Regression Analysis of Submerged Arc Welding Process Parameters with Respect to Different Electrode Angle as Well as Welding Direction

DOI : 10.17577/IJERTV5IS040131

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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 Tripura-799046, India

Dr. Ajay Biswas

Assistant Professor Department of Mechanical Engineering National Institute of Technology Agartala

Agartala, Jirania, West Tripura-799046, 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. Bead-on plate weldment obtained by SAW on mild steel plate of specification as per IS2062 Grade-B, 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.

  1. 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 stick-out 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 twin-arc 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 input-output of the MIG welding process by using regression analysis based on the data collected as per full-factorial 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 signal-to 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].

  2. MATERIAL USED

    IS2062 Grade-B, 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: C-0.04%, Mn-0.4%, Si-0.05% 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).

  3. METHODOLOGY

    1. Experimental data

      Process parameters and their range Open circuit voltage (Volt): 29-54 Travel speed (m/min): 0.1 to 1.5 Wire feed rate (m/min): 0.5 to 4.0 Stick-out (mm): 25-31

      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).

    2. 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 stick-out distances were obtained from regression analysis to predict Bead width. Performed SAW conditions and corresponding weld bead values are presented in Table3.

      1. 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

        F-value

        P-value

        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

        2-Way 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

        F-value

        P-value

        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

        2-Way 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)

      2. 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

        R-sq

        R-sq(adj)

        R-sq(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)

      3. 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

    R-sq

    R-sq(adj)

    R-sq(pred)

    1.21786

    98.54%

    89.07%

    0.00%

    Table 6: Coded Coefficients

    Term

    Effect

    Coef

    SE Coef

    T-value

    P-value

    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

    R-sq

    R-sq(adj)

    R-sq(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 stick-out distance

  4. 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. 3-5 by normal probability plots corresponding to the individual welding conditions [Table 3]. Accuracy was presented in figs. 6-8 and found that percentage error for responses is less than ±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.

  5. 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. [1] Janez Tusek, Marjan Suban, High-productivity multiple-wire submerged-arc welding and cladding with metal-powder, Journal of Materials Processing Technology, Elsevier Science, Vol.133, 207-213, 2003.

  2. [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, 266-275.

  3. [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), 1699-1711.

  4. [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.

  5. [5] Kumanan S.K., Dhas R. and Gowthaman K. Determination of Submerged arc welding process parameters using Taguchi method and Regression Analysis, Indian journal of Engineering and material sciences, Vol.14, June 2007, 177-183.

  6. [6] Ghosh A., Chattopadhyaya S. and Dhas R.K, Critical analysis of confounded parameter of SAW, Procedia Engineering, 10, 2011, 2786- 2790.

  7. [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.

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