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Research Article | | Peer-Reviewed

Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints

Sunny Itaofu Iremia*,  Izelu Christoper Okechukwu,  Omonigho Benedict Otanocha
Published in International Journal of Mechanical Engineering and Applications (Volume 13, Issue 1)
Received: 14 January 2025     Accepted: 27 January 2025     Published: 21 February 2025
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Abstract

The structural integrity of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries.. This research systematically investigate the effects and interactions of welding parameters such as welding current, welding voltage, gas flow rate and welding speed for enhanced structural integrity of mild-steel SKTM13A pipe butt joints. Central Composite Design (CCD) based Response Surface Methodology (RSM) was used to investigate and optimized these Tungsten Inert Gas (TIG) welding process dependent variables to minimize responses such as residual stress, distortion in weld-ment, heat flux, and maximize Peak Temperature, tensile strength of the welded joints. The results indicated model F-values of 29.81 at a P-value of <0.0001 for the tensile strength explained the significance of the employed model. Optimal tensile strength of 308.56Mpa, minimum distortion in weldment of 0.2, Peak Temperature of 1518.45°C, residual stress of 282.724Mpa and heat flux of 1500.26Kw/min were achieved at a welding current of 140A, welding voltage 24V, gas flow rate 12lit/min and welding speed of 150 cm/min. Overall, these statistics suggest that the regression model for the desired responses are robust and adequately captures the relationship with the predictor variables. In conclusion, this research has provided valuable insights into the optimization of welding parameters using Response Surface Methodology (RSM) that can be effectively apply to drive innovation and competitiveness in the welding industry.

Published in International Journal of Mechanical Engineering and Applications (Volume 13, Issue 1)
DOI 10.11648/j.ijmea.20251301.12
Page(s) 27-52
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group
Previous article
Keywords

Analysis, Optimization, Parametric, Structural Integrity, TIG, Pipe

1. Introduction
The structural integrity and performance of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries. Mild steel, known for its excellent weldability and mechanical properties, is commonly used in these applications. However, achieving optimal weld quality through Tungsten Inert Gas (TIG) welding, particularly in multi-pass weldments of mild steel pipe butt joints, presents significant challenges. These challenges are rooted in managing the thermal effects induced by the welding process, which directly impact the residual stresses and distortion of the weldment
[1]
. Optimization of welding parameters is essential for enhancing the quality and structural integrity of weldments.
The welding of mild steel, particularly through the TIG (Tungsten Inert Gas) process, is a critical operation in the manufacturing, construction, and pipeline industries due to its impact on the structural integrity and performance of the welded components. While TIG welding is favored for its ability to produce high-quality, clean welds with minimal spatter
[2]
, the process is not without its challenges. One of the primary concerns in TIG welding is the management of thermal input to prevent adverse effects such as excessive residual stresses and distortion, which can significantly compromise the mechanical properties and longevity of the weld
[3]
. This research systematically investigate the effects and interactions of welding parameters such as welding current, welding voltage, gas flow rate and welding speed for enhanced structural integrity of mild-steel SKTM13A pipe butt joints using the Central Composite Design (CCD) based Response Surface Methodology (RSM).
2. Literature Review
Despite the vast array of studies on welding processes, there remains a significant gap in research specifically addressing the optimization of TIG welding parameters for multi-pass weldments on mild steel pipe butt joints. These parameters, including welding current, voltage, and gas flow rate, are crucial for controlling the Heat Flux and, consequently mechanical properties of the weld
[4]
. Moreover, while computational models have been extensively applied to predict outcomes in welding
[5]
, there is a notable shortage of studies that validate these models against experimental data, especially in the context of multi-pass TIG welding of mild steel.
Parametric optimization using advanced algorithms has shown promise in various welding applications
[6]
, yet its application to TIG welding of mild steel requires further exploration. Tungsten Inert Gas (TIG) welding, also known as Gas Tungsten Arc Welding (GTAW), has been the subject of extensive research aimed at understanding and optimizing the process to improve weld quality and performance. The literature reveals a concerted effort to investigate various aspects of the TIG welding process, including the effects of Heat Flux, shielding gas, electrode type, and welding speed on the resultant weld properties.
Several researchers conducted detailed studies on the optimization of TIG welding parameters for stainless steel, highlighting the critical role of current and gas flow rate in determining the quality of the weld bead
[7-13]
. Their work demonstrated that precise control over these parameters could significantly enhance the mechanical properties and surface finish of the weld. Achebo and
[14-26]
focused on the application of Design of Experiments (DoE) and Response Surface Methodology (RSM) in optimizing TIG welding parameters for mild steel. Their research demonstrated the effectiveness of these statistical tools in developing predictive models and optimizing welding conditions to achieve desired weld characteristics
[27-36]
examined the thermal and mechanical behavior of TIG weldments using advanced computational modeling techniques. Their study provided insights into the heat distribution and stress development during welding, contributing to a better understanding of the factors that influence weld integrity. Gas Tungsten Arc Welding (GTAW), commonly known as Tungsten Inert Gas (TIG) welding, has been extensively studied due to its versatility and ability to produce high-quality welds in various materials.
[37]
conducted research specifically focused on GTAW, exploring its application, process parameters, and effects on weld properties. Their study delved into the intricate relationship between welding parameters such as current, voltage, and shielding gas flow rate, and the resulting weld bead profiles and mechanical properties. By systematically varying these parameters and conducting experimental analyses, Palani and Saju were able to characterize the influence of each parameter on the geometry and quality of the weld bead. Tig welding is widely used in the fabrication industry but the process dynamics such as management of thermal input and optimal parameter settings are not fully understood due to its complexity. This research contributes to filling this knowledge gap. The study also demonstrates the efficacy of experimental design methodologies, such as DoE and RSM, in systematically exploring the effects of welding parameters and optimizing the welding process for improved performance and quality. By leveraging these techniques, researchers can gain valuable insights into the complex relationships between process variables and weld properties, leading to more efficient and reliable welding processes with enhanced mechanical and metallurgical properties.
3. Materials and Methodology
The study involving the Parametric Analysis and Optimization of TIG Weldment of Mild Steel pipe butt Joint for Improved Structural Integrity, was carried out using Response Surface Methodology (RSM). The material used for Tugsten Inert Gas welding is mild steel STKM13A pipe and the G2Si1 filler electrod with the following details as shown in Table 1.
Table 1. Composition (wt. %) of Mild steel STKM13A pipe and G2Si1filler electrode.

Alloys

%

C

Cu

Fe

Mn

N

P

S

STKM13A

Max

0.15

0.4

98.293

1.1

.01

0.02

0.027

Alloys

%

C

Fe

Mn

Al

Mo

Ni

Si

Ti

G2Si1 electrode

Max

0.1

97.68

1.1

0.02

0.15

0.15

0.65

0.15
The key input parameters considered in this work are welding current, welding voltage, gas flow rate and welding speed with experimental process parameter ranges given in Table 2.
Table 2. Range and Levels of independent variables.

Parameters

Unit

Symbol

Coded value Low (-1)

Coded value High (+1)

Current

Amp

A

140

160

Voltage

Volt

V

20

24

Welding speed

cm/min

S

150

170

Gas flow rate

Lit/min

F

12

14
3.1. Response Surface Methodology (RSM) Analysis
The response or measured parameters includes; Peak Temperature, Residual Stress, Distortion in Weldment, Heat Flux and Tensile Strength. The range and level of the experimental variables were obtained from literature and are presented in Table 2.
3.2. Design of Experiments (DoE)
The objective of the Design of Experiments (DoE) is to systematically investigate the effects of key TIG welding parameters on the structural integrity of mild steel pipe butt joints. This study aims to optimize these parameters to enhance the weld quality and performance.
The central composite design (CCD) method is used to generate the experimental runs. CCD is a robust design method in RSM that includes factorial points, axial points, and center points to provide sufficient information for a quadratic model. In this study, 30 experimental runs were generated.
3.3. Sample Preparation
For the RSM analysis, mild steel pipes with a diameter of 75 mm and a thickness of 12 mm were used. As shown in Figure 1, the pipes were cut to the required length and bevelled at the edges to prepare for butt joint welding.
Figure 1. Sketch of mild steel pipe used for Butt joint welding.
The pipe surfaces and bevel edges were thoroughly cleaned to remove contaminants, and the pipes were aligned and held in place using welding fixtures to ensure precise alignment and correct gap maintenance. The TIG welding equipment shown in Figure 5 was calibrated according to the experimental parameters, and welding was performed with consistent technique and specified shielding gas flow. The welding process uses a shielding gas to protect the weld specimen from atmospheric interaction. For this study, 100% pure Argon gas was used. The weld samples were made from 12mm thickness of mild steel pipe; the pipe was cut to size with the power hacksaw.
The edges grinded and surfaces polished with emery paper and the joints welded. The samples preparation and welding were done at the Metallurgical Training Institute (MTI) 4Q2X+HGW, Owerri Rd. Layout, Obosi, Anambra State.
Figures 1, 2 and 3 shows sample preparation, finished coupons and welded joint respectively.
Figure 2. Sample preparation.
Figure 3. Finished coupon.
Figure 4. Welded joint.
Figure 5. TIG/MMA-250S Welding Machine and other Equipment.
Peak Temperature as well as heat flux across the weldment was measured using infrared thermometer and infrared thermography device shown in Figures 6 and 7. Multiple temperature readings along the weld provided comprehensive thermal profiles essential for understanding heat distribution. After welding, the joints were allowed to cool at a controlled rate, labelled with unique identifiers, and inspected for defects. These prepared samples were then used for further testing to measure responses such as residual stress, distortion, and tensile strength at Ahmadu Bello University Engineering and metallurgical Laboratory, Zaria ensuring reliable data for the RSM analysis.
Stress values were recorded at critical points to capture variations induced by welding parameters using the X-ray diffraction machine shown in Figure 8. The distortion in welded joints was quantified using precision measurement tools such as coordinate measuring machines (CMM) in Figure 9.
Tensile test specimens were prepared according to standardized procedures and subjected to tensile testing using universal testing machines (UTM) presented in Figure 3.
Figure 6. Fluke62 Max infrared thermometer.
Figure 7. FLIR System-GF304 thermography.
Figure 8. AutoMATE II X-ray diffraction device.
Figure 9. Coordinate Measuring machine (CMM).
Figure 10. M0565SHIMADZU UNIVERSAL TESTING MACHINE (UTM).
Repeat measurements and calibration checks were performed to validate the consistency of results. The collected data provided essential insights into the relationship between welding parameters and weld quality, enabling informed decision-making for process optimization and structural integrity enhancement. Table 3 shows the CCD experimental result obtained from the lab.
4. Experimental Results and Discussions
Table 3. CCD Experimental results.

Std

Run

Factor 1

Factor 2

Factor 3

Factor 4

Response 1

Response 2

Response 3

Response 4

Response 5

A: Current

B: Voltage

C: Weld Speed

D: Gas Flow Rate

Temperature Distribution

Residual Stress

Distortion in Weldment

Heat Flux

Tensile Strength

A

V

cm/min

Lit/min

°C

MPa

mm

KW/m2

MPa

15

1

140

24

170

14

1520.04

284.147

0.21

674.06

287.19

2

2

160

20

150

12

1519.66

266.105

0.22

873.61

271.96

16

3

160

24

170

14

1519.86

258.797

0.22

769.4

274.37

26

4

150

22

160

13

1519.88

259.18

0.22

760.34

281.32

5

5

140

24

150

12

1518.45

282.724

0.2

1500.26

308.56

21

6

150

18

160

13

1519.8

243.024

0.25

800.8

293.371

23

7

150

22

140

13

1519.87

259.431

0.22

760.94

266.998

8

8

160

24

150

14

1519.86

259.79

0.22

769.4

277.705

22

9

150

26

160

13

1520.19

279.801

0.25

596.76

307.593

11

10

140

20

170

14

1519.64

255.67

0.22

882.22

286.857

13

11

140

24

170

12

1518.3

292.48

0.19

1577.54

304.463

20

12

150

22

160

15

1517.14

258.65

0.19

2179.4

294.781

10

13

160

20

170

12

1519.66

257.1

0.22

873.61

281.838

18

14

170

22

160

13

1519.7

253.52

0.22

853.87

269.128

17

15

130

22

160

13

1520.06

286.11

0.2

664.59

290.016

28

16

150

22

160

13

1519.23

259.18

0.22

1093.67

281.32

27

17

150

22

160

13

1519.23

259.18

0.22

1093.67

281.32

24

18

150

22

180

13

1519.8

259.18

0.22

800.53

281.32

19

19

150

22

160

11

1518.27

287.97

0.17

1593.36

305.61

4

20

160

20

150

14

1517.53

249.37

0.23

1976.99

268.224

9

21

140

20

170

12

1519.85

270.1

0.21

774.25

289.625

1

22

140

20

150

12

1519.85

270.1

0.21

774.25

284.86

12

23

160

20

170

14

1518.4

234.66

0.21

1524.4

293.84

30

24

150

22

160

13

1519.88

259.18

0.22

760.34

281.32

29

25

150

22

160

13

1519.88

259.18

0.22

760.34

281.32

7

26

140

24

150

14

1520.04

272.41

0.21

674.06

287.19

6

27

160

24

150

12

1520.07

272.55

0.21

659.65

287.91

25

28

150

22

160

13

1519.88

259.18

0.22

760.34

281.32

14

29

160

24

170

12

1520.07

272.55

0.21

659.65

287.91

3

30

140

20

150

14

1519.64

255.67

0.22

882.22

270.13
The Response Surface Methodology (RSM) study was built using version 13.0.5.0 and employed a randomized central composite design to develop a quadratic model. The study consisted of 30 runs without any blocks, indicating a single experimental setup. This setup facilitated robust predictions and optimizations of welding process responses, contributing to the overall analysis presented in Table 4.
Table 4. Model Summary Statistics for individual responses.

Responses

Source

Std. Dev.

R²

Adjusted R²

Predicted R²

PRESS

Peak Temperature

Linear

0.8340

0.0519

-0.0998

-0.4598

26.77

2FI

0.6767

0.5256

0.2759

0.2108

14.47

Quadratic

0.2723

0.9394

0.8828

0.7805

4.02

Suggested

Cubic

0.3045

0.9646

0.8534

0.1838

14.97

Aliased

Residual Stress

Linear

5.83

0.8378

0.8119

0.7630

1240.67

2FI

5.15

0.9039

0.8533

0.8361

857.86

Quadratic

1.09

0.9966

0.9934

0.9803

103.24

Suggested

Cubic

0.2655

0.9999

0.9996

0.9864

71.05

Aliased

Distortion in weldment

Linear

0.0151

0.1799

0.0486

-0.2708

0.0088

2FI

0.0170

0.2068

-0.2106

-0.3883

0.0096

Quadratic

0.0047

0.9532

0.9096

0.7306

0.0019

Suggested

Cubic

0.0019

0.9964

0.9851

0.4820

0.0036

Aliased

Heat flux

Linear

432.55

0.0519

-0.0998

-0.4598

7.202E+06

2FI

350.97

0.5256

0.2759

0.2108

3.894E+06

Quadratic

141.21

0.9394

0.8828

0.7805

1.083E+06

Suggested

Cubic

157.95

0.9646

0.8534

0.1838

4.027E+06

Aliased

Tensile Strength

Linear

9.24

0.4247

0.3326

0.1138

3284.61

2FI

8.61

0.6200

0.4200

0.3331

2471.51

Quadratic

3.24

0.9575

0.9179

0.7554

906.53

Suggested

Cubic

3.18

0.9809

0.9208

-1.7539

10206.47

Aliased
Table 5. Fit Statistics for Individual Responses.

Responses

Std. Dev.

Mean

C.V. %

R²

Adjusted R²

Predicted R²

Adeq Precision

Peak Temperature

0.2723

1519.46

0.0179

0.9394

0.8828

0.7805

14.6631

Residual Stress

1.09

264.57

0.4132

0.9966

0.9934

0.9803

75.7186

Distortion in Weldment

0.0047

0.2150

2.16

0.9532

0.9096

0.7306

25.8249

Heat Flux

141.21

977.48

14.45

0.9394

0.8828

0.7805

14.6630

Tensile Strength

3.24

285.31

1.14

0.9575

0.9179

0.7554

20.0008
Table 5 presents fit statistics for the response variables such as Peak Temperature, Residual stress, Distortion in weldment, Heat flux and Tensile strength. The "Std. Dev." column indicates the standard deviation of the residuals, which measures the dispersion of data points around the regression line. A lower standard deviation suggests less variability and better fit of the model to the data. The "R²" value represents the coefficient of determination, indicating the proportion of variance in the responses explained by the regression model. For example, Peak Temperature with an R² of 0.9394 suggests that approximately 93.94% of the variance in Peak Temperature is accounted for by the regression model. The "Adjusted R²" adjusts R² for the number of predictors in the model, offering a more accurate assessment of model fit. In this case, the Adjusted R² of 0.8828 implies that the model retains its explanatory power even after considering the number of predictors. The "Predicted R²" provides an estimate of the model's predictive capability on new data, with a value of 0.7805 indicating a reasonably good prediction performance. Lastly, the "Adeq Precision" indicates the signal-to-noise ratio, suggesting that the model is adequate for making predictions, with a value of 14.6631. Overall, these statistics suggest that the regression model for Peak Temperature is robust and adequately captures the relationship with the predictor variables.
4.1. Individual Response ANOVA for Quadratic Modeling
Tables 6 to 10 present ANOVA for the different responses, each row corresponds to a different factor or interaction, including individual factors (A, B, C, D), their interactions (AB, AC, AD, BC, BD, CD), and the quadratic terms (A², B², C², D²), along with residual terms. The table shows the sum of squares, degrees of freedom, mean square, F-value, and p-value for each factor. The p-values indicate the significance of each factor in explaining the variability in the response. Factors with p-values less than the significance level (usually 0.05) are considered significant. In this case, the quadratic terms and the interactions which have significant p-values, suggest that they significantly contribute to the variability in the response variables. Conversely, factors with non-significant p-values are considered not significant. The tables show that the model for each of the responses is significant with p-value less than 0.005 with lack of fit not significant.
Table 6. ANOVA for Peak Temperature.

Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

17.23

14

1.23

16.60

< 0.0001

significant

A-Current

0.0863

1

0.0863

1.16

0.2976

B-Voltage

0.4401

1

0.4401

5.94

0.0278

C-Weld Speed

0.0136

1

0.0136

0.1833

0.6747

D-Gas Flow Rate

0.4125

1

0.4125

5.57

0.0323

AB

2.85

1

2.85

38.48

< 0.0001

AC

0.0652

1

0.0652

0.8801

0.3630

AD

2.83

1

2.83

38.12

< 0.0001

BC

0.0652

1

0.0652

0.8801

0.3630

BD

2.81

1

2.81

37.97

< 0.0001

CD

0.0652

1

0.0652

0.8801

0.3630

A²

0.1240

1

0.1240

1.67

0.2154

B²

0.2548

1

0.2548

3.44

0.0835

C²

0.0887

1

0.0887

1.20

0.2911

D²

6.22

1

6.22

83.86

< 0.0001

Residual

1.11

15

0.0741

Lack of Fit

0.5611

10

0.0561

0.5094

0.8297

not significant

Pure Error

0.5507

5

0.1101

Cor Total

18.34

29
Table 7. ANOVA for Residual Stress.

Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

5216.91

14

372.64

311.85

< 0.0001

significant

A-Current

1313.62

1

1313.62

1099.35

< 0.0001

B-Voltage

1230.30

1

1230.30

1029.62

< 0.0001

C-Weld Speed

1841.46

1

1841.46

1541.10

< 0.0001

D-Gas Flow Rate

0.5757

1

0.5757

0.4818

0.4982

AB

20.66

1

20.66

17.29

0.0008

AC

35.31

1

35.31

29.55

< 0.0001

AD

133.41

1

133.41

111.65

< 0.0001

BC

32.71

1

32.71

27.37

0.0001

BD

1.39

1

1.39

1.16

0.2977

CD

122.19

1

122.19

102.26

< 0.0001

A²

192.15

1

192.15

160.81

< 0.0001

B²

339.96

1

339.96

284.50

< 0.0001

C²

8.18

1

8.18

6.85

0.0194

D²

0.0103

1

0.0103

0.0086

0.9272

Residual

17.92

15

1.19

Lack of Fit

17.92

10

1.79

0.5094

0.8297

not significant

Pure Error

0.0000

5

0.0000

Cor Total

5234.83

29
Table 8. ANOVA for Distortion in Weldment.

Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

0.0066

14

0.0005

21.84

< 0.0001

significant

A-Current

0.0005

1

0.0005

23.27

0.0002

B-Voltage

0.0005

1

0.0005

23.27

0.0002

C-Weld Speed

0.0002

1

0.0002

9.42

0.0078

D-Gas Flow Rate

0.0000

1

0.0000

1.73

0.2081

AB

0.0001

1

0.0001

2.60

0.1280

AC

0.0001

1

0.0001

2.60

0.1280

AD

6.250E-06

1

6.250E-06

0.2885

0.5991

BC

0.0001

1

0.0001

2.60

0.1280

BD

6.250E-06

1

6.250E-06

0.2885

0.5991

CD

6.250E-06

1

6.250E-06

0.2885

0.5991

A²

0.0002

1

0.0002

10.01

0.0064

B²

0.0029

1

0.0029

134.63

< 0.0001

C²

0.0014

1

0.0014

65.40

< 0.0001

D²

2.679E-06

1

2.679E-06

0.1236

0.7300

Residual

0.0003

15

0.0000

Lack of Fit

0.0003

10

0.0000

0.5094

0.8297

not significant

Pure Error

0.0000

5

0.0000

Cor Total

0.0069

29
Table 9. ANOVA for Heat Flux.

Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

4.635E+06

14

3.310E+05

16.60

< 0.0001

significant

A-Current

23213.66

1

23213.66

1.16

0.2976

B-Voltage

1.110E+05

1

1.110E+05

5.57

0.0323

C-Weld Speed

1.184E+05

1

1.184E+05

5.94

0.0278

D-Gas Flow Rate

3653.87

1

3653.87

0.1833

0.6747

AB

7.601E+05

1

7.601E+05

38.12

< 0.0001

AC

7.672E+05

1

7.672E+05

38.47

< 0.0001

AD

17547.64

1

17547.64

0.8801

0.3630

BC

7.570E+05

1

7.570E+05

37.97

< 0.0001

BD

17547.64

1

17547.64

0.8801

0.3630

CD

17547.64

1

17547.64

0.8801

0.3630

A²

33365.15

1

33365.15

1.67

0.2154

B²

1.672E+06

1

1.672E+06

83.86

< 0.0001

C²

68543.91

1

68543.91

3.44

0.0835

D²

23871.68

1

23871.68

1.20

0.2911

Residual

2.991E+05

15

19939.18

Lack of Fit

1.509E+05

10

15093.95

0.5094

0.8297

not significant

Pure Error

1.481E+05

5

29629.63

Cor Total

4.934E+06

29
Table 10. ANOVA for Tensile Strength.

Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

3548.83

14

253.49

24.16

< 0.0001

significant

A-Current

569.34

1

569.34

54.26

< 0.0001

B-Voltage

362.53

1

362.53

34.55

< 0.0001

C-Weld Speed

387.28

1

387.28

36.91

< 0.0001

D-Gas Flow Rate

254.79

1

254.79

24.28

0.0002

AB

103.34

1

103.34

9.85

0.0068

AC

120.44

1

120.44

11.48

0.0041

AD

13.62

1

13.62

1.30

0.2724

BC

176.59

1

176.59

16.83

0.0009

BD

50.63

1

50.63

4.83

0.0442

CD

259.35

1

259.35

24.72

0.0002

A²

27.98

1

27.98

2.67

0.1233

B²

471.47

1

471.47

44.93

< 0.0001

C²

487.90

1

487.90

46.50

< 0.0001

D²

153.18

1

153.18

14.60

0.0017

Residual

157.38

15

10.49

Lack of Fit

157.38

10

15.74

0.5094

0.8297

not significant

Pure Error

0.0000

5

0.0000

Cor Total

3706.22

29
4.2. Model Equation Derived from RSM Analysis Using Design Expert 13
Table 11 present the model equations for the responses and describe how they are influenced by the linear terms current A, voltage B, weld speed C, and gas flow rate D, including their interactions and quadratic terms. The base Peak Temperature is 1546.34758 units. An increase in current slightly reduces the temperature, while an increase in voltage significantly raises it. Weld speed has a moderate negative effect, and gas flow rate has a negligible negative effect. Interaction terms like AB, AC, and BC show how combined changes in these factors impact the Peak Temperature, with AB and CD having a minor influence. The quadratic term B2 has a substantial negative effect, indicating that higher voltage levels reduce Peak Temperature significantly. The other quadratic terms have minimal impact. The base level of residual stress is 1100.68047 units. The residual stress decreases with increasing current, voltage, and weld speed, as shown by the negative coefficients for these factors -0.948666, -92.65930, and -21.88771 respectively, while gas flow rate has a minor positive effect 1.59734. Interaction terms like AB -0.113633, AC -0.074277, and others indicate how combinations of these factors further influence residual stress, albeit slightly. Additionally, quadratic terms reveal nonlinear influences: for instance, B2 3.52054 significantly increases residual stress, whereas A2 0.026468 and C2 0.136542 have smaller positive effects. The model equation for distortion in weldment reveals a complex relationship between the variables and Distortion, with both positive and negative effects. The linear terms (0.010271A, 0.290208B, -0.111771C, 0.001937D) indicate the individual contributions of each variable, while the interaction terms (0.000187AB, 0.000094AC, -6.25000E-06AD, 0.000938BC, -0.000062BD, 0.000031CD) show how the variables interact to influence Distortion. The quadratic terms (-0.000028A², -0.010312B², 0.001797C², -3.12500E-06D²) indicate non-linear relationships. Heat Flux has a base level of significantly negative value at -12969.61316 units, which adjusts with the contributions from the other factors. Current A and gas flow rate D have positive effects on Heat Flux with coefficients of 118.24231 and 149.46870 respectively, whereas voltage B and weld speed C show negative effects with coefficients of -6698.53799 and -3305.96649 respectively. The interaction terms such as AB (21.79631) and AC (-10.94841) show how combinations of these factors further influence the Heat Flux. Quadratic terms reveal non-linear effects, with B2 (246.91003) significantly increasing the Heat Flux, while other quadratic terms like A2 (-0.348775) and C2 (-12.49749) have negative influences. The base tensile strength is 138.01713 units, adjusted by the contributions of the factors. Current A and gas flow rate D have positive effects on tensile strength, with coefficients of 0.780513 and 8.62017 respectively. In contrast, voltage B has a significant negative impact with a coefficient of -141.71908, while weld speed C positively affects tensile strength with a coefficient of 29.99611. Interaction terms such as AB (0.254139) and BC (-1.66111) indicate how combinations of these factors further influence tensile strength. Quadratic terms like B2 (4.14596) and C2 (1.05439) reveal the non-linear effects, suggesting that increases in voltage and weld speed lead to significant changes in tensile strength. Overall, this equation provides a detailed model of how various welding parameters and their interactions impact the tensile strength of the weldment.
In all, these equations allow for precise adjustments of welding parameters to control the different responses effectively.
Table 11. Final Equation in Terms of Actual Factors.

Actual Factors

Coefficients of actual factors in the responses

Peak Temperature=

Residual stress=

Distrtion in weldment =

Heat flux =

Tensile strength =

+1546.34758

+1100.68047

-1.40031

-12969.61316

+138.01713

A-Current

-0.227975

-0.948666

+0.010271

+118.24231

+0.780513

B-Voltage

+12.91510

-92.65930

+0.290208

-6698.53799

-141.71908

C-Welding speed

-6.37405

-21.88771

-0.111771

+3305.96649

+29.99611

D-Gas flow rate

-0.28818

+1.59734

+0.001937

+149.46870

+8.62017

AB

-0.042024

-0.113633

-0.000187

+21.79631

+0.254139

AC

+0.021109

-0.074277

+0.000094

-10.94841

-0.137184

AD

+0.000639

-0.028876

-6.25000E-06

-0.331169

+0.009228

BC

+0.209692

+0.714883

+0.000938

-108.75906

-1.66111

BD

+0.209692

-0.029482

-0.000062

-3.31169

+0.177882

CD

-0.003193

+0.138173

+0.000031

+1.65584

-0.201306

A2

+0.000672

+0.026468

-0.000028

-0.348775

-0.010099

B2

-0.476054

+3.52054

-0.010312

+246.91003

+4.14596

C2

+0.024096

+0.136542

-0.010312

-12.49749

+1.05439

D2

+0.000569

+0.000194

-3.12500E-06

-0.295012

-0.023632
4.3. Predicted vs Actual Plots for the Responses
Figure 11. Peak Temperature.
Figure 12. Residual stress.
Figure 13. Distortion in weldment.
Figure 14. Heat flux.
Figure 15. Tensile strength.
Figures 11-15, illustrate the relationship between the predicted values of the responses, Peak Temperature, residual stress, distortion in weldment, heat flux and tensile strength obtained from the regression model and the actual measured values. This plot is essential for evaluating the accuracy and reliability of the predictive model for each of the responses. Ideally, the points should cluster closely around the diagonal line, indicating a strong agreement between the predicted and actual values. Deviations from this line suggest discrepancies between the predicted and observed values, which may indicate areas where the model requires refinement or where other unaccounted-for factors may be influencing the each of the responses. Analyzing this plot helps assess the model's performance and identify areas for improvement in predicting each of the responses accurately.
4.4. Perturbation Plots for the Response Variables
The perturbation plot shows the comparative effects of the independent variables at a particular point in the design space as shown in Figures 16 to 20. A significant steep slope or curvature of factors A, B, and D for instance in Figure 16, show that the Peak Temperature is mostly influenced by the gas flow rate, voltage and the current, The sensitivity of the independent variable to the response variables is explained by the steepness or higher slope of the factors in the perturbation plot.
Figure 16. Perturbation plot for Peak Temperature.
Figure 17. Perturbation plot for residual stress.
Figure 18. Perturbation plot for distortion in weldment.
Figure 19. Perturbation plot for heat flux.
Figure 20. Perturbation plot for tensile strength.
Figure 21. Contour plot for Peak Temperature.
Figure 22. 3D surface plot for Peak Temperature.
Figure 21 depicts a contour plot illustrating the Peak Temperature across a welding process. Contour plots like this one are essential for understanding the spatial distribution of temperature within the welded component or structure. In this specific plot, different colors represent different temperature levels, with warmer colors indicating higher temperatures and cooler colors indicating lower temperatures. By analyzing this plot, engineers and researchers can identify regions of interest where temperature may be excessively high or low, allowing for adjustments to welding parameters or the design of cooling strategies to ensure uniform Peak Temperature and prevent issues like overheating or insufficient heat penetration, which could compromise the integrity of the weld. Figure 22 presents a 3D surface plot depicting the Peak Temperature within a welded structure. The plot provides a visual representation of how temperature varies across the surface, offering insights into the heat-affected zone and thermal gradients during the welding process. Understanding Peak Temperature is crucial for controlling metallurgical transformations and mechanical properties, as excessive heat can lead to undesirable outcomes such as distortion, residual stresses, and reduced mechanical strength. Engineers can use this plot to optimize welding parameters, ensuring uniform Peak Temperature for desired material propertiess and structural integrity in welded components.
Figure 23, presents a contour plot demonstrating the distribution of residual stress within a welded component or structure. Residual stress, which remains in a material after the removal of external forces, can significantly affect the performance and longevity of welded structures. In this contour plot, different colors represent varying levels of residual stress, with warmer colors indicating higher stress levels and cooler colors indicating lower stress levels. Analyzing this plot enables engineers and researchers to identify regions of the weld where residual stress concentrations are particularly high, which may lead to issues such as cracking, distortion, or reduced fatigue life. By understanding and managing residual stress through appropriate welding techniques or post-weld heat treatments, engineers can enhance the structural integrity and performance of welded components. Figure 24 illustrates a 3D surface plot representing the distribution of residual stress within a welded structure. Residual stresses arise from thermal gradients and material shrinkage during the welding process, impacting the structural integrity and performance of welded components. This plot provides a visual depiction of how residual stresses vary across the surface, indicating regions of high and low stress concentrations. By analyzing this distribution, engineers can optimize welding procedures to minimize residual stresses, thereby reducing the risk of deformation, cracking, and premature failure in welded structures. Additionally, understanding the residual stress distribution enables the design of effective post-weld heat treatment or stress-relieving techniques to mitigate potential issues and enhance the overall performance of welded assemblies.
Figure 23. Contour plot for Residual Stress.
Figure 24. 3D surface plot for Residual Stress.
Figure 25 illustrates a contour plot revealing the distortion distribution in a weldment, offering insights into the spatial variation of distortion levels across the welded structure. Distortion, caused by thermal expansion and contraction during the welding process, can adversely affect the dimensional accuracy and mechanical properties of welded components. The contour plot displays different distortion levels using varying colors or contour lines, with regions of higher distortion represented by warmer colors and regions of lower distortion by cooler colors. Understanding the distortion pattern aids engineers in optimizing welding parameters and implementing corrective measures to mitigate distortion, ensuring the dimensional accura cy and structural integrity of welded assemblies. Figure 26 presents a 3D surface plot depicting the distortion in a welded structure. Welding induces thermal gradients and material shrinkage, resulting in deformation, which can compromise the structural integrity and dimensional accuracy of the welded components. This plot visualizes how distortion varies across the surface of the welded structure, highlighting areas where significant deformation occurs. By analyzing this distribution, engineers can identify regions prone to distortion and develop strategies to minimize it during the welding process. Implementing corrective measures such as fixture design, welding sequence optimization, or pre-welding component manipulation can help mitigate distortion, ensuring that the final product meets dimensional specifications and performance requirements.
Figure 25. Contour plot for Distortion in Weldment.
Figure 26. 3D plot for Distortion in Weldment.
Figure 27. Contour plot for Heat Flux.
Figure 28. 3D surface plot for Heat Flux.
Figure 27 presents a contour plot illustrating the distribution of Heat Flux during welding processes. Heat Flux plays a critical role in determining the metallurgical characteristics and mechanical properties of welded joints. The contour plot visualizes the spatial variation of Heat Flux across the welded structure, with different regions experiencing varying levels of Heat Flux represented by distinct colors or contour lines. Understanding the Heat Flux distribution enables engineers to optimize welding parameters, such as voltage, current, and travel speed, to achieve desired weld bead geometry, minimize distortion, and ensure adequate penetration and fusion between the base materials. Additionally, controlling Heat Flux helps in managing microstructural changes, such as grain growth and phase transformations, which influence the mechanical performance and integrity of the welded components. Figure 28 illustrates a 3D surface plot representing Heat Flux distribution during a welding process. Heat Flux plays a crucial role in welding as it influences the material's microstructure, mechanical properties, and overall weld quality. This plot provides a visual representation of how heat is distributed across the welded joint, with different colors indicating varying levels of Heat Flux. Analyzing this distribution helps welders and engineers understand the thermal characteristics of the welding process and optimize parameters such as welding current, voltage, and travel speed to achieve desired weld properties.
By controlling Heat Flux, they can minimize issueslike distortion, residual stresses, and metallurgical defects, ensuring high-quality welds and reliable performance of welded components.
Figure 29 depicts a contour plot showcasing the distribution of tensile strength across a welded structure. Tensile strength is a crucial mechanical property indicating the material's resistance to breaking under tension. This contour plot visualizes how tensile strength varies spatially within the weldment, providing insights into the effectiveness of the welding process and the quality of the resulting joints. Understanding the distribution of tensile strength helps engineers identify areas with potential weaknesses or defects, allowing for targeted improvements in welding parameters or post-weld heat treatments to enhance the overall mechanical integrity of the welded components. By optimizing tensile strength distribution, engineers can ensure that welded structures meet performance requirements and exhibit reliable performance under applied loads. Figure 30 presents a 3D surface plot illustrating the distribution of tensile strength across a welded joint. Tensile strength is a critical mechanical property that indicates the material's ability to resist deformation or fracture under tension. This plot visually represents how tensile strength varies across the welded region, providing insights into the weld's structural integrity and load-bearing capacity. By examining this distribution, engineers can identify areas of potential weakness or variability in tensile strength, allowing them to optimize welding parameters and post-weld treatments to achieve uniform mechanical properties throughout the welded structure. This understanding is essential for ensuring the reliability and performance of welded components in real-world applications, particularly in industries where structural integrity is paramount, such as aerospace, automotive, and construction Figure 31 illustrates the RSM (Response Surface Methodology) Ramps Optimization Diagram, which provides a visual representation of the optimal settings for various welding process parameters. The diagram shows how adjustments in parameters such as current, voltage, weld speed, and gas flow rate impact the response variables: Peak Temperature, residual stress, distortion in weldment, Heat Flux, and tensile strength. Each ramp on the diagram corresponds to a specific parameter and indicates the optimal levels that achieve the desired response. The intersection points on the ramps represent the exact values for each parameter to meet the optimization goals, allowing for a clear and comprehensive understanding of how each parameter influences the overall welding performance.
Figure 29. Contour plot for Tensile strength.
Figure 30. 3D surface plot for Tensile strength.
Figure 31. RSM Ramps Optimization diagram.
Figure 32 presents a bar graph generated through Response Surface Methodology (RSM) for optimization purposes. This graph visually depicts the relationship between different optimization objectives and their corresponding desirability values. Each bar represents a specific optimization goal, such as maximizing tensile strength or minimizing residual stress, and the height of the bar indicates the desirability score achieved for that particular objective. The graph provides a straightforward comparison of the desirability levels for each optimization goal, enabling a quick assessment of the overall effectiveness of the optimization process and helping identify which objectives have been successfully met or require further refinement.
Figure 32. RSM Bar graph for Optimization.
5. Conclusion
RSM helps in understanding the effect of each parameter and their interactions on the responses. The fitted model was used to predict the optimal set of parameters that minimize or maximize the desired responses.
The results indicated model F-values of 29.81 at a P-value of <0.0001 for the tensile strength explained the significance of the employed model. Optimal tensile strength of 308.56Mpa, minimum distortion in weldment of 0.2, Peak Temperature of 1518.45oC, residual stress of 282.724Mpa and heat flux of 1500.26Kw/min were achieved with overall desirability 0f 62.51% at a welding current of 140A, welding voltage 24V, gas flow rate 12lit/min and welding speed of 150 cm/min.
Overall, these statistics suggest that the regression model for the desired responses are robust and adequately captures the relationship with the predictor variables. In this case, the quadratic terms and the interactions which have significant p-values, suggest that they significantly contribute to the variability in the response variables.
In conclusion, this research has provided valuable insights into the optimization of welding parameters using Response Surface Methodology (RSM) that can be effectively apply to drive innovation and competitiveness in the welding industry.
Acknowledgments
I sincerely wish to express my profound gratitude first of all to the Almighty GOD for the strength given me to carry out this researched work. Secondly my appreciation goes to my Supervisor Engr. Prof. Christopher Izelu for his distinguished supervision and review of my work, constantly inspiring and motivating me on this research. I also wish to thank my second supervisor and reviewer Dr. Benedict Omonigho Otanocha.
Author Contributions
Sunny Itaofu Iremia: Conceptualization, Formal Analysis, Investigation, Methodology, Project administration, Resources, Validation, Writing – original draft, Writing – review & editing
Izelu Christopher Okhechukwu: Supervision, Writing – review & editing
Omonigho Benedict Otanocha: Supervision
Conflicts of Interest
There is no conflict of interest. This is part of my researched thesis for my educational fulfilment.
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    Iremia, S. I., Okechukwu, I. C., Otanocha, O. B. (2025). Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints. International Journal of Mechanical Engineering and Applications, 13(1), 27-52. https://doi.org/10.11648/j.ijmea.20251301.12

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    Iremia, S. I.; Okechukwu, I. C.; Otanocha, O. B. Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints. Int. J. Mech. Eng. Appl. 2025, 13(1), 27-52. doi: 10.11648/j.ijmea.20251301.12

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    Iremia SI, Okechukwu IC, Otanocha OB. Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints. Int J Mech Eng Appl. 2025;13(1):27-52. doi: 10.11648/j.ijmea.20251301.12

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  • @article{10.11648/j.ijmea.20251301.12,
  author = {Sunny Itaofu Iremia and Izelu Christoper Okechukwu and Omonigho Benedict Otanocha},
  title = {Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints
},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {13},
  number = {1},
  pages = {27-52},
  doi = {10.11648/j.ijmea.20251301.12},
  url = {https://doi.org/10.11648/j.ijmea.20251301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20251301.12},
  abstract = {The structural integrity of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries.. This research systematically investigate the effects and interactions of welding parameters such as welding current, welding voltage, gas flow rate and welding speed for enhanced structural integrity of mild-steel SKTM13A pipe butt joints. Central Composite Design (CCD) based Response Surface Methodology (RSM) was used to investigate and optimized these Tungsten Inert Gas (TIG) welding process dependent variables to minimize responses such as residual stress, distortion in weld-ment, heat flux, and maximize Peak Temperature, tensile strength of the welded joints. The results indicated model F-values of 29.81 at a P-value of <0.0001 for the tensile strength explained the significance of the employed model. Optimal tensile strength of 308.56Mpa, minimum distortion in weldment of 0.2, Peak Temperature of 1518.45°C, residual stress of 282.724Mpa and heat flux of 1500.26Kw/min were achieved at a welding current of 140A, welding voltage 24V, gas flow rate 12lit/min and welding speed of 150 cm/min. Overall, these statistics suggest that the regression model for the desired responses are robust and adequately captures the relationship with the predictor variables. In conclusion, this research has provided valuable insights into the optimization of welding parameters using Response Surface Methodology (RSM) that can be effectively apply to drive innovation and competitiveness in the welding industry.
},
 year = {2025}
}

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  • TY  - JOUR
T1  - Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints

AU  - Sunny Itaofu Iremia
AU  - Izelu Christoper Okechukwu
AU  - Omonigho Benedict Otanocha
Y1  - 2025/02/21
PY  - 2025
N1  - https://doi.org/10.11648/j.ijmea.20251301.12
DO  - 10.11648/j.ijmea.20251301.12
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 27
EP  - 52
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20251301.12
AB  - The structural integrity of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries.. This research systematically investigate the effects and interactions of welding parameters such as welding current, welding voltage, gas flow rate and welding speed for enhanced structural integrity of mild-steel SKTM13A pipe butt joints. Central Composite Design (CCD) based Response Surface Methodology (RSM) was used to investigate and optimized these Tungsten Inert Gas (TIG) welding process dependent variables to minimize responses such as residual stress, distortion in weld-ment, heat flux, and maximize Peak Temperature, tensile strength of the welded joints. The results indicated model F-values of 29.81 at a P-value of <0.0001 for the tensile strength explained the significance of the employed model. Optimal tensile strength of 308.56Mpa, minimum distortion in weldment of 0.2, Peak Temperature of 1518.45°C, residual stress of 282.724Mpa and heat flux of 1500.26Kw/min were achieved at a welding current of 140A, welding voltage 24V, gas flow rate 12lit/min and welding speed of 150 cm/min. Overall, these statistics suggest that the regression model for the desired responses are robust and adequately captures the relationship with the predictor variables. In conclusion, this research has provided valuable insights into the optimization of welding parameters using Response Surface Methodology (RSM) that can be effectively apply to drive innovation and competitiveness in the welding industry.

VL  - 13
IS  - 1
ER  -

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