The document summarizes an experimental study that used Response Surface Methodology (RSM) to develop a model for predicting weld strength in CO2 arc welding of AISI 1022 steel plates. The study investigated how welding current, voltage, wire feed rate, and gas pressure affect weld strength. Central composite design was used to test various parameter combinations in 31 experimental runs. Regression analysis found that current, voltage, wire feed rate, and gas pressure significantly affect weld strength in linear terms. An RSM model was developed to predict weld strength as a function of the parameters. The model fits the experimental data with 98.8% accuracy.

1. 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. 37-42
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IJMET
©IAEME
EXPERIMENTAL STUDY OF CO2 ARC WELDING PARAMETERS ON
WELD STRENGTH FOR AISI 1022 STEEL PLATES USING RESPONSE
SURFACE METHODOLOGY
Mr. Shukla B.A.(1),
Prof. Phafat N.G.(2)
(1)
Student, M.E. Manufacturing, Mechanical Engineering Department, J.N.E.C. Aurangabad,
Maharashtra, India
(2)
Associate Professor, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra,
India
ABSTRACT
This paper focuses on the investigation of CO2 welding parameters to maximize the weld
strength using Response Surface Methodology. Welding current, welding voltage, wire feed rate and
gas pressure was taken as input parameters while the response was only weld strength. Central
Composite Design was chosen for the experimental design. RSM based model has been developed to
determine the weld strength attained by various welding parameters. The quadratic models
developed using RSM shows high accuracy and can be used for prediction within the limits of the
factors investigated.
Keywords: CO2 Welding, AISI 1022, RSM, Weld Strength.
1. INTRODUCTION
CO2 arc welding is one of the major welding process used in industries like automobile,
aircraft industries, railway industries due to its cheaper rates, ease of availability and good deposition
rate.
Weld strength is one of the most important term in welded joints. The life of the welded joint
depends on the weld strength, higher the weld strength higher is the life of the joint. Weld strength
also increases the load bearing capacity of the welded joint, less load bearing capacity is the most
undesirable property in automobile industries. Pinholes, cracks and porosity are the influential
factors for the decrease in weld strength, therefore while welding operation care must be taken to
minimize these defects or eliminate it. Due to all these problems the welding cost increases, there is
waste of time and money, more number of rejected components in the industries. Industries in which
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
welding is a critical enabling technology account for 59% of the total value of production by all
Manufacturing, Construction, and Mining industries.
K. Lalitnarayan, et al [1] studied the effect of CO2 welding parameters using multiple
regression analysis and inverse transformation. M.R. Nakhaei et al [2] conducted an experiment on
laser CO2 welding using Taguchi technique. Ampaiboon A. and Lasunon O. [3] has done the
optimization of joint strength in CO2 welding using Response surface methodology. H.H.Na et al [4]
studied the interaction between process parameters and bead geometry in GMAW using Taguchi
technique. S.W. Campbell et al [5] performed ANN prediction of weld geometry using gas metal arc
welding (GMAW) with alternate shielding gases. S.V. Sapakal and M.T. Telsang [6] has performed
the parametric optimization of MIG welding using Taguchi design method. Vinod Kumar [7] had
performed modeling of weld bead geometry and shape relationships using RSM technique.
As weld strength is very important phenomenon affected by many parameters like type of
material used, welding current, welding voltage etc., Therefore it becomes necessary to develop a
reliable model that predicts the weld strength to reduce the cost of welding, time and money. The
important process parameters are determined based on the literature review carried out on weld
strength. In this investigation an RSM model is developed which predicts the weld strength. RSM is
selected because of its capability to learn and simplify from examples and adjust to changing
conditions. In addition they can be applied in manufacturing area as they are an effective tool to
model non linear systems.
2. RESPONSE SURFACE METHODOLOGY
Response Surface Methodology is one of the optimization techniques in describing the
performance of the welding process and finding the optimum setting of parameters. RSM is a
mathematical-statistical method that used for modeling and predicting the response of interest
affected by some input variables to optimize the response [8].
RSM also specifies the relationships among one or more measured responses and the
essential controllable input factors. When all independent variables are measurable, controllable and
continuous in the process, with negligible error, the response surface model is as follows [8]:
(1)
y= f(x1,x2,…xn)
where “n” is the number of independent variables.
To optimize the response “y”, it is necessary to find an appropriate approximation for the true
functional relationship between the independent variables and the response surface. Usually a
second-order polynomial Equation (2) is used in RSM.
k
k
j =1
j =1
k −1 k
y = β 0 + ∑ β j x j + ∑ β jj x 2 + ∑ ∑ β ij xi x j + ε
j
i
(2)
j
3. EXPERIMENTAL WORK
AISI 1022 steel plates of 100 (length)*90 (width)*6 (thickness) was used as work piece
material for square butt welding in the given study. AISI 1022 has lots of engineering applications
especially in manufacturing sector. AISI 1022 is used by all industry sectors for applications
involving welding plus lightly stressed carburized parts. Typical applications are General
Engineering Parts and Components, Welded Structures etc. Also carburized components like
Camshafts, Light Duty Gears, Gudgeon Pins, Ratchets, Spindles, Worm Gears etc. The chemical
composition of AISI 1022 is shown in Table 1.
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3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
C
Mn
0.206% 0.70%
Table 1. Chemical composition of AISI 1022 Steel
Cr
Ni
Mo
S
P
Si
0.02% 0.01% 0.01% 0.039% 0.050% 0.19%
Al
0.028%
In the present study four parameters namely welding current, welding voltage, wire feed rate
and gas pressure were considered. A five level central composite design (CCD) was used to study
linear, quadratic and two factor interaction effect between the four process variable and one response
(Table 2). The upper limit of a factor was coded as +2 and the lower limit as -2, coded values for
intermediate levels were calculated from the following relationship:
Xi =
2[2 X − ( X max + X min )]
X max − X min
(3)
Where Xi is the required coded values of a variable X, X is any value of the variable from Xmin to
Xmax. Xmin is the lower level of the variable and Xmax is the upper level of the variable.
Sr. no
1.
2.
3.
4.
5.
Levels
-2
-1
0
1
2
Table 2. Factors and their levels
Current (A) Voltage(V) Wire feed rate(cm/min)
90
20
10.16
100
25
12.70
110
30
15.24
120
35
17.78
130
40
20.32
Gas pressure (psi)
20
30
40
50
60
4. RESULTS AND DISCUSSIONS
For the weld strength, the regression table shows the following:
Table 3. Estimated Regression Coefficients for WS
Term
Coef
SE Coef
T
P
Const
3190.69
8.405
379.601
0.000
A
52.12
4.539
11.481
0.000
V
37.05
4.539
8.163
0.000
WF
-82.49
4.539
-18.173
0.000
GP
125.97
4.539
27.751
0.000
A*A
-1.38
4.159
-0.331
0.745
V*V
-2.66
4.159
-0.639
0.532
WF*WF
-2.66
4.159
-0.639
0.532
GP*GP
6.71
4.159
1.613
0.126
A*V
-9.87
5.560
-1.776
0.095
A*WF
-2.97
5.560
-0.534
0.601
A*GP
5.14
5.560
0.924
0.369
V*WF
6.99
5.560
1.258
0.226
V*GP
2.59
5.560
0.466
0.647
WF*GP
-13.25
5.560
-2.383
0.030
R-Sq = 98.80%
R-Sq(adj) = 97.75%
S = 22.2386
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4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
From the regression table following points were observed
• Linear effects: The p-value of current, voltage, wire feed rate and gas pressure is less than
0.05. Therefore all these parameters has significant effect on weld strength.
• Squared effects: All squared effects are greater than 0.05. Therefore, there is no significant
effects of these squared values on the weld strength.
• Interaction effects: The p-values of WF*GP= 0.03 is less than 0.05. Therefore, their effect on
the model is significant.
We have construct an equation representing the relationship between the response and the factors.
WS = 3191 + 52.1 (A) + 37.1 (V) - 82.5 (WF) + 126 (GP)
(4)
For the weld strength regression equation is
Weld Strength (WS) = 3190.69+52.12(A)+37.05(V)-82.49(WF)+125.97(GP)-1.38(A)22.66(V)2- 2.66(WF)2+6.71(GP)2-9.87(A*V)-2.97(A*WF)+5.14(A*GP)+6.99(V*WF)+
2059(V*GP)-13.25(WF*GP)
(5)
The experimental designs and response Weld Strength (WS) is shown in Table 4.
Run
order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Current (A)
110
110
120
110
110
100
110
110
110
120
120
100
110
110
110
100
120
90
120
110
100
110
110
Table 4. Experimental design table
Wire feed rate
Gas pressure
Voltage (V)
(cms/min)
(psi)
30
20.32
40
30
15.24
40
35
17.78
50
30
15.24
60
30
15.24
40
25
17.78
50
40
15.24
40
30
15.24
40
30
15.24
40
25
12.7
30
25
17.78
50
25
12.7
30
30
15.24
40
30
15.24
20
30
15.24
40
35
17.78
30
35
17.78
30
30
15.24
40
35
12.7
30
20
15.24
40
35
12.7
50
30
15.24
40
30
10.16
40
40
Actual WS
(Kg)
3025.42
3180.26
3321.76
3450.42
3180.26
3100.09
3250.42
3160.26
3180.26
3150.26
3245.59
3035.76
3185.26
3000
3223.26
3000.09
3055.59
3100.76
3210.42
3125.09
3400.909
3225.26
3350.09

5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
24
25
26
27
28
29
30
31
100
100
120
100
120
130
120
100
35
25
25
35
25
30
35
25
12.7
17.78
17.78
17.78
12.7
15.24
12.7
12.7
30
30
30
50
50
40
50
50
3100
2880.92
3000.57
3225
3451.42
3285
3500.409
3310.925
The normal probability plots of the residuals versus the predicted response for weld strength
is shown in Fig. 1, respectively
Normal Probability Plot
(response is WS)
99
95
90
Percent
80
70
60
50
40
30
20
10
5
1
-40
-30
-20
-10
0
Residual
10
20
30
40
Fig.1 Normal probability plot of residuals for weld strength
Fig.1 reveals that the residuals generally fall on straight line, implying that errors are
normally distributed. This implies that the models proposed are adequate, and there is no reason to
suspect any violation of the independence or constant variance assumption.
5. CONCLUSION
This paper has investigated the effect CO2 arc welding parameters on weld strength of AISI
1022 steel plates and has used Response Surface Methodology for analysis of process parameters.
The paper effectively describes the linear, squared and interaction effects on the RSM based model.
The conclusions of this present study were drawn as follows.
• The R2 value obtained in the regression table is 98.80% which itself is the evidence that the
developed model is good enough for predicting the weld strength. Also, higher the value of
R2 the better the model fits your data.
• All the linear effects of welding parameters were found to be less than the p-value which is
0.05. Hence, the current, voltage, wire feed rate and gas pressure are significant terms in
maximizing the weld strength.
• From RSM model and experiment results, the predicted and measured values are quite close,
which indicates that the developed model can be effectively used to predict the weld strength.
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6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
K. Lalitnarayan, M.M.M. Sarcar, K. Mallikarjuna Rao and K. Kameshwaran, “Prediction of
Weld Bead Geometry for CO2 Welding process by Multiple Regression Analysis,”
INTERNATIONAL JOURNAL OF MATHEMATICS AND SCIENTIFIC COMPUTING,
VOL. 1, NO. 1, (2011), 52-57.
M.R. Nakhaei, N. B. Mostafa Arab, Gh. Naderi and M. Hoseinpour Gollo, “Experimental
study on optimization of CO2 laser welding parameters for polypropylene-clay
nanocomposite welds,” Journal of Mechanical Science and Technology 27 (3), (2013), 843848.
Ampaiboon A. and Lasunon O, “Optimization of joint strength in Gas Metal Arc Welding by
Response Surface Methodology,” AIJSTPME, (2010), 3(3), 73-77.
H.H.Na, I.S. Kim, B.Y. Kang, J.Y. Shim, “A experiment study for welding optimization of
fillet welded structure,” Journal of Achievements in Materials and Manufacturing
Engineering, Vol. 45, Issue 2, (2011), 178-187.
S. W. CAMPBELL, A. M. GALLOWAY, and N. A. McPHERSON, “Artificial Neural
Network Prediction of Weld Geometry Performed using GMAW with Alternating Shielding
Gases,” WELDING JOURNAL, VOL. 91, (2012), 174-181.
S. V. Sapakal and M. T. Telsang, “PARAMETRIC OPTIMIZATION OF MIG WELDING
USING TAGUCHI DESIGN METHOD,” International Journal of Advanced Engineering
Research and Studies, Vol. 1, Issue 4, (2012), 28-30.
Vinod Kumar, “Modelling of Weld Bead Geometry and Shape Relationships in Submerged
Arc Welding using Developed Fluxes,” Jordan Journal of Mechanical and Industrial
Engineering, Vol. 5, (2011), 461-470.
Ali Khorram, Majid Ghoreishi, Mohammad Reza Soleymani Yazdi, Mahmood Moradi,
“Optimization of Bead Geometry in CO2 Laser Welding of Ti 6Al 4V Using Response
Surface Methodology,” Scientific Research, 3, (2011), 708-712.
MINITAB 16 (2010) User’s manual, Version 16.
P.B.Wagh, R.R.Deshmukh and S.D.Deshmukh, “Process Parameters Optimization for
Surface Roughness in Edm for AISI D2 Steel by Response Surface Methodology”,
International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 1,
2013, pp. 203 - 208, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
Aniruddha Ghosh and Somnath Chattopadhyaya,, “Conical Gaussian Heat Distribution for
Submerged Arc Welding Process”, International Journal of Mechanical Engineering &
Technology (IJMET), Volume 1, Issue 1, 2010, pp. 109 - 123, ISSN Print: 0976 – 6340,
ISSN Online: 0976 – 6359.
Ravi Butola, Shanti Lal Meena and Jitendra Kumar, “Effect of Welding Parameter on Micro
Hardness of Synergic MIG Welding of 304l Austenitic Stainless Steel”, International Journal
of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 337 - 343,
ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
Aniruddha Ghosh and Somnath Chattopadhyaya,, “Submerged Arc Welding Parameters
Estimation Through Graphical Technique”, International Journal of Mechanical Engineering
& Technology (IJMET), Volume 1, Issue 1, 2010, pp. 95 - 108, ISSN Print: 0976 – 6340,
ISSN Online: 0976 – 6359.
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