Effect of Titanium powder addition on hardness in submerged arc welding

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ISSN: 2454-5031

www.ijlret.comǁ Volume 2 Issue 1ǁ January 2016 ǁ PP 28-34

Effect of Titanium powder addition on hardness in submerged arc welding

Atul Kathuria

Student M.tech (mechanical(I&P) Galaxy Global Group of Instituions(Ambala)

Deepak Gupta

Assistant Professor(Mechanical Dept.) Galaxy Global Group of Institutions(Ambala)

Abstract—This paper reports on an optimization of SAW process by the effects of Titanium powder on hardness by applying Taguchi methods to improve the quality of Submerged arc welding, and engineering development of designs for studying variation. IS 2062 steel is used as the work piece material for carrying out the experimentation to optimize the optimal parameter for higher hardness. There are three machining parameters i.e. current, electrode stick out and flux. Taguchi orthogonal array is designed with three levels of parameters with the help of software Minitab 15. In the first run nine experiments are performed and hardness is calculated. The hardness was considered as the quality characteristic with the concept of "the larger-the-better". The S/N ratio for the larger-the-better Where n is the number of measurements in a trial/row, in this case, n=1 and y is the measured value in a run/row. The S/N ratio values are calculated by taking into consideration with the help of software Minitab 15. The hardness values measured from the experiments and their optimum value for maximum hardness Every day scientists are developing new materials and for each new material, we need economical and efficient welding. It is also predicted that Taguchi method is a good method for optimization of various machining parameters as it reduces the number of experiments. The optimal value of Hardness is maximum on the parameter when current is 350 ampere, electrode stick out 25 mm and flux 3 is used. After all study it was found titanium powder is helpful to increase hardness of weld in submerged arc welding.

Index Terms— SAW welding, optimization, orthogonal array, ANOVA, S/N ratio.

I. INTRODUCTION

In submerged arc welding, the end of a continuous bare wire electrode is inserted into a mound of flux that covers the area or joint to be welded. An arc is initiated using one of six arc- starting methods, described later in this chapter. A wire-feeding mechanism then begins to feed the electrode wire towards the joint at a controlled rate, and the feeder is moved manually or automatically along the weld seam. For machine or automatic welding, the work may be moved beneath a stationary wire feeder. Additional flux is continually fed in front of and around the electrode, and continuously distributed over the joint. Heat evolved by the electric arc progressively melts some of the flux, the end of the wire, and the adjacent edges of the base metal, creating a pool of molten metal beneath a layer of liquid slag.

The melted bath near the arc is in a highly turbulent state. Gas bubbles are quickly swept to the surface of the pool.

The flux floats on the molten metal and completely shields the welding zone from the atmosphere. The liquid flux may conduct some electric current between the wire and base metal, but an electric arc is the predominant heat source. The flux blanket on the top surface of the weld pool prevents atmospheric gases from contaminating the weld metal, and dissolves impurities in the base metal and electrode and floats them to the surface. The flux can also add or remove certain alloying elements to or from the weld metal. As the welding zone progresses along the seam, the weld metal and then the liquid flux cool and solidify, forming a weld bead and a protective slag shield over it. It is important that the slag is completely removed before making another weld pass. [1]

II. LITRETURE SURVEY

A.M. Mercado, V.M. Hirata and M. L. Munoz (2) were investigation on Influence of the chemical composition of flux on the microstructure and tensile properties of submerged-arc welds. V.B. Trindade, R.S.T. Mello, J.C. Payão and R.P.R. Paranhos (3) said about the Influence of Zirconium on Microstructure and Toughness of Low-Alloy Steel Weld Metals. P. Kanjilal, T.K. Pal and S.K. Majumdar (4) were researched on combined effect of flux and welding parameters on chemical composition and mechanical properties of submerged arc weld metal. S.

Kumanan, J.E.R. Dhas and K. Gowthaman (5) were explaining Determination of submerged arc welding process parameters using Taguchi method and Regression analysis. S. Datta, A. Bandyopadhyay and P.K. Pal (6) were discussed about Application of Taguchi philosophy for parametric optimization of bead geometry and

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HAZ width in SAW using a mixture of fresh flux and fused flux. A. Singh, S. Datta, S.S. Mahapatra, T. Singha and G. Majumdar (7) were said that Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach.

The experimental studies were conducted under varying current, electrode stick out and flux.

III. TAGUCHI’SDESIGNMETHODE

Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering, advertising, Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying variation, but have criticized the inefficiency of some of Taguchi's proposals. "Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments and Dr. Taguchi's Signal-to-Noise ratios (S/N), which are log functions of desired output, serve as objective functions for optimization, help in data analysis and prediction of optimum results.

A. Signal to Noise Ratio

There are 3 Signal-to-Noise r a t i o s of common interest for optimization (I) Smaller-The-Better:

n = -10 Log10 [mean of sum of squares of measured data]

(Ii) Larger-The-Better:

n = -10 Log10 [mean of sum squares of reciprocal of measured data]

(Iii) Nominal-The-Best:

Square of mean n = 10 Log10

Variance B. Work Material

The work material used for present work is Mild Steel IS 2062, the dimensions of the work piece length 250mm, width of 125mm, thickness 10mm.

Table 1: % age composition of base metal C (%) Mn

(%) Si (%) P (%) S (%) 0.1567 0.9718 0.1175 0.01521 0.00780 C. Electrode

EH-14 type of electrode wire is used in this experiment. The diameter of the electrode wire is 3.2mm is constant.

Table 2: % age composition of electrode C (%) Mn (%) Si (%) P (%) S (%)

0.14 1.5 0.3 0.03 0.03

D. Flux

Firstly, AUTOMELT B31 type of flux is used in this experiment. Basicity index of AUTOMELT B31 is 1.5 with grain size of 0.25-2.0 mm and is being considered neutral flux, according to the basicity index.

Table 3: % age composition of flux SiO2+TiO2

(%)

CaO+MgO (%)

Al2O3+MnO (%)

CaF2 (%)

15 20 30 35

E. Welding parameters and their levels

For selection of parameters and their level is based on pilot study.

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Table 4: Welding Parameters and Their Levels Parameters Level 1 Level 2 Level 3

Current (Amp.) 250 300 350

Electrode stick out

(mm) 22 25 28

flux 1 2 3

Where 1st flux is (AUTOMELT B31), 2nd flux is (10 % titanium powder addition in AUTOMELT B31) and 3rd flux is (20 % titanium powder addition in AUTOMELT B31).

FL9 3 Level Taguchi Orthogonal Array

Taguchi’s orthogonal design uses a special set of predefined arrays called orthogonal arrays (OAs) to design the plan of experiment. These standard arrays stipulate the way of full information of all the factors that affects the process performance (process responses). The corresponding OA is selected from the set of predefined OAs according to the number of factors and t he i r l e ve l s t ha t w i l l b e u s e d i n t he experiment. Table No.5 shows L9 Orthogonal array

Table 5: L9 orthogonal array Experimen

t no.

Process Parameter Current

(Ampere)

Electrode stick out

(mm)

flux

1 L1 L1 L 1

2 L1 L2 L 2

3 L1 L3 L3

4 L2 L1 L 2

5 L2 L2 L3

6 L2 L3 L 1

7 L3 L1 L3

8 L3 L2 L 1

9 L3 L3 L 2

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IV. ANALYSISOFHARDNESSRESULTS

The testing would be carried on computerized Vickers Hardness test machine. Table 6 shows the result of hardness.

Table 6 Results for Hardness Tria

l No.

Current (A)

Electrode Stick out (mm)

Fux on base Metal (hvn)

on Welding

(hvn)

on HAZ (hvn)

Mean (hvn)

S/N ratio

1 250 22 1 168 180 160 169.3

3

44.54 4

2 250 25 2 168 221 183 190.6

6

45.43 8

3 250 28 3 170 235 188 197.6

6

45.68 6

4 300 22 2 167 230 189 195.3

3

45.59 3

5 300 25 3 166 253 192 203.6

6

45.79 2

6 300 28 1 169 192 180 180.3

3

45.08 6

7 350 22 3 171 240 192 201.0

0

45.81 2

8 350 25 1 166 234 178

192.6 6

45.42 0

9 350 28 2 167 245 199

203.6 6

45.86 3

V. ANOVAFORS/N RATIOS OF HARDNESS

The purpose of the analysis of variance (ANOVA) is to investigate which design parameters significantly affect the quality characteristic. This is to accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error. The result of ANOVA is shown in given table.

Table 7: Result of analysis of variance for Hardness

Source D.

O .F

Seq SS Adj SS

Adj

MS F P

% age cont ribu tion Current 2 0.3412

2

0.3412 2

0.170 61

7.68 0.11 5

23.6 1 Electrode

stick out 2 0.1068 9

0.1068 9

0.053

44 2.41 0.29

4 7.4

Flux 2 0.9525 1

0.9525 1

0.476 25

21.4 3

0.04 5

65.9 2

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Residual

error 2 0.0444 4

0.0444 4

0.022

22 3.07

Total 8 1.4450 5

The response table for signal to noise ratio are shown in Table 8

Table 8 Response table for signal to noise ratio Level Current Electrode

stick out

Flux

1 45.22 45.32 45.02

2 45.49 45.55 45.63

3 45.70 45.54 45.76

Delta 0.48 0.23 0.75

Rank 2 3 1

In our experimental analysis, the ranks indicate that flux has the greatest influence on the S/N ratio. For S/N ratio, current has the next greatest influence and the electrode stick out has the least influence. The optimum combination of parameters for hardness value is shown in Table 9.

Table 9 Optimum combination of parameters

Current Level 3 350 Ampere

Electrode

stick out Level 2 25 mm

Flux Level 3 3

Main effect plot of signal to noise ratio for hardness test is shown in Fig.1

350 300

250 45.8 45.6 45.4 45.2 45.0

28 25

22

3 2

1 45.8 45.6 45.4 45.2 45.0

Current

Mean of SN ratios

Electrode stick out

Flux

Main Effects Plot for SN ratios Data Means

Signal-to-noise: Larger is better

Fig. 1 Main effect plot of signal to noise ratio for hardness

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VI. ANOVA FOR MEAN OF HARDNESS The analysis of variance for mean is shown in Table 10

Table 10 Analysis of variance for mean Source D.O.F. Seq SS Adj

SS

Adj MS

F P %Age

contribution Current 2 262.99 262.9

9

131.4 9

8.17 0.10 9

25.19 Electrode

stick out

2 82.17 82.17 41.09 2.55 0.28 1

7.87

Flux 2 666.77 666.7

7

333.3 8

20.7 2

0.04 6

63.86 Residual

error

2 32.17 32.17 16.09 3.08

Total 8 1044.1

0

The response

for mean is shown in Table 11. The response table shows the average of each response characteristic for each level of each factor.

Table 11 Response table for mean

Level Current Electrode stick

out Flux

1 185.9 188.6 180.8

2 193.1 195.7 196.6

3 199.1 193.9 200.8

Delta 13.2 7.1 20.0

Rank 2 3 1

The optimum combinations of parameters is shown in Table 12

Table 12 Optimum combinations of parameters

Current Level 3 250 Ampere

Electrode Stick out Level 2 25 mm

Flux Level 3 3

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Main effect plot of mean for hardness test is shown in Fig.5

Fig. 2 Main effect plot of mean for hardness VII. CONCLUSION

Flux has significant effect on the hardness with contribution of 63.86 % and whereas current and electrode stick out travel speed has insignificantly effected with contribution of 25.19 % and 7.87 %. Hardness of mild steel of grade IS 2062 will be the maximum when we using current 350 ampere, electrode stick out 25 mm and flux 3.

ACKNOWLEDGMENT

First of all, I would like to express my gratitude to my supervisor Mr. Deepak Gupta, Assistant Professor of Mechanical Engineering Department, Glaxy Gloable group of institutions for his exceptional patience, for all his help and for being there for me through all good and bad times. Thank you for all what you have done for me and for providing me with valuable feedback in my research which made it stronger and more valuable. I am sincerely grateful for his priceless support and contribution toward my research.

REFERANCES [1]. R. S. Parmar, “Welding processes and technology”, 2nd Ed, 2008.

[2]. A.M. Mercado, V.M. Hirata and M. L. Munoz (2005) “Influence of the chemical composition of flux on the microstructure and tensile properties of submerged-arc welds”, Journal of Materials Processing Technology, Volume–169, issue 3, pp. 346–351.

[3]. V.B. Trindade, R.S.T. Mello, J.C. Payão and R.P.R. Paranhos (2005) “Influence of Zirconium on Microstructure and Toughness of Low-Alloy Steel Weld Metals”, International Journal of Advanced Manufacturing Technology, Volume–l5, pp. 284–286.

[4]. P. Kanjilal, T.K. Pal and S.K. Majumdar (2006) “Combined effect of flux and welding parameters on chemical composition and mechanical properties of submerged arc weld metal”. Journal of Materials Processing Technology, Volume– 171, issue 2, pp. 223–231.

[5]. S. Kumanan, J.E.R. Dhas and K. Gowthaman (2007) “Determination of submerged arc welding process parameters using Taguchi method and Regression analysis” Indian Journal of Engineering and Material Science, Volume–14, pp. 177–183.

[6]. S. Datta, A. Bandyopadhyay and P.K. Pal (2007) “Application of Taguchi philosophy for parametric optimization of bead geometry and HAZ width in SAW using a mixture of fresh flux and fused flux”, International Journal of Advanced Manufacturing Technology, Volume–36, pp. 689–698.

[7]. A. Singh, S. Datta, S.S. Mahapatra, T. Singha and G. Majumdar (2011) “Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach”, International Journal of Advanced Manufacturing Technology, Volume–24, pp. 35–44.

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