# 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

• 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

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 Â±5%.

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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)

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#### Scatterplot of Calculated bead width (mm) vs Measured bead width (mm)

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Fig.7: Accuracy of the calculated bead width valued with respect to measured data while electrode is forehand with 45 0.

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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.