The document summarizes the results of phase 1 submerged arc welding experiments conducted by Dr. Bikram Jit Singh. 60 experimental runs were conducted with varying levels of slag flux (0%, 20%, 40%, etc.). The mechanical properties tested include tensile strength, impact strength, hardness, and elongation. Statistical analysis using one-way ANOVA found that tensile strength, hardness, and elongation were significantly affected by the slag flux level, while impact strength was not. The experiments provide data on how slag flux content impacts weld metal properties in submerged arc welding.

1. Phase-1
Submerged Arc Welding Experiments
By
Dr. Bikram Jit Singh
Professor
MMDU Mullana

2. Runs Settings
Slag % (in
Flux)
Tensile Strength
(MPa)
Impact Strength (J) Hardness (HRC) Elongation (%)
1 0 540 106 12.5 29
2 0 547 157 16.0 10
3 0 560 144 20.0 9
4 0 548 50 18.0 25
5 0 545 75 11.0 32
6 0 555 100 9.0 50
7 0 552 107 14.0 35
8 0 540 125 8.0 45
9 0 547 110 9.0 40
10 0 546 100 7.0 30
11 20 502 125 14.0 39
12 20 445 101 17.5 10
13 20 494 111 22.0 25
14 20 501 78 25.1 9
15 20 496 88 20.2 32
16 20 493 99 12.1 22
17 20 485 98 10.2 20
18 20 491 60 5.9 45
19 20 520 101 12.1 32
20 20 525 121 10.2 55
21 40 442 88 16.0 25
22 40 440 70 19.0 21
23 40 460 85 32.0 22
24 40 455 110 22.1 25
25 40 430 105 20.6 35
26 40 435 99 12.3 31
27 40 456 89 11.8 30
28 40 450 88 10.6 25
29 40 445 75 8.9 23
30 40 450 95 12.5 25
31 60 465 110 18.0 33
32 60 460 95 22.0 25
33 60 489 96 16.2 23
34 60 463 94 17.5 31
35 60 445 92 21.0 30
36 60 410 82 19.5 30
37 60 440 88 19.0 29
38 60 475 99 17.0 32
39 60 498 101 17.5 17
40 60 461 108 17.2 22
41 80 440 92 15.0 23
42 80 385 55 19.0 5
43 80 429 65 5.0 15
44 80 458 99 12.5 19
45 80 445 125 18.0 30
46 80 439 101 10.0 44
47 80 440 135 20.2 41
48 80 449 80 18.2 28
49 80 425 78 18.2 25
50 80 422 81 15.0 22
51 100 402 68 11.5 20
52 100 412 45 15.0 4
53 100 425 78 3.5 18
54 100 370 65 5.1 38
55 100 385 112 10.2 32
56 100 392 110 10.0 21
57 100 401 99 15.3 18
58 100 400 100 14.2 11
59 100 398 55 13.3 15
60 100 401 50 12.5 14
6
Experimental Runs of SAW
1
2
3
4
5

3. Phase-2
Statistical Analysis

4. Quantitative Tool: One Way-ANOVA
Sr. No. Response Under
Analysis
Hypothification
1 Tensile Strength H0 = µ1 = µ2 = µ3 = µ4 = µ5 = µ6
Ha = µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6
2 Impact Strength H0 = µ1 = µ2 = µ3 = µ4 = µ5 = µ6
Ha = µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6
3 Hardness H0 = µ1 = µ2 = µ3 = µ4 = µ5 = µ6
Ha = µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6
4 Elongation H0 = µ1 = µ2 = µ3 = µ4 = µ5 = µ6
Ha = µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6

5. One-way ANOVA: Tensile Strength (MPa) versus Slag % (in Flux)
Source DF SS MS F P
Slag % (in Flux) 5 135794 27159 87.82 0.000
Error 54 16700 309
Total 59 152494
S = 17.59 R-Sq = 89.05% R-Sq(adj) = 88.03%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---+---------+---------+---------+------
0 10 548.00 6.25 (--*-)
20 10 495.20 21.68 (-*-)
40 10 446.30 9.65 (-*-)
60 10 460.60 25.09 (-*-)
80 10 433.20 20.18 (--*-)
100 10 398.60 14.71 (--*-)
---+---------+---------+---------+------
400 450 500 550
Pooled StDev = 17.59
1. Inferences for Tensile Strength

6. 100806040200
550
500
450
400
350
Slag % (in Flux)
TensileStrength(MPa)
398.6
433.2
460.6
446.3
495.2
548
Individual Value Plot of Tensile Strength (MPa) vs Slag % (in Flux)
100806040200
550
500
450
400
350
Slag % (in Flux)
TensileStrength(MPa)
400.5
439.5
462
447.5
495
547
398.6
433.2
460.6
446.3
495.2
548
Boxplot of Tensile Strength (MPa)

7. at most a 60% chance of detecting a difference of 12.074.
least a 90% chance of detecting a difference of 47.438, and
Based on your samples and alpha level (0.05), you have at
100%
47.438
90%
12.074
60%< 40%
12.074 9.7 - 60.0
33.856 60.0 - 100.0
37.489 70.0 - 100.0
41.713 80.0 - 100.0
47.438 90.0 - 100.0
Difference Power
with your sample sizes?
What difference can you detect
0 10 548 6.2539 (543.53, 552.47)
20 10 495.2 21.684 (479.69, 510.71)
40 10 446.3 9.6500 (439.40, 453.20)
60 10 460.6 25.092 (442.65, 478.55)
80 10 433.2 20.176 (418.77, 447.63)
100 10 398.6 14.714 (388.07, 409.13)
Slag % (in Flux) Size
Sample
Mean Deviation
Standard
95% CI for Mean
Individual
Statistics
41.713, consider increasing the sample sizes.
Power is a function of the sample sizes and the standard deviations. To detect differences smaller than
One-Way ANOVA for Tensile Stre by Slag % (in F
Power Report
Power
What is the chance of detecting a difference?

8. One-way ANOVA: Impact Strength (J) versus Slag % (in Flux)
Source DF SS MS F P
Slag % (in Flux) 5 4737 947 2.01 0.091
Error 54 25397 470
Total 59 30134
S = 21.69 R-Sq = 15.72% R-Sq(adj) = 7.91%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev -------+---------+---------+---------+-
-
0 10 107.40 30.86 (---------*--------)
20 10 98.20 19.41 (--------*---------)
40 10 90.40 12.42 (--------*--------)
60 10 96.50 8.51 (--------*--------)
80 10 91.10 24.99 (--------*--------)
100 10 78.20 25.33 (--------*--------)
-------+---------+---------+---------+-
-
75 90 105 120
Pooled St Dev = 21.69
2. Inferences for Impact Strength

9. 100806040200
150
125
100
75
50
Slag % (in Flux)
ImpactStrength(J)
78.2
91.1
96.5
90.4
98.2
107.4
Individual Value Plot of Impact Strength (J) vs Slag % (in Flux)
100806040200
150
125
100
75
50
Slag % (in Flux)
ImpactStrength(J)
73
86.5
95.5
88.5
100
106.5
78.2
91.1
96.5
90.498.2
107.4
Boxplot of Impact Strength (J)

10. at most a 60% chance of detecting a difference of 15.786.
least a 90% chance of detecting a difference of 57.112, and
Based on your samples and alpha level (0.05), you have at
100%
57.112
90%
15.786
60%< 40%
15.786 10.9 - 60.0
40.768 60.0 - 100.0
45.146 70.0 - 100.0
50.233 80.0 - 100.0
57.112 90.0 - 100.0
Difference Power
with your sample sizes?
What difference can you detect
0 10 107.4 30.862 (85.322, 129.48)
20 10 98.2 19.407 (84.317, 112.08)
40 10 90.4 12.420 (81.515, 99.285)
60 10 96.5 8.5147 (90.409, 102.59)
80 10 91.1 24.986 (73.226, 108.97)
100 10 78.2 25.332 (60.078, 96.322)
Slag % (in Flux) Size
Sample
Mean Deviation
Standard
95% CI for Mean
Individual
Statistics
50.233, consider increasing the sample sizes.
Power is a function of the sample sizes and the standard deviations. To detect differences smaller than
One-Way ANOVA for Impact Stren by Slag % (in F
Power Report
Power
What is the chance of detecting a difference?

11. One-way ANOVA: Hardness (HRC) versus Slag % (in Flux)
Source DF SS MS F P
Slag % (in Flux) 5 364.0 72.8 2.95 0.020
Error 54 1332.4 24.7
Total 59 1696.4
S = 4.967 R-Sq = 21.46% R-Sq(adj) = 14.19%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev -------+---------+---------+---------+--
0 10 12.450 4.450 (--------*--------)
20 10 14.930 6.064 (--------*--------)
40 10 16.580 7.017 (--------*--------)
60 10 18.490 1.863 (--------*--------)
80 10 15.110 4.757 (--------*--------)
100 10 11.060 4.016 (--------*--------)
-------+---------+---------+---------+--
10.5 14.0 17.5 21.0
Pooled StDev = 4.967
3. Inferences for Hardness

12. 100806040200
35
30
25
20
15
10
5
0
Slag % (in Flux)
Hardness(HRC) 11.06
15.11
18.49
16.58
14.93
12.45
Individual Value Plot of Hardness (HRC) vs Slag % (in Flux)
100806040200
35
30
25
20
15
10
5
0
Slag % (in Flux)
Hardness(HRC)
12
16.517.75
14.25
13.05
11.75 11.06
15.11
18.49
16.58
14.93
12.45
Boxplot of Hardness (HRC)

13. at most a 60% chance of detecting a difference of 4.6358.
least a 90% chance of detecting a difference of 13.279, and
Based on your samples and alpha level (0.05), you have at
100%
13.279
90%
4.6358
60%< 40%
4.6358 15.7 - 60.0
9.4807 60.0 - 100.0
10.497 70.0 - 100.0
11.678 80.0 - 100.0
13.279 90.0 - 100.0
Difference Power
with your sample sizes?
What difference can you detect
0 10 12.45 4.4500 (9.2666, 15.633)
20 10 14.93 6.0641 (10.592, 19.268)
40 10 16.58 7.0171 (11.560, 21.600)
60 10 18.49 1.8628 (17.157, 19.823)
80 10 15.11 4.7569 (11.707, 18.513)
100 10 11.06 4.0164 (8.1868, 13.933)
Slag % (in Flux) Size
Sample
Mean Deviation
Standard
95% CI for Mean
Individual
Statistics
11.678, consider increasing the sample sizes.
Power is a function of the sample sizes and the standard deviations. To detect differences smaller than
One-Way ANOVA for Hardness (HR by Slag % (in F
Power Report
Power
What is the chance of detecting a difference?

14. One-way ANOVA: Elongation (%) versus Slag % (in Flux)
Source DF SS MS F P
Slag % (in Flux) 5 782 156 1.39 0.241
Error 54 6061 112
Total 59 6843
S = 10.59 R-Sq = 11.43% R-Sq(adj) = 3.22%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev --+---------+---------+---------+-------
0 10 30.50 13.41 (---------*--------)
20 10 28.90 14.75 (--------*---------)
40 10 26.20 4.42 (--------*---------)
60 10 27.20 5.20 (---------*--------)
80 10 25.20 11.55 (---------*---------)
100 10 19.10 9.81 (--------*---------)
--+---------+---------+---------+-------
14.0 21.0 28.0 35.0
Pooled StDev = 10.59
4. Inferences for Elongation

15. 100806040200
60
50
40
30
20
10
0
Slag % (in Flux)
Elongation(%)
19.1
25.2
27.226.2
28.9
30.5
Individual Value Plot of Elongation (%) vs Slag % (in Flux)
100806040200
60
50
40
30
20
10
0
Slag % (in Flux)
Elongation(%)
18
24
29.5
25
28.5
31
19.1
25.227.226.2
28.9
30.5
Boxplot of Elongation (%)

16. at most a 60% chance of detecting a difference of 7.1553.
least a 90% chance of detecting a difference of 28.477, and
Based on your samples and alpha level (0.05), you have at
100%
28.477
90%
7.1553
60%< 40%
7.1553 9.6 - 60.0
20.317 60.0 - 100.0
22.498 70.0 - 100.0
25.035 80.0 - 100.0
28.477 90.0 - 100.0
Difference Power
with your sample sizes?
What difference can you detect
0 10 30.5 13.410 (20.907, 40.093)
20 10 28.9 14.746 (18.352, 39.448)
40 10 26.2 4.4171 (23.040, 29.360)
60 10 27.2 5.2026 (23.478, 30.922)
80 10 25.2 11.545 (16.941, 33.459)
100 10 19.1 9.8144 (12.079, 26.121)
Slag % (in Flux) Size
Sample
Mean Deviation
Standard
95% CI for Mean
Individual
Statistics
25.035, consider increasing the sample sizes.
Power is a function of the sample sizes and the standard deviations. To detect differences smaller than
One-Way ANOVA for Elongation ( by Slag % (in F
Power Report
Power
What is the chance of detecting a difference?

17. Phase-3
Regression Modeling
(for critical responses only)

18. General Regression Analysis: Tensile Strength (MPa) versus Slag % (in
Flux)
Regression Equation
Tensile Strength (MPa) = 550.09 - 4.45063 Slag % (in Flux) + 0.0721726 Slag %
(in Flux)*Slag % (in Flux) - 0.000428472 Slag % (in
Flux)*Slag % (in Flux)*Slag % (in Flux)
Coefficients
Term Coef SE Coef T
Constant 550.090 5.94031 92.6031
Slag % (in Flux) -4.451 0.57693 -7.7144
Slag % (in Flux)*Slag % (in Flux) 0.072 0.01434 5.0345
Slag % (in Flux)*Slag % (in Flux)*Slag % (in Flux) -0.000 0.00009 -4.5520
Term P
Constant 0.000
Slag % (in Flux) 0.000
Slag % (in Flux)*Slag % (in Flux) 0.000
Slag % (in Flux)*Slag % (in Flux)*Slag % (in Flux) 0.000
Summary of Model
S = 19.1691 R-Sq = 86.51% R-Sq(adj) = 85.78%
PRESS = 23149.0 R-Sq(pred) = 84.82%

19. Statistics
R-squared (adjusted)
P-value, model
P-value, linear term
P-value, quadratic term
P-value, cubic term
Residual standard deviation
85.78%
0.000*
0.000*
0.000*
0.000*
19.169
Cubic
Selected Model
78.71% 80.86%
0.000* 0.000*
0.000* 0.000*
- 0.008*
- -
23.460 22.239
Linear Quadratic
Alternative Models
100806040200
550
500
450
400
350
Slag % (in Flux)
TensileStrength(MPa)
Large residual
Y: Tensile Strength (MPa)
X: Slag % (in Flux)
* Statistically significant (p < 0.05)
Regression for Tensile Strength (MPa) vs Slag % (in Flux)
Model Selection Report
Fitted Line Plot for Cubic Model
Y = 550.1 - 4.451 X + 0.07217 X**2 - 0.000428 X**3

20. Slag % (in Flux) is statistically significant (p < 0.05).
The relationship between Tensile Strength (MPa) and
> 0.50.10.050
NoYes
P = 0.000
be accounted for by the regression model.
85.78% of the variation in Tensile Strength (MPa) can
100%0%
R-sq (adj) = 85.78%
1007550250
550
500
450
400
Slag % (in Flux)
TensileStrength(MPa)
causes Y.
A statistically significant relationship does not imply that X
Tensile Strength (MPa).
correspond to a desired value or range of values for
Flux), or find the settings for Slag % (in Flux) that
to predict Tensile Strength (MPa) for a value of Slag % (in
If the model fits the data well, this equation can be used
Y = 550.1 - 4.451 X + 0.07217 X**2 - 0.000428 X**3
relationship between Y and X is:
The fitted equation for the cubic model that describes the
Y: Tensile Strength (MPa)
X: Slag % (in Flux)
Is there a relationship between Y and X?
Fitted Line Plot for Cubic Model
Y = 550.1 - 4.451 X + 0.07217 X**2 - 0.000428 X**3
Comments
Regression for Tensile Strength (MPa) vs Slag % (in Flux)
Summary Report
% of variation accounted for by model

21. General Regression Analysis: Hardness (HRC) versus Slag % (in Flux)
Regression Equation
Hardness (HRC) = 11.9504 + 0.229152 Slag % (in Flux) - 0.0023558 Slag % (in
Flux)*Slag % (in Flux)
Coefficients
Term Coef SE Coef T P
Constant 11.9504 1.40101 8.52984 0.000
Slag % (in Flux) 0.2292 0.06589 3.47772 0.001
Slag % (in Flux)*Slag % (in Flux) -0.0024 0.00063 -3.72471 0.000
Summary of Model
S = 4.88827 R-Sq = 19.71% R-Sq(adj) = 16.90%
PRESS = 1496.83 R-Sq(pred) = 11.77%

22. Statistics
R-squared (adjusted)
P-value, model
P-value, linear term
P-value, quadratic term
P-value, cubic term
Residual standard deviation
16.90%
0.002*
0.001*
0.000*
-
4.888
Quadratic
Selected Model
0.00% 16.28%
0.754 0.005*
0.754 0.387
- 0.914
- 0.450
5.404 4.906
Linear Cubic
Alternative Models
100806040200
30
25
20
15
10
5
Slag % (in Flux)
Hardness(HRC)
Large residual
Y: Hardness (HRC)
X: Slag % (in Flux)
* Statistically significant (p < 0.05)
Regression for Hardness (HRC) vs Slag % (in Flux)
Model Selection Report
Fitted Line Plot for Quadratic Model
Y = 11.95 + 0.2292 X - 0.002356 X**2

23. (in Flux) is statistically significant (p < 0.05).
The relationship between Hardness (HRC) and Slag %
> 0.50.10.050
NoYes
P = 0.002
accounted for by the regression model.
16.90% of the variation in Hardness (HRC) can be
100%0%
R-sq (adj) = 16.90%
1007550250
30
20
10
0
Slag % (in Flux)
Hardness(HRC)
causes Y.
A statistically significant relationship does not imply that X
a desired value or range of values for Hardness (HRC).
or find the settings for Slag % (in Flux) that correspond to
to predict Hardness (HRC) for a value of Slag % (in Flux),
If the model fits the data well, this equation can be used
Y = 11.95 + 0.2292 X - 0.002356 X**2
the relationship between Y and X is:
The fitted equation for the quadratic model that describes
Y: Hardness (HRC)
X: Slag % (in Flux)
Is there a relationship between Y and X?
Fitted Line Plot for Quadratic Model
Y = 11.95 + 0.2292 X - 0.002356 X**2
Comments
Regression for Hardness (HRC) vs Slag % (in Flux)
Summary Report
% of variation accounted for by model

24. Phase-4
Micro-Structure Validation

25. Mean Table (showing validation of structure)
Slag %
(in Flux)
Tensile Strength
(MPa)
Impact Strength
(J)
Hardness
(HRC)
Elongation (%)
0 548 107.4 12.5 30.5
20 495.2 98.2 14.9 28.9
40 446.7 90.4 16.6 26.2
60 460.6 96.5 18.5 27.2
80 433.2 91.1 15.1 25.2
100 398.6 78.2 11.1 19.1
Micro structure for each settings

26. Phase-5
Optimization of Slag Content

27. 0.500.250.00-0.25-0.50
560
540
520
500
480
460
440
420
400
deviation from reference blend in proportion
FittedTensileStrength
Flux % 0.5000
Slag % 0.5000
Comp:RefBlend
Cox Response Trace Plot

28. 0.500.250.00-0.25-0.50
18
17
16
15
14
13
12
11
deviation from reference blend in proportion
FittedHardness
Flux % 0.5000
Slag % 0.5000
Comp:RefBlend
Cox Response Trace Plot

29. Response Optimization
Parameters
Goal Lower Target Upper Weight Import
Tensile Stre Target 420 425 430 1 1
Hardness Target 12 14 16 1 1
Starting Point
Components
Flux % = 1
Slag % = 0
Local Solution
Components
Flux % = 0.236307
Slag % = 0.763693
Predicted Responses
Tensile Stre = 424.743 , desirability = 0.948502
Hardness = 15.431 , desirability = 0.284495
Composite Desirability = 0.519465

30. Cur
High
Low0.51946
D
Optimal
d = 0.94850
Targ: 425.0
Tensile
y = 424.7425
d = 0.28449
Targ: 14.0
Hardness
y = 15.4310
0.51946
Desirability
Composite
0.0
1.0
0.0
1.0
[ ]:Slag %[ ]:Flux %
[0.2363] [0.7637]

31. Phase-6
Actual Trend Achieved at
Optimized Settings

32. Runs Tensile Strength (At 76% Slag) Hardness (At 76% Slag)
1 423.4 15
2 424.3 14
3 425 12.8
4 422.5 15.8
5 422 13
6 425 13.4
7 422 13.5
8 425 14
9 421 15.2
10 422.5 12.9
11 425.9 15.1
12 422.8 15.2
13 422 15
14 426.7 14
15 421.3 14.2
16 422.9 14
17 424.1 14.2
18 424.3 13.5
19 425.8 12.9
20 425.3 14.2
21 421.5 13.2
22 425.8 14.2
23 425 14
24 422.5 14.2
25 423.54 14.1
26 421.5 13.5
27 424 13.8
28 422.9 15.1
29 426.1 15.2
30 425.3 13.3

33. Total N 30
Subgroup size 1
Mean 423.73
StDev (overall) 1.6522
StDev (within) 1.9436
Process Characterization
Cp 0.86
Cpk 0.64
Z.Bench 1.91
% Out of spec (expected) 2.81
PPM (DPMO) (expected) 28072
Actual (overall)
Pp 1.01
Ppk 0.75
Z.Bench 2.26
% Out of spec (observed) 0.00
% Out of spec (expected) 1.20
PPM (DPMO) (observed) 0
PPM (DPMO) (expected) 12036
Potential (within)
Capability Statistics
429.0427.5426.0424.5423.0421.5420.0
LSL Target USL
Capability Histogram
Are the data inside the limits and close to the target?
Actual (overall) capability is what the customer experiences.
shifts and drifts were eliminated.
Potential (within) capability is what could be achieved if process
Capability Analysis for Tensile Stre
Process Performance Report

34. Total N 30
Subgroup size 1
Mean 14.083
StDev (overall) 0.81201
StDev (within) 0.79176
Process Characterization
Cp 0.84
Cpk 0.81
Z.Bench 2.26
% Out of spec (expected) 1.20
PPM (DPMO) (expected) 11997
Actual (overall)
Pp 0.82
Ppk 0.79
Z.Bench 2.19
% Out of spec (observed) 0.00
% Out of spec (expected) 1.43
PPM (DPMO) (observed) 0
PPM (DPMO) (expected) 14277
Potential (within)
Capability Statistics
1615141312
LSL Target USL
Capability Histogram
Are the data inside the limits and close to the target?
Actual (overall) capability is what the customer experiences.
shifts and drifts were eliminated.
Potential (within) capability is what could be achieved if process
Capability Analysis for Hardness (At
Process Performance Report