Journal of Mechanical Science and Technology 31 (3) (2017) 1341~1347
www.springerlink.com/content/1738-494x(Print)/1976-3824(Online)
DOI 10.1007/s12206-017-0234-x
Effect of process parameters on ferrite number in cladding of
317L stainless steel by pulsed MIG welding†
R. Prabhu1,* and T. Alwarsamy2
1
Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, Enathur, Kancheepuram, 631561, Tamil Nadu, India
2
Government College of Technology, Coimbatore-13, Tamil Nadu, India
(Manuscript Received May 27, 2015; Revised March 15, 2016; Accepted October 26, 2016)
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Abstract
In the current scenario cladding process are used in many engineering industries to enhance the corrosion resistance surface and wear
resistance of the base material in chloride environment. Cladding of stainless steel results in formation of ferrite number content on cladded surface, which deleterious the material properties. The present investigation address the effect of process parameters on ferrite number during austenitic stainless steel 317L cladding by Pulsed Metal inert gas (MIG) welding process. Ferrite number was measured by
using Fisher’s ferrite scope. Response surface methodology (RSM) based central composite rotatable design were approached to predict
and develop the mathematical model for process parameters such as welding current, welding speed and contact tip to work distance on
ferrite number. From the analysis of variance technique it is found that the developed mathematical model was significant. The developed mathematical model is useful to control and determine the ferrite number content in austenitic stainless steel cladding. The direct
and interaction effects of input process parameters are presented graphically.
Keywords: Ferrite number; Pulse cladding; Prediction; Response surface methodology; Stainless steel
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1. Introduction
Austenitic stainless steels are widely used in a variety of
product forms for architectural consumer and industrial application because of their excellent corrosion and oxidant resistance however these are susceptible to corrosion during welding [1]. Corrosion is a problem which dwindles the steel structure leads to failure. Though it cannot be eradicated it can be
reduced to certain extent. Corrosion resistance protective layer
is formed over the base metal by a process called weld cladding [2].
The microstructure of austenitic stainless steel shows a delta
ferrite phase during cladding generally, delta ferrite phase
leads to decremented effect on corrosion resistance [3].
Among fusion welding process pulsed metal inert gas welding
has been widely used for cladding austenitic stainless steel due
to several advantages like easy to control the process parameters, to produce good bead dimensions, minimising the percentage of dilution, less spatter, fumes and high metal deposition rate [4, 5]. To obtain the better quality welds, it is necessary to have a full control over the relevant process parameters
to get the required bead geometry dimensions [6, 7].
*
Corresponding author. Tel.: +91 9488458583
E-mail address: prabhu9495@gmail.com
†
Recommended by Associate Editor Young Whan Park
© KSME & Springer 2017
Amos Robert et al. [8] investigated the behaviour of process
parameters during stainless steel cladding they concluded that
stand of distance is the dominant parameter to decide the bead
appearance than with diffusion temperature and welding speed.
Palani and Murugan [9] carried out the optimisation study
of weld bead geometry for stainless steel cladding deposited
by Flux cored arc welding. They found that percentage of
dilution increases the pitting corrosion which is initiated by
presence of ferrite content during cladding. Rho et al. [10]
studied the effects of delta ferrite content on the 304L stainless
steel during continuous low-cycle fatigue test with different
temperatures. They concluded that the fatigue crack was initiated by delta ferrite on the surface.
Sudhakaran et al. [11] investigated the effect of process parameters on ferrite content of 202 grade stainless steel using
response surface methodology in gas tungsten arc welding
process in that they used Delong diagram and ferrite scope to
measure the ferrite number. Lia et al. [12] focused on the formation and microstructural evolution of delta ferrite phase in
SAVE12 steel. Further they demonstrated that the formation
of delta ferrite was due to the high content of ferrite forming
alloy elements.
Corrosion resistance of the base material is mainly depends
on the chemical composition of filler material during cladding.
The more amount of the ferrite number results in poor mate-
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R. Prabhu and T. Alwarsamy / Journal of Mechanical Science and Technology 31 (3) (2017) 1341~1347
rial property. Hence it is very important to obtain the optimal
number of ferrite number during cladding [13]. In this row
Kannan and Murugan [14] predicted the ferrite number of
duplex stainless steel cladded metals by using response surface methodology.
The mathematical models were developed to predict the
delta ferrite content during cladding of austenitic stainless
steel and duplex stainless steel using response surface methodology [15, 16]. Vasudevan et al. [17] used Bayesian neural
network to develop the mathematical for the prediction of
delta ferrite content in stainless steel weld.
Rao et al. [18] studied the effect of process parameters on
bead geometry during pulsed gas metal arc welding using
Taguchi method. Mathematic models were developed using
multiple regression analysis and validated the adequacy. Further it was found that predicted models for the responses are
within reasonable accuracy.
Pal et al. [19] made a study on optimization of process parameters for quality characteristic by using grey-based Taguchi method. It was found that the process parameter has
more influence on quality characteristic by ANOVA. Response surface methodology based central composite rotatable
design in design expert software technique is mostly used for
developing the experimental design matrix. It is the simplest
method for developing a mathematical model and correlate
relationship between process parameters and response. It is
useful to discuss the main effect and interaction effects [2022].
The above literatures clearly show that identifying and selecting the process parameters for the cladding process is challenging one for many research works. However response surface methodology based central composite rotatable design
technique was used by various researchers for developing
mathematical model and analysing the process parameters.
Hence the study related to prediction of ferrite number in
Pulsed MIG welding was not that much discussed.
There was various attempts have been made to develop the
mathematical model for prediction of ferrite number by using
nickel and chromium alloy composition. It was found that
very few works was discussed about prediction of ferrite
number based on process parameters.
In this present investigation, the effects of process parameters on ferrite number in Pulsed MIG welding during 317L
solid stainless steel wire cladded on structural steel plate IS:
2062 was studied by following approach.
By conducting the weld cladding of 317L austenitic stainless steel onto structural steel IS: 2062. Preparing the specimen as per metallurgical procedure and ferrite number was
measured by using Fishers ferrite scope. By developing the
quadratic polynomial equation and analysing their effects.
Table 1. Chemical composition of base material and filler wire
(Weight percent).
Materials
Base
material
Filler
wire
C
Si
Mn
P
S
Cr
Ni
Cu
Mo
Fe
0.23 0.31 0.93 0.05 0.02
-
-
-
-
98.46
0.03 0.65 2.5 0.04 0.03
20
15
0.75
4
57
Table 2. Process variables and experimental levels.
Process
Symbols
parameters
Levels
-1.682
-1
0
+1
+1.682
I
190
200
215
230
240
Welding
speed
S
170
180
195
210
220
Contact tip to
work distance
N
17
18
20
21
22
Welding
current
solid wire of diameter 1.2 mm was used as filler wire and low
carbon structural steel IS: 2062 of 20 mm thickness plate was
used as base material. The chemical composition of the base
material and filler wire are shown in Table 1.
2.2 Experimental methods
There are various methods to conduct the experiments such
as Taguchi method [19], grey relational analysis, orthogonal
array, full factorial design [20], and artificial intelligence [17],
face centered composite design and central composite rotatable design [11, 22]. Response surface methodology based
central composite rotatable design technique was used to design the experimental matrix to perform the experiments. This
design is generally used by maximum number of researchers
for obtaining minimum number of experimental runs with
good accuracy and developing the second order polynomial
regression equation [11, 15]. Based on the literature analysis,
the three factors are considered for this present investigation
such as welding current (I), welding speed (S) and contact tip
to work distance (N). Bead-on-plate type welding were performed with changing the process parameters values and examine the appearance of the bead process parameters levels
are selected for this investigation, their upper limits and lower
limits of each process parameters are fixed respectively. The
upper limits and lower limits of a process parameters was
coded as +1.682 and -1.682 respectively, the coded values of
the intermediate levels being calculated from the Eq. (1)
shown in Table 2 [9].
(1)
2. Experimentation
2.1 Experimental materials
In this investigation austenitic stainless steel AISI 317L of
where Xi is the required coded value of a variable X, X is any
value of the variable from X min to X max, X min is the lower
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Table 3. Design matrix and ferrite number values.
Fig. 1. Typical view of cladded specimen.
Fig. 2. Mirror finished specimens for ferrite number measurement.
level of the variable, X max is the highest level of the variable
the first 8 conditions were derived from factorial experimental
design matrix (23 = 8). All the variables at an intermediate
level of 0 constitute the 6 center point while, the combination
of each process variable between a lower level of −1.682 and
a higher level of +1.682 with the other four variables at the
intermediate levels constitute the 6 star points totally 20 runs
are there [9, 22].
2.3 Experimental procedure for cladding
The deposition was performed as per the design matrix by
using Lincoln power wave 455 Pulsed MIG welding machine.
A programmable logic controlled manipulator was used to set
and control the welding speed uniformly for each run.
The contact tip to work distance is also adjusted with help
of the manipulator and position of the torch is exactly maintained at 90° throughout the experiments. Base material was
prepared as per the required dimensions such as 300 x 200 x
20 mm form structural steel plate IS: 2062. Top surface of the
base material was cleaned with steel wire brush and emery
sheets to free it from dirt and rust layer. A gas mixture of Argon and Carbon dioxide (95 % and 5 %) are supplied throughout the experiments at the flow rate of 20 lit/min as shielding
gas. The base material, filler material and dimensions of the
specimen is indicated in Fig 1, weld cladded mirror finished
cladded specimens are shown in Fig. 2.
2.4 Experimental procedure for specimen preparation and
ferrite number measurement
The cladded plates are cross-sectioned at the midpoint to
prepare the test specimen, the top surface of the specimens are
grounded as flat surface without disturbing the bead geometry
Trails
Avg
FN
values
Std
exp
Run
exp
I
1
3
-1
-1
-1
0.8 0.84 0.81 0.82 0.83
0.82
2
8
1
-1
-1
0.86 0.88 0.87 0.89 0.9
0.88
3
5
-1
1
-1
0.64 0.63 0.52 0.6 0.61
0.6
4
20
1
1
-1
0.57 0.58 0.62 0.61 0.57
0.59
5
6
-1
-1
1
0.56 0.58 0.6 0.61 0.55
0.58
6
7
1
-1
1
0.84 0.8 0.82 0.78 0.81
0.81
7
9
-1
1
1
0.62 0.6 0.59 0.65 0.64
0.62
8
10
1
1
1
0.81 0.84 0.83 0.82 0.85
0.83
9
11 -1.682
0
0
0.73 0.75 0.77 0.74 0.71
0.74
10
4
1.682
0
0
0.97 0.99 0.92 1.04 0.98
0.98
11
2
0
-1.682
0
0.73 0.74 0.75 0.72 0.71
0.73
12
15
0
1.682
0
0.54 0.54 0.52 0.6 0.55
0.55
13
14
0
0
-1.682 0.52 0.54 0.51 0.58 0.55
0.54
14
17
0
0
1.682 0.59 0.6 0.58 0.57 0.61
0.59
15
1
0
0
0
16
13
0
0
17
18
0
0
18
12
0
19
16
20
19
S
N
1
2
3
4
5
0.58 0.54 0.52 0.6 0.55
0.56
0
0.5 0.54 0.52 0.6 0.55
0.55
0
0.57 0.59 0.6 0.61 0.55
0.58
0
0
0.6 0.58 0.59 0.55 0.53
0.57
0
0
0
0.57 0.59 0.6 0.61 0.55
0.58
0
0
0
0.58 0.54 0.52 0.6 0.55
0.56
and surface texture [11, 13]. It was then polished with etching
solution and made as mirror finish by following metallurgical
procedure [12]. Fischer FERRITESCOPE_MP30 were used
to measure the ferrite number on cladded specimen [9, 11, 13,
14], measurement ranges from 0.1 to 110 ferrite number or 0.1
to 80 % ferrite in austenitic and duplex stainless steel. A schematic arrangement of ferrite scope and specimen are shown in
Fig. 3. Before measuring the ferrite number the instrument has
to be calibrated according to existing prepared specimen and
ferrite form by comparing with ANSI/AWS A4.2M/A4.2:
1997 [11, 16] standard specimen. The values are measured on
the prepared surface of the 20 specimens and in each specimen there are five values are measured along the axis of the
deposition and averages of these measured values are tabulated in Table 3.
2.5 Development of mathematical model
Ferrite number (Y) of cladded stainless steel is the response
function of process parameters such as welding current, welding speed and contact tip to work distance can be expressed by
Eq. (2) [20-22].
Y = (I, S, N)
where Y : The response function,
I : The welding current amps,
(2)
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Table 4. Calculated β coefficients values for ferrite number model.
Table 5. ANOVA table for ferrite number.
Process parameters
Polynominal coefficients
Coefficients values
Constant
β0
47.5088
Source
SS
DoF
MS
F value
p-value
Prob > F
I
β1
-0.2332
Model
0.33219
9
0.03691
119.279
< 0.0001 Significant
S
β2
-0.1058
I
0.05847
1
0.05847
188.969
< 0.0001
0.04149
1
0.04149
134.074
< 0.0001
1.5E-08
1
1.5E-08
4.9E-05
0.9946
N
Β3
-1.1966
S
IxS
β12
-5E-05
N
IxN
β13
0.0022
IS
0.00101
1
0.00101
3.27205
0.1006
0.01901
1
0.01901
61.4419
< 0.0001
0.04061
1
0.04061
131.246
< 0.0001
SxN
β23
0.0032
IN
IxI
β11
0.00048
SN
SxS
β 22
NxN
β 33
2
0.00013
I
0.16591
1
0.16591
536.167
< 0.0001
0.0029
S2
0.01255
1
0.01255
40.5703
< 0.0001
N2
0.00062
1
0.00062
1.98823
0.1889
Residual 0.00309
10
0.00031
Lack of fit 0.00236
5
0.00047
3.21962
0.1126
Pure error 0.00073
5
0.00015
Cor total 0.33528
19
Not
significant
Eq. (4).
Ferrite number = + 47.5088 - 0.2332 * I -0.1058 *
S - 1.1966 * N -5E-05* I * S + 0.00217 * I * N +
0.00317 * S* N + 0.00048 * I2 + 0.00013 * S2 +
0.0029 * N2.
Fig. 3. Schematic arrangement of ferrite scope and specimen.
S : The welding speed mm/min and
N : The contact tip to work distance mm.
3.1 Validation of developed mathematical model
Relationship between the response and the process variable
are unknown. In order to study the main effect and interaction
of the process parameters a second order polynomial response
surface can be fitted into the following Eq. (3) [21, 22].
k
k
y = b 0 + å b i xi + å b ii xi2 + åå b ij xi x j + e
i =1
i =1
i
(4)
(3)
j
i≠j
where βo is the free term of the regression equation.
The coefficients of β1, β2 and β3 are linear terms.
The coefficients of β11, β22 and β33 are the quadratic terms.
The coefficients of β12, β13 and β23 are the interaction terms.
2.6 Calculating the coefficients of the models
The values of the β coefficients in the second order polynomial equation are calculated by using design expert software and coefficients values are tabulated in Table 4.
3. Development of final mathematical model
The final mathematical model in actual variable form are
determined by following above procedure and represented as
From the above mathematical model, the effects of process
parameters on ferrite number were examined. The Eq. (4) it is
clear that the positive values of the polynomial coefficients
increases and negative values decreases the response. Analysis
of variance technique was further used to validate the adequacy of the developed mathematical model. The F-value of
119.28 implies the model is significant at 95 % of confident
level. To check the goodness of the fitted model the coefficient of determination R2 and adjusted coefficient of determination R2 are calculated. The value of calculated R2 should lie
on 0-1 accordingly R2 is 0.99 it is very closer to 1 indicated
that the developed model is good. Adjusted coefficient of
determination R2 is 0.98 which is high nearer to coefficient of
determination R2 0.9908 agrees that model is adequate it was
shown in Table 5. The coefficient of variation and adequate
precision ratio are found 2.65 and 33.986, respectively. The
coefficient of variation is less than 5 and adequate precision
ratio is greater than 4.
3.2 Scatter diagram
Further the validity of developed regression models are
validated by constructing scatter diagram. A typical scatter
diagram for the ferrite number was shown in Fig. 4. The scatter diagram shows that the predicated values and measured
R. Prabhu and T. Alwarsamy / Journal of Mechanical Science and Technology 31 (3) (2017) 1341~1347
1345
Table 6. Conformation test parameters.
Welding
speed
Contact tip
Predicated Measured
to work
Error %
FN
FN
distance
No
Welding
current
1
212.55
202.7
19.46
0.53791
0.55138
2.50413
2
207.89
198.02
20.15
0.54783
0.56771
3.62886
3
209.83
201.08
18.91
0.53662
0.53210
0.84231
4
217.53
205.16
18.93
0.53467
0.54961
2.79424
Fig. 5. Effect of welding current on Ferrite number.
Fig. 4. Normal probability of residuals for Ferrite number.
values of ferrite number are very closer to each other which
indicating the developed regression model an almost perfect
fit [12, 15, 16].
Fig. 6. Effect of welding speed on Ferrite number.
4. Conformation test
The conformation test was conducted to evaluate the developed polynomial equation Eq. (4). The three conformation test
run were conducted with different combination of process
parameters predicted from software. The ferrite number was
measured and differences in predicted and measured values
are calculated by using error calculation equation Eq. (5). The
values are tabulated in Table 6.
% Error =
Measured value - Predicted value
* 100 .
Predicted value
(5)
5. Results and discussion
RSM based central composite rotatable design was used to
develop the mathematical model and predict the effect of
process parameters in this investigation. The direct effect and
interaction effects of process parameters on ferrite number are
presented graphically in Figs. 5-7.
5.1 Effect of welding current on Ferrite number
Fig. 5 shows that an increase in welding current decrease
the ferrite number up to the mid value of the welding current
level, thereafter there is sudden increase in ferrite number.
This is mainly attributed to the fact that at higher current level
the heat input to the base metal is higher which increases the
ferrite number.
Fig. 7. Effect of contact tip to work distance on Ferrite number.
5.2 Effect of welding speed on ferrite number
In Fig. 6, it is clear that there is continuous decrease in ferrite number along with all the levels of welding speed, hence
when welding speed increases the deposition of the filler material also decreases thus results in reducing the ferrite content.
5.3 Effect of contact tip to work distance on ferrite number
The direct effect of contact tip to work distance on ferrite
number was shown in Fig. 7. It does not affect the response in
any levels which shows that it is an insignificant factor for
ferrite number measurement.22.
5.4 Interaction effect of welding current and welding speed
on ferrite number
The interaction effects of welding current and welding
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R. Prabhu and T. Alwarsamy / Journal of Mechanical Science and Technology 31 (3) (2017) 1341~1347
considering the contact tip to work distance.
6. Conclusion
Fig. 8. 3D Surface plot showing an interaction effect of welding current and welding speed on ferrite number.
·A five-level, three factor design matrix based on central
composite rotatable design approach was used for the
development of mathematical models to predict the ferrite number for cladding of austenitic stainless steel 317L
deposited using pulsed MIG welding process.
·The predicted values using developed model as well as
experimental values are closer to each other.
·Welding current and welding speed are the most significant factor in prediction of ferrite number than with the
contact tip to work distance.
·The significant factors are identified by ANOVA. The
model values are adequate with 95 % of confidence level.
·Ferrite number decreases constantly with the increase in
welding speed due to the reduced deposition of filler material over the base metal. However, the ferrite number
increases with the increase in welding current.
Nomenclature------------------------------------------------------------------------
Fig. 9. 3D Surface plot showing an interaction effects of Welding
speed and contact tip to work distance on Ferrite number.
speed on ferrite number are shown in Fig. 8. It is clear that at
lower level of the welding current with lower level of welding
speed the ferrite number was considerably at mid value of the
graph. But when the welding current increases there are
downstream and rapid upstream in ferrite number was observed. However, when the welding current increases, the
melting rate and deposition rate of filler material also increases. The weld pool which gives enough of time for ferrous
material to dominate alloying element by diffusion and the
ferrous materials occupies top surfaces of the cladded surface
to increase ferrite content. On the other hand it was noticed
that welding speed also plays an important role to control the
ferrite number by increasing their levels.
5.5 Interaction effect of welding speed and contact tip to
work distance on ferrite number
It is evident that the ferrite number was drastically decreases with increase in welding speed as compared to welding speed (Fig. 9). Because at low levels of welding speed the
axial or longitudinal movement of the nozzle is very slow at
the same time the contact tip to work distance is also at lower
level as it is close to the base material. Hence both the values
are at low level, the cushioning effect of the weld zone increases the penetration of the filler material. The increase in
penetration causes increases in ferrite number. But when
welding speed increases there in no cushioning on the weld
zone which drastically decreases the ferrite number without
I
S
N
FN
SS
DoF
: Welding current
: Welding speed
: Contact tip to work distance
: Ferrite number
: Sum of squares
: Degree of freedom
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optimization of features of bead geometry including percentage dilution in submerged arc welding using mixture of fresh
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R. Prabhu graduated in Mechanical
Engineering from Anna University,
Chennai, India in 2005 and received his
Masters in Manufacturing Engineering
from Anna University, Coimbatore,
India in 2009. He is currently an Assistant Professor in the Department of Mechanical Engineering in Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University Kanchipuram, India. His research is in welding of dissimilar metals stainless steel and low carbon steel, corrosion analysis,
wear behavior of cladded surface study and currently extending his knowledge by undergoing Ph.D. studies in pulsed MIG
welding.