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International Journal of Engineering and Management Research, Vol.-3, Issue-4, August 2013

ISSN No.: 2250-0758

Pages: 18-22

www.ijemr.net

Parameters Optimization for Gas Metal Arc Welding of Austenitic

Stainless Steel (AISI 304) & Low Carbon Steel using Taguchi’s Technique

Pawan Kumar¹, Dr.B.K.Roy², Nishant3

1

Post Graduate Student, Om Institute of Technology & Management Hisar, Haryana, INDIA.

2

Direcor-Principal, Om Institute of Technology & Management Hisar, Haryana, INDIA.

3

Assistant professor, Om Institute of Technology & Management Hisar, Haryana, INDIA.

ABSTRACT

Welding is widely used by manufacturing engineers and production personnel to quickly and effectively set up manufacturing processes for new products. This study discusses an investigation into the use of Taguchi’s Parameter Design methodology for Parametric Study of Gas Metal Arc Welding of Stainless Steel & Low Carbon Steel. In this research work, bead on plate welds were carried out on AISI 304 & Low Carbon Steel plates using gas metal arc welding (GMAW) process. Taguchi method is used to formulate the experimental design. Design of experiments using orthogonal array is employed to develop the weldments. The input process variables considered here include welding current, welding voltage & gas flow rate. A total no of 9 experimental runs were conducted using an L9 orthogonal array, and the ideal combination of controllable factor levels was determined for the hardness to calculate the signal-to-noise ratio. After collecting the data signal-to-noise (S/N) ratios were calculated and used in order to obtain optimum levels for every input parameter. Subsequently, using analysis of variance (ANOVA) the significant coefficients for each input parameter on tensile strength & Hardness (PM, WZ & HAZ) were determined and validated.

Keywords:Dissimilar metals welding, GMA welding, Taguchi method, optimal parameters, hardness.

I.

INTRODUCTION

Metal Inert Gas welding is one of the most widely used processes in industry. The input parameters play a very significant role in determining the quality of a welded joint. In fact, weld geometry directly affects the complexity of weld schedules and thereby the construction and manufacturing costs of steel structures and Mechanical devices. Therefore, these parameters affecting the arc and welding should be estimated and their changing conditions during process must be known before in order to obtain optimum results; in fact a perfect arc can be achieved when all the parameters are in conformity. These are combined in two groups as first order adjustable and second order adjustable parameters defined before welding process. Former are welding current, arc voltage and Gas flow rate. These parameters will affect the weld characteristics to a

great extent. Because these factors can be varied over a large range, they are considered the primary adjustments in any welding operation. Their values should be recorded for every different type of weld to permit reproducibility.

II.

LITERATURE REVIEW

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parameters that optimize mechanical properties of weld specimen MS C20 material .Process parameters of MIG welding setup considered are welding current, welding voltage, welding speed. Assigning process parameters to L-9 orthogonal array, experiments were conducted and optimization condition was obtained along with the identification of most influencing parameters using S/N analysis, mean response analysis and ANOVA. K. Abbasi, S. Alam and Dr. M.I. Khan [5] study the effect of MIG welding parameters on the weld bead and shape factor characteristic .In this study the specimen used for welding is of mild steel. The welding parameters chosen as current, arc voltage, welding speed & heat input rate. The depth of penetration and weld width were measured for each specimen after the welding operation and effect of welding speed and heat input rate parameters on depth of penetration and weld width were investigated. Dr.L.Suresh Kumar, Dr.S.M.Verma, B.Suryanarayana, P.Kiran Kumar[6] discussed about the mechanical properties of austenitic stainless steel for the process of TIG and MIG welding.In this research the voltage is taken constant and various characteristics such as strength, hardness, ductility, grain structure, modulus of elasticity, tensile strength breaking point, HAZ are observed in two processes and analyzed and concluded that MIG welding is suitable where the higher ductility & higher hardness.TIG welded specimen can bear higher loads & have high yield strength. Sathish et al. [7] used taguchi method to design process parameters that optimize mechanical properties of weld specimen for Carbon steel pipe (A106 Grade B) and stainless steel pipe (A312 TP 316L) which find wide application in the field of chemical, oil and petroleum industries. Process parameters of GTA welding setup considered are gas flow rate, current and bevel angle. Assigning process parameters to L-9 orthogonal array, experiments were conducted and optimization condition was obtained along with the identification of most influencing parameters using S/N analysis, mean response analysis and ANOVA. Mr.L.Suresh Kumar,Dr.S.M.Verma,P.RadhakrishnaPrasad,P.Kiran kumar & Dr.T.Siva Shanker [8] discussed about the mechanical properties of austenitic stainless steel (AISI 304 & AISI 316) for the process of TIG and MIG welding.taguchi method is used to design the orthogonal array. Process parameters of MIG & TIG welding setup considered are current, speed, angle & root gap. Assigning process parameters to L-9 orthogonal array, experiments were conducted and various characteristics such as strength, hardness, ductility, grain structure, modulus of elasticity, tensile strength breaking point, HAZ are observed in two processes and analyzed and a comparison is made between these two & concluded that TIG welded specimen has higher tensile strength as compare to MIG welded specimen & MIG welded specimen have higher ductility as comparison to TIG welded specimen. Abbasi. K, Alam. S & Khan M.I [9] studies the effect of increased pressure on MIG welding arc. In this research the shielding gas used is a mixture of argon and carbon dioxide. The variation of welding parameters like feed rate, arc

voltage and arc current were observed on penetration. Variation of penetration with pressure at different wire feed speeds has also been studied. G Haragopal, P V R Ravindra Reddy, G Chandra Mohan Reddy and J V Subrahmanyam [10] used taguchi method to design process parameters that optimize mechanical properties of weld specimen for aluminum alloy (Al-65032), used for construction of aerospace wings. Process parameters of MIG welding setup considered are gas pressure, current, groove angle and pre-heat. Assigning process parameters to L-9 orthogonal array, experiments were conducted and optimization condition was obtained along with the identification of most influencing parameters using S/N analysis, mean response analysis and ANOVA.

III.

TAGUCHI’S DESIGN METHOD

Optimization of process parameters is the key step in the Taguchi method for achieving high quality without increasing cost. This is because optimization of process parameters can improve quality characteristics and the optimal process parameters obtained from the Taguchi method are insensitive to the variation of environmental conditions and other noise factors. Basically, classical process parameter design is complex and not easy to use. A large number of experiments have to be carried out when the number of process parameters increases. To solve this task, the Taguchi method uses a special design of orthogonal arrays to study the entire process parameter space with a small number of experiments only. A loss function is then defined to calculate the deviation between the experimental value and the desired value. Taguchi recommends the use of the loss function to measure the deviation of the quality characteristic from the desired value. The value of the loss function is further transformed into signal-to-noise (S/N) ratio.

3.1 Signal-to-Noise Ratio

There are 3 Signal-to-Noise ratios 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:

n = 10 Log10 (square of mean)/ Variance

3.2 Work Material

The work material used for present work is austenitic stainless steel & low carbon steel, the dimensions of the work piece length 100 mm, width 75mm, thickness 8mm. CO2 is used

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Table 1 Chemical composition of Stainless steel

C Mn S P Si Cu Ni

0.0195 1.72 0.001 0.03 0.29 0.17 9.14

Table 2 Chemical composition of Low carbon steel in wt%

Mtrl C Mn S P Fe

% 0.15 0.6 0.055 0.055 99.14

3.3 Orthogonal array Experiment

In the present study, three 3-level process parameters i.e. welding current, welding voltage and gas flow rate are considered. The values of the welding process parameters are listed in Table 3. The ranges and levels are fixed based on the screening experiments. The interaction effect between the parameters is not considered.

Table 3: Parameters, codes, and level values used for the orthogonal Array.

Parameters Code Level 1 Level 2 Level 3

Welding Current (Amp) A 100 150 200

Arc Voltage (Volt) B 23 25 30

Gas Flow Rate (CFH) C 20 23 25

The total degrees of freedom of all process parameters are 8. The degrees of freedom of the orthogonal array should be greater than or at least equal to the degrees of freedom of all the process parameters. Hence, L9 (33

Table 4 L9-3 Level Taguchi Orthogonal Array

) Orthogonal array was

chosen which has 8 degrees of freedom. This is shown in Table 4.

No of Runs

Control Factors

A B C

1 L 1 L 1 L 1

2 L 1 L 2 L 2

3 L 1 L3 L3

4 L 2 L 1 L 2

5 L 2 L 2 L3

6 L 2 L3 L 1

7 L3 L 1 L3

8 L3 L 2 L 1

9 L3 L3 L 2

3.4 Analysis of S/N Ratio

In the Taguchi Method the term ‘signal’ represents the desirable value (mean) for the output characteristic and the term ‘noise’ represents the undesirable value (standard Deviation) for the output characteristic. Therefore, the S/N ratio to the mean to the S. D. S/N ratio used to measure the quality characteristic deviating from the desired value. The S/N ratio S is defined as

S= -10 log (M.S.D.)

where, M.S.D. is the mean square deviation for the output characteristic.

To obtain optimal welding performance, higher-the better quality characteristic for Tensile strength must be taken. The M.S.D. for higher-the –better quality characteristic can be expressed

Where R = Number of repetitions

Ti Cr Mb V Tu Al Fe

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Table3: Experimental result for hardness and S/N ratio:

RUN CURRENT (Amp)

VOLTAGE (Volt)

GFR

(CFH) S/N RATIO

1 100 23 20 11.5675

2 100 25 23 12.1043

3 100 30 25 12.9425

4 150 23 23 13.5084

5 150 25 25 14.9222

6 150 30 20 7.6795

7 200 23 25 7.7661

8 200 25 20 8.7531

9 200 30 23 7.3513

Regardless of the category of the quality characteristic, a greater S/N ratio corresponds to better quality characteristics. Therefore, the optimal level of the process parameters is the level with the greatest S/N ratio. The S/N response table for Tensile strength is shown in Table No.4 as below.

Table 4: S/N response table for hardness.

3.5 Analysis of Variance (ANOVA)

Table 7.8 shows the result of the analysis of variance (ANOVA) for the Hardness (PM,WZ & HAZ). The analysis of variance was carried out at 95% confidence level. The main purpose of analysis of variance is to investigate the influence of the design parameters on Hardness by indicating that which parameters is significantly affected the quality characteristics. In our experimentation work, we have generated results for S/N ratios of Hardness (PM,WZ & HAZ). For S/N ratios, all the

factors and the interaction terms are significant at an α-level of 0.05. For S/N ratio, Selected parameters Arc Current

(p=0.0242), Arc Voltage (p=0.0317) & Gas Flow Rate (p=0.0346) are significant because their p-values are less than 0.05.

The purpose of ANOVA is to investigate which welding process parameters significantly affect the quality characteristics. This is accomplished by separating the total variability of the S/N Ratios, which is measured by the sum of squared deviations from the total mean of the S/N ratio, into contributions by each welding process parameter and the error. The percentage contribution by each of the welding process parameters in the total sum of the squared deviations can be used to evaluate the importance of the process parameter change on the quality characteristic.

Table 5: Result of analysis of variance for hardness.

Figure 1: Pie Chart for % age Contribution of Different Parameters for Hardness

Arc Voltage Level 2 25

Arc Current Level 1 100

Gas Flow Rate Level 3 25

Source DF Seq SS Adj

MS F

%

Contribution

Arc Current 2 34.718 17.359 3.12 52.45

Arc Voltage 2 10.364 5.182 0.93 15.66

Gas Flow Rate 2 9.998 4.999 0.90 15.10

Residual Error 2 11.114 5.557 16.79

Total 8

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From Figure 1 we can conclude that Arc Current is significantly affects the Hardness of Weld Zone, Parent Metal & Heat Affected Zone with contribution of 52.45%followed by arc voltage with contribution of 15.66 % and Gas flow rate with contribution of 15.10%.

3.6 Conformation test

The final step is to predict and verify the improvement of quality characteristic using the optimal level of the welding process parameters. The 3 experiments for the optimal inputs, i.e. welding current at level 1 and arc voltage at level 2, & gas flow rate at level 3 are conducted to obtain the greater hardness.

IV.

CONCLUSION

In this paper, the optimization of the process parameters for GMA welding of stainless steel and low carbon steel with greater weld strength has been reported. The Nominal-the-better quality characteristic is considered in the hardness prediction. The Taguchi method is adopted to solve this problem. The experimental result shows that the hardness is greatly improved by using this approach.

REFERENCES

[1] Anoop C A, Pawan Kumar, " Application of Taguchi Methods and ANOVA in GTAW Process Parameters Optimization for Aluminium Alloy 7039 ", International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 11, May 2013 M. Aagakhani, E. Mehardad, E. Hayati, “Parameteric optimization of GMAW process by Tagauchi Method on weld dilution”, International Journal of Modeling and Optimization, Vol. 1, No. 3, August’2011.

[2] Anand Sagar, Dr. G.K.Purohit, “Some Studies on Mig Hardfacing Of Mild Steel Components”, International Journal of Engineering Research and Development, Volume 4, Issue 8 (November 2012), PP. 42-56.

[3] R.K Rajkumar, Fatin Hamimi, Nachimani Charde “Investigating the Dissimilar Weld Joints of AISI 302 Austenitic Stainless Steel and Low Carbon Steel”, International Journal of Scientific and Research Publications, Volume 2, Issue 11, November 2012.

[4] S. V. Sapakal, M. T. Telsang, “Parametric

Optimization of MIG Welding using Taguchi Design Method”, International Journal of Advanced Engineering Research and Studies, Vol. I/ Issue IV/July-Sept., 2012/28-30.

[5] K. Abbasi , S. Alam and Dr. M.I. Khan “An

Experimental Study on the Effect of MIG Welding parameters on the Weld-Bead Shape Characteristics”, IRACST – Engineering Science and Technology: An International Journal, Vol.2, No. 4, August 2012.

[6] Dr. L.Suresh Kumar, Dr. S.M.Verma, B.

Suryanarayana, P. Kiran Kumar , “Analysis of Welding Characteristics on Stainless Steel for the Process of TIG and MIG with Dye Penetrate Testing”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 1, July 2012. [7] R. Sathish, B. Naveen, P. Nijanthan, K. Arun Vasantha

Geethan, Vaddi Seshagiri Rao“Weldability and Process Parameter Optimization of Dissimilar Pipe Joints Using GTAW”, International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp.2525-2530.

[8] Mr.L.Suresh Kumar, Dr.S.M.Verma, P.Radhakrishna Prasad, P.Kiran kumar, Dr.T.Siva Shanker “Experimental Investigation for Welding Aspects of AISI 304 & 316 by Taguchi Technique for the Process of TIG & MIG Welding”, International Journal of Engineering Trends and Technology- Volume2Issue2- 2011.

[9] Abbasi. K, Alam. S, Khan. M.I “An experimental study on the effect of increased pressure on MIG welding”, International Journal of Applied Engineering Research Dindigul , Volume 2, No 1, 2011.

Figure

Table 1 Chemical composition of Stainless steel
Table 5: Result of analysis of variance for hardness.

References

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