With
increase in nozzle-to-plate distance the arc length is increased
hence the bead width is increased due to wider arc area at
the weld surface and this consequently increases the reinforce-
ment height because the same volume of filler metal is added
[14].
4.1.4. Direct effect of welding torch angle (T) on clad
quality parameters
From Fig. 10 R, P and D decrease with increase in weld-
ing torch angle but W increases with increase in welding torch
angle. The reason is when the angle is increased in fore-
hand welding the arc force pushes the weld metal forward, i.e.
Fig. 10. Effect of welding torch angle on clad quality parameters.

8. T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239 237
Fig. 11. Interaction effects of welding current and welding speed on bead width.
towards the cold metal, which reduces the penetration, reinforce-
ment and percentage dilution but width of the weld increases
[15].
4.2. Interaction effects of process parameters on clad
quality parameters
4.2.1. Interaction effects of welding current (I) and welding
speed (S) on bead width (W)
From Fig. 11 it is clear that the W increases with increase
in welding current. It also increases with decrease in welding
Fig. 12. Interaction effects of welding torch angle and welding speed on pene-
tration.
Fig. 13. Interaction effects of nozzle-to-plate distance and welding speed on
reinforcement.
speed. These effects are due to welding current having a pos-
itive effect but welding speed having negative effect on bead
width. The increasing trend of weld bead width with increase
in welding current decreases with the increase in welding
speed.
4.2.2. Interaction effects of welding torch angle (T) and
welding speed (S) on average depth of penetration (P)
Fig. 12 shows that the P decreases with increase in welding
torch angle. This decreasing trend of P with increase in welding
torch angle gradually decreases with decrease in welding speed.
Fig. 14. Interaction effects of welding current and welding speed on percentage
dilution.

9. 238 T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239
Fig. 15. Interaction effects of nozzle-to-plate distance and welding current on
percentage dilution.
This is because welding torch angle has negative effect and
welding speed has positive effect on penetration.
4.2.3. Interaction effects of nozzle-to-plate distance (N) and
welding speed (S) on average height of reinforcement (R)
From Fig. 13, it is evident that R increases with the increase in
nozzle-to-plate distance when welding speed is from 20 cm/min
to 50 cm/min and the rate of increase in R also decreases with
the increase in welding speed. But when welding speed is at
60 cm/min, R decreases with increase in nozzle-to-plate dis-
tance.
Fig. 16. Interaction effects of welding speed and welding torch angle on per-
centage dilution.
4.2.4. Interaction effects of welding current (I) and welding
speed (S) on percentage dilution (D)
From Fig. 14, it is clear that D increases with increase in weld-
ing current for all values of welding speed. But this increasing
trend of D with the increase in welding current decreases grad-
ually with the decrease in welding speed. These effects occur
because both welding current and welding speed have a positive
effect on percentage dilution.
4.2.5. Interaction effects of welding current (I) and
nozzle-to-plate distance (N) on percentage dilution (D)
From Fig. 15, it is clear that D decreases with increase in
nozzle-to-plate distance for all values of welding current. But
this decreasing trend of D with the increase in nozzle-to-plate
distance decreases gradually with decrease in welding current.
These effects occur because welding current has a positive effect
whereas nozzle-to-plate distance has a negative effect on D.
4.2.6. Interaction effects of welding torch angle (T) and
welding speed (S) on percentage dilution (D)
From Fig. 16, it is evident that D decreases with the increase
in welding torch angle when welding speed is from 30 cm/min
to 60 cm/min and the rate of decrease in D also increases with
increase in welding speed from 30 cm/min. But when welding
speed is 20 cm/min D increases with the increase in welding
torch angle.
5. Conclusions
• Afivelevelfourfactorfullfactorialdesignmatrixbasedonthe
central composite rotatable design technique can be used for
the development of mathematical models to predict the clad
quality parameters for duplex stainless steel cladding using
FCAW.
• Dilution increases with the rise in welding current and weld-
ing speed and decreases with the rise in nozzle-to-plate dis-
tance and welding torch angle.
• Reinforcement increases with the rise in welding current and
nozzle-to-platedistanceanddecreaseswiththeriseinwelding
speed and welding torch angle.
• Weld bead width increases with the rise in welding cur-
rent, nozzle-to-plate distance and welding torch angle and
decreases with the rise in welding speed.
• Penetration increases with the rise in welding current and
welding speed and decreases with the rise in nozzle-to-plate
distance and welding torch angle.
• Bead width increases with the increase in welding current at
all levels of welding speed. But the rate of increase in bead
width with the increase in welding current decreases signifi-
cantly with the increase in welding speed.
• Increase in welding torch angle decreases penetration when
welding speed is high but penetration slightly increases with
the increase in welding torch angle when welding speed is
low.
• Percentage dilution decreases with the increase in nozzle-
to-plate distance at all levels of welding current. But the
rate of decrease in percentage dilution with the increase in

10. T. Kannan, N. Murugan / Journal of Materials Processing Technology 176 (2006) 230–239 239
nozzle-to-plate distance decreases significantly with decrease
in welding current.
Acknowledgements
The authors wish to thank M/S Bohler welding, Austria for
providing flux cored welding wire for this work. The finan-
cial support for this work from All India Council of Techni-
cal Education and University Grant Commission is gratefully
acknowledged. The authors also wish to thank the management
of Coimbatore Institute of Technology and Kumaraguru College
of Technology for having provided all the necessary facilities to
carryout this work.
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