 Open Access
 Total Downloads : 470
 Authors : Mr. Rahul Kanti Nath, Dr. Ajay Biswas
 Paper ID : IJERTV5IS040131
 Volume & Issue : Volume 05, Issue 04 (April 2016)
 DOI : http://dx.doi.org/10.17577/IJERTV5IS040131
 Published (First Online): 01042016
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Regression Analysis of Submerged Arc Welding Process Parameters with Respect to Different Electrode Angle as Well as Welding Direction
Mr. Rahul Kanti Nath
Post Graduate Student Department of Mechanical Engineering National Institute of Technology Agartala
Agartala, Jirania, West Tripura799046, India
Dr. Ajay Biswas
Assistant Professor Department of Mechanical Engineering National Institute of Technology Agartala
Agartala, Jirania, West Tripura799046, India
Abstract The present experiment is carried out to study the application of regression analysis to determine the optimal process parameter for submerged arc welding (SAW). The quality of welding depends on process parameter. Beadon plate weldment obtained by SAW on mild steel plate of specification as per IS2062 GradeB, SAIL steel, by varying electrode inclination viz. 900,450 as well as by changing welding direction, by keeping other set of parameters fixed. Transverse section in respect of welding direction of all the specimen are well surface finished, etched and measured the bead geometry. It is observed that bead width changes with the change of electrode inclination and welding direction causing change in heat input rate. Subsequently mathematical analysis is done and it is established that regression analysis can be done using the attribute to predict the optimal process parameter.
KeywordsResponse Surface Method (RSM), Submerged arc welding (SAW), Taguchi method.

INTRODUCTION
Submerged Arc Welding (SAW) process is generally considered as heavy welding process due to its high heat input and high metal deposition rate. The process is suitable for both butt and fillet welding. It is widely used in ship building, manufacturing of pressure vessels, railway wagon, heavy bridge members, massive water pipes etc. Various process parameters influence the bead geometry as well as weld quality. In the present experiment four parameters were varied such as Voltage (V), Travel speed(S), Wire feed rate (F), Electrode stickout distance (N) in respect of different electrode inclination angle viz. 900,450 as well as welding direction [Table 3]. Design of experiment as per Taguchis L16 orthogonal array is incorporated to restrict the number of experimental runs in each case. All the forty eight welded specimens were transversely sectioned in respect of welding direction and subsequently well surface finished, etched with natal solution i.e. 5% nitric acid solution in distilled water and measured the bead geometry. Bead width (W) [Fig.1] were measured and considered as output response. Janez Tusek, et al[1] have performed that twinarc SAW have its peculiar process parameters such as the welding current type, the size of the two arcs, location and space of the two wires. Gunaraj et al [2] have employed RMS methodology to develop mathematical model to determine and represent the cause and effect relationship between true mean responses and input
variables of SAW and to plot three dimensional surface graphs. They concluded that all responses decrease with increasing welding speed. Also, when nozzle to plate distance increase all response decrease, but reinforcement increases. Dongcheol kim et al. [3] Proposed a method to optimize the variables for an arc welding process using the genetic algorithm and the response surface methodology. In this study, systematic experiments done without the use of models to correlate the input and output variables. J.P.Ganigatti, et al. [4] gives a relationship with inputoutput of the MIG welding process by using regression analysis based on the data collected as per fullfactorial design of experiments. Kumanan et al. [5] implemented Taguchi technique to determine the main process parameters of submerged arc welding process and their influences are studied by using signalto noise ratio and analysis of variance of technique (ANOVA).The effort has also made to propose the multiple regression based mathematical model to predict the weld bead width, weld reinforcement, depth of penetration. Design of experiment approach has used to determine the main factors, viz. current, wire feed rate, travel speed and stick out, there way of affecting to weld bead parameters, influence of interaction among main parameters and finally to determine theoptimal setting for main parameter by Ghosh et al.[6]. Bead geometry optimization of SAW have been carried out by using an integrated optimization approach based on weighted principle component analysis (WPCA) and Taguchis robust design methodology by Biswas et al.[7].

MATERIAL USED
IS2062 GradeB, SAIL steel Plate Grade and Specification

Thickness:16 mm

Width: 50 mm

Length: 100 mm
The chemical and mechanical properties are listed in the below:
Table 1: Chemical Properties of the steel used
Characteristics
Value (%)
Carbon
0.22max
Manganese
1.50 max
Sulphur
.045
Phosphorous
.045
Silicon
0.40max
C.E.
0.41
Table 2: Mechanical Properties of the steel used
Characteristics
Value
Y.S(Mpa)(Min)
250
UTS (Mpa) (Min)
410
EI(%)
23
IMPACT(Min)
27 J at 0 0C
Bend
2T & 3T*
* 2T – <= 25mm
* 3T – > 25mm
Copper coated electrode specification

Diameter: 3.15 mm

Chemical composition: C0.04%, Mn0.4%, Si0.05% Flux specification

Compositions: SiO2 + TiO2= 30%, CaO + MgO= 10%, Al2O3 + MnO= 45%, CaF2= 15%

Grain Size: 0.25 – 2.00 mm
Fig.1: A schematic diagram of weld bead geometry (W: bead width).


METHODOLOGY

Experimental data
Process parameters and their range Open circuit voltage (Volt): 2954 Travel speed (m/min): 0.1 to 1.5 Wire feed rate (m/min): 0.5 to 4.0 Stickout (mm): 2531
Bead geometry for all the forty eight specimens are sequentially recorded corresponding to applied parameter setting as well as boundary conditions [Table 3].
Fig.2: A schematic representation of SAW at different condition (backhand with 450, forehand with 900, forehand with 450).

Mathematical modeling
Mathematical modeling of SAW process may be established using multiple regression analysis. The purpose of multiple regressions is to predict a single variable from one or more independent variables. Mathematical models based on welding parameters such as Voltage, Travel speed, Wire feed rate and Electrode stickout distances were obtained from regression analysis to predict Bead width. Performed SAW conditions and corresponding weld bead values are presented in Table3.

Regression analysis of weld bead characteristics (bead width) while the electrode is 900 with forehand [Table 3]. Response Surface Regression: Bead width (W) versus U, S, F, N.
Table 3: Welding condition and measured weld bead values
Specimen number
Voltage(V)
Travel speed(m/min)
Wire feed rate (knob setting poin)
Electrode stick out distance (mm)
900 with
forehand
450 with
forehand
450 with
backhand
Bead width (mm)
Bead width (mm)
Bead width (mm)
1
31
0.45
1
25
10.9910
9.6800
9.8500
2
31
0.60
2
27
11.5420
10.6810
10.8530
3
31
0.75
3
29
12.8320
14.3610
10.4210
4
31
0.90
4
31
9.0910
13.3220
9.6640
5
32.5
0.45
2
29
14.5010
16.6030
12.6470
6
32.5
0.60
1
31
7.3760
9.3210
9.6560
7
32.5
0.75
4
25
11.3430
15.1190
12.6040
8
32.5
0.90
3
27
10.4350
13.4600
10.4370
9
35
0.45
3
31
19.1400
19.9510
16.9150
10
35
0.60
4
29
15.2900
16.1620
14.2550
11
35
0.75
1
27
10.2800
8.1310
8.7210
12
35
0.90
2
25
10.5440
11.4010
9.1030
13
37
0.45
4
27
18.0710
14.3320
18.2520
14
37
0.60
3
25
18.3520
16.6900
12.8800
15
37
0.75
2
31
11.9110
14.0110
11.9110
16
37
0.90
1
29
7.3440
11.1930
8.4520
Table 4: Analysis of Variance
Regression Equation of Bead while electrode inclination 900with forehand welding direction is given below:
Source
DF
Adj SS
Adj MS
Fvalue
Pvalue
Model
13
200.606
15.4312
10.40
0.091
Linear
4
58.717
14.6792
9.90
0.094
U
1
1.326
1.3257
0.89
0.444
S
1
35.897
35.8969
24.20
0.039
F
1
6.086
6.0856
4.10
0.180
N
1
4.462
4.4625
3.01
0.225
Square
4
25.867
6.4668
4.36
0.195
U*U
1
5.524
5.5245
3.72
0.193
S*S
1
0.089
0.0887
0.06
0.830
F*F
1
13.436
13.4362
9.06
0.095
N*N
1
4.013
4.0130
2.71
0.242
2Way Interaction
5
9.335
1.8670
1.26
0.498
U*S
1
1.213
1.2127
0.82
0.461
U*F
1
2.615
2.6153
1.76
0.315
U*N
1
0.049
0.0490
0.03
0.873
S*F
1
4.951
4.9506
3.34
0.209
S*N
1
5.495
5.4952
3.71
0.194
Error
2
2.966
1.4832
Total
15
203.573
Source
DF
Adj SS
Adj MS
Fvalue
Pvalue
Model
13
200.606
15.4312
10.40
0.091
Linear
4
58.717
14.6792
9.90
0.094
U
1
1.326
1.3257
0.89
0.444
S
1
35.897
35.8969
24.20
0.039
F
1
6.086
6.0856
4.10
0.180
N
1
4.462
4.4625
3.01
0.225
Square
4
25.867
6.4668
4.36
0.195
U*U
1
5.524
5.5245
3.72
0.193
S*S
1
0.089
0.0887
0.06
0.830
F*F
1
13.436
13.4362
9.06
0.095
N*N
1
4.013
4.0130
2.71
0.242
2Way Interaction
5
9.335
1.8670
1.26
0.498
U*S
1
1.213
1.2127
0.82
0.461
U*F
1
2.615
2.6153
1.76
0.315
U*N
1
0.049
0.0490
0.03
0.873
S*F
1
4.951
4.9506
3.34
0.209
S*N
1
5.495
5.4952
3.71
0.194
Error
2
2.966
1.4832
Total
15
203.573
Bead width (W) = 663 + 69.4 U – 265 S + 43.9 F – 31.6 N – 0.996 U*U + 3.3 S*S – 3.13 F*F + 0.413 N*N – 1.67 U*S –
0.335 U*F – 0.0142 U*N – 23.3 S*F + 12.77 S*N (1)

Regression analysis of weld bead characteristics (bead width) while the electrode is 450 with forehand [Table 3].
Response Surface Regression: Bead width (W) versus U, S, F, N.
Table 7: Model Summary
S
Rsq
Rsq(adj)
Rsq(pred)
1.86634
95.39%
65.46%
0.00%
Regression Equation of Bead while electrode inclination450 with forehand welding direction is given below:
Bead width (W) = 56 – 12.7 U – 30 S + 18.2 F + 10.4 N + 0.209 U*U + 15.2 S*S – 0.62 F*F – 0.111 N*N + 2.61 U*S –
0.563 U*F – 0.036 U*N + 9.7 S*F – 4.2 S*N (2)

Regression analysis of weld bead characteristics (bead width) while the electrode is 450 with backhand [Table 3].

Table 5: Model Summary
Response Surface Regression: Bead width (W) versus U, S, F, N.
S
Rsq
Rsq(adj)
Rsq(pred)
1.21786
98.54%
89.07%
0.00%
Table 6: Coded Coefficients
Term
Effect
Coef
SE Coef
Tvalue
Pvalue
Constant
19.40
2.89
6.71
0.022
U
4.03
2.01
2.13
0.95
0.444
S
8.163
4.081
0.830
4.92
0.039
F
3.335
1.667
0.823
2.03
0.180
N
1.796
0.898
0.518
1.73
0.225
U*U
17.92
8.96
4.64
1.93
0.193
S*S
0.335
0.167
0.685
0.24
0.830
F*F
14.10
7.05
2.34
3.01
0.095
N*N
7.44
3.72
2.26
1.64
0.242
U*S
2.25
1.13
1.25
0.90
0.461
U*F
3.02
1.51
1.14
1.33
0.315
U*N
0.255
0.128
0.702
0.18
0.873
S*F
15.75
7.87
4.31
1.83
0.209
S*N
17.24
8.62
4.48
1.92
0.194
Table 8: Model Summary
S
Rsq
Rsq(adj)
Rsq(pred)
0.187603
99.94%
99.57%
94.80%
Regression Equation of Bead while electrode inclination 450 with backhand welding direction is given below:
Bead width (W) = 246.6 – 19.86 U + 143.9 S – 2.04 F + 3.36 N
+ 0.2723 U*U + 11.16 S*S + 0.368 F*F – 0.0807 N*N – 1.922
U*S – 0.0223 U*F + 0.1156 U*N + 3.09 S*F – 4.00 S*N (3)
Where,
U for Voltage
S for Travel speed F for Wire feed rate
N for Electrode stickout distance


RESULT AND DISCUSSION
It is observed from the collected data and their subsequent analysis in the present experiment that a measurable influence occurred on the weldment due to varying electrode inclination as well as by changing welding direction. Secondly, regression analyses by response surface method results are represented in figs. 35 by normal probability plots corresponding to the individual welding conditions [Table 3]. Accuracy was presented in figs. 68 and found that percentage error for responses is less than Â±5%.
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
20
18
16
14
12
10
8
6
5.0 7.5 10.0 12.5 15.0 17.5
Measured bead width (mm)
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
20
18
16
14
12
10
8
6
5.0 7.5 10.0 12.5 15.0 17.5
Measured bead width (mm)
Calculated bead width (mm)
Calculated bead width (mm)
Fig.3: Normal probability plot for forehand with 900 (response is bead width).
Fig.4: Normal probability plot for forehand with 45 0 (response is bead width).
Fig.5: Normal probability plot for backhand 450 (response is bead width).
Fig.6: Accuracy of the calculated bead width valued with respect to measured data while electrode is forehand with 900.
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
20
18
16
14
12
10
8
8 10 12 14 16 18 20
Measured bead width (mm)
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
20
18
16
14
12
10
8
8 10 12 14 16 18 20
Measured bead width (mm)
Calculated bead width (mm)
Calculated bead width (mm)
Calculated bead width (mm)
Calculated bead width (mm)
Fig.7: Accuracy of the calculated bead width valued with respect to measured data while electrode is forehand with 45 0.
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
18
16
14
12
10
8
8 10 12 14 16 18
Measured bead width (mm)
Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)
18
16
14
12
10
8
8 10 12 14 16 18
Measured bead width (mm)
Fig.8: Accuracy of the calculated bead width valued with respect to measured data while electrode is backhand 450.

CONCLUSION
It is established from the present experiment that a measurable influence can be achieved on the bead width of the weldment obtained by submerged arc welding on mild steel plate by varying electrode inclination as well as by changing welding direction. Regression analysis can be successfully applied to predict the weld responses. Using regration analysis by RSM mathematical model to establish relationship between input parameter (V, S, F, N) and weld bead geometry (equation .1 3). Computational results indicated that proposed Response Surface Method can efficiently and accurately predict the desird bead geometry by applying optimal process parameter.
REFERENCES
 [1] Janez Tusek, Marjan Suban, Highproductivity multiplewire submergedarc welding and cladding with metalpowder, Journal of Materials Processing Technology, Elsevier Science, Vol.133, 207213, 2003.
 [2] Gunaraj, V and Murugan, N, 1999, Application of Response Surface Methodology for Predicting Weld Bead Quality in Submerged Arc Welding of Pipes, Journal of Material Processing Technology, Vol. 88, 266275.
 [3] Dongcheol Kim, Sehun Rhee & Hyunsung Park (2010), Modeling and optimization of a GMA welding process by genetic algorithm and response surfae methodology, Int. J. Prod. Res., 40(7), 16991711.
 [4] J.P.Ganigatti, D.K.Pratihar, A.Roy Choudhury (2008), modeling of MIG welding process using statistical approaches, Int. j adv manuf. Technol, 35:11661190.
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 [7] Biswas A., Bhaumik S.,Majumder G., Datta S. and Mahapatra S.S., Bead geometry optimization of submerged arc weld: Exploration of weighted principle component analysis (WPCA), 2nd Int. conference on Mechanical, industrial and Manufacturing technology (MIMT2011), 26 28 Feb2011, Singapore.