OPTIMIZATION OF MIG WELDING TECHNIQUE PARAMETERS WITH …€¦ · Optimization of MIG Welding...

http://www.iaeme.com/ijptm/index.asp 16 [email protected]

International Journal of Production Technology and Management (IJPTM) Volume 7, Issue 2, July-December, 2016, pp. 16–27, Article ID: IJPTM_07_02_003

Available online at

http://www.iaeme.com/ijptm/issues.asp?JType=IJPTM&VType=7&IType=2

Journal Impact Factor (2016): 4.7601 (Calculated by GISI) www.jifactor.com

ISSN Print: 0976- 6383 and ISSN Online: 0976 – 6391

© IAEME Publication

OPTIMIZATION OF MIG WELDING TECHNIQUE

PARAMETERS WITH THE HELP OF TAGUCHI

METHOD

Raj Kumar Yadav

Assistant Professor & Head,

Department of Mechanical Engineering, AIST Sagar M.P., India.

Sandhaya Yadav

B.E. Research Scholar, Department of Mechanical Engineering, AIST Sagar M.P., India.

Anurag Singh and Pankaj Singh

Assistant Professor, Department of Mechanical Engineering, AIST Sagar M.P., India.

ABSTRACT

Welding is a basic manufacturing process for making components or assemblies. Recent

welding economics research has focused on developing the reliable machinery database to ensure

optimum production. In this paper, the optimization of welding input process parameters for

obtaining greater weld strength in the manual metal inert gas (MIG) welding of similar metals of

mild steel is presented. The Taguchi method is adopted to analyze the effect of each welding

process parameter on the weld strength, and the optimal process parameters are obtained to

achieve greater weld strength. Experimental results are provided to illustrate the proposed

approach.

Key words: MIG Welding, Taguchi method, Signal to noise ratio, Voltage, Current, WFR.

Cite this Article: Raj Kumar Yadav, Sandhaya Yadav, Pankaj Singh and Anurag Singh,

Optimization of MIG Welding Technique Parameters with the Help of Taguchi Method,

International Journal of Production Technology and Management (IJPTM), 7(2), 2016, pp. 16–27.

http://www.iaeme.com/ijptm/issues.asp?JType=IJPTM&VType=7&IType=2

1. HISTORY OF WELDING - IN THE BEGINNING

The Bible mentions Tubal Cain, "forged all types of tools from bronze and iron." He may have been one of

the first to join metals with the forging process. His flame was an open hearth into which he placed the

metals to be heated to the forging temperature.

In 1881 a Russian inventor, Benardos demonstrated the carbon electrode welding process. An arc was

formed between essentially a moderately consumable carbon electrode and the work. A rod was added to

provide needed extra metal. At right is an image of Nikolay Benardos on a stamp from Russia honoring

him as the "father of welding" A sketch of a carbon arc torch is shown to his right.

Optimization of MIG Welding Technique Parameters with the Help of Taguchi Method


2. MIG WELDING

Gas metal arc welding (GMAW), sometimes referred to by its subtypes metal inert gas (MIG)

welding or metal active gas (MAG) welding, is a welding process in which an electric arc is formed

between a consumable wire electrode and the work piece metal(s), which heats the work piece metal(s),

causing them to melt, and join. Along with the wire electrode, a shielding gas is fed through the welding

gun, which shields the process from contaminants in the air. The process can be semi-automatic or

automatic. A constant voltage, direct current power source is most commonly used with GMAW, but

constant current systems, as well as alternating current, can be used. There are four primary methods of

metal transfer in GMAW, called globular, short-circuiting, spray, and pulsed-spray, each of which has

distinct properties and corresponding advantages and limitations.

3. INTRODUCTION OF TAGUCHI METHOD

Every experimenter has to plan and conduct experiments to obtain enough and relevant data so that he can

infer the science behind the observed phenomenon. He can do so by,

3.1. Triel and Error Approch

Performing a series of experiments each of which gives some understanding. This requires making

measurements after every experiment so that analysis of observed data will allow him to decide what to do

next - "Which parameters should be varied and by how much". Many a times such series does not progress

much as negative results may discourage or will not allow a selection of parameters which ought to be

changed in the next experiment. Therefore, such experimentation usually ends well before the number of

experiments reaches a double digit! The data is insufficient to draw any significant conclusions and the

main problem (of understanding the science) still remains unsolved.

3.2. Design of Experiment

A well planned set of experiments, in which all parameters of interest are varied over a specified range, is a

much better approach to obtain systematic data. Mathematically speaking, such a complete set of

experiments ought to give desired results. Usually the number of experiments and resources (materials and

time) required are prohibitively large. Often the experimenter decides to perform a subset of the complete

set of experiments to save on time and money! However, it does not easily lend itself to understanding of

science behind the phenomenon. The analysis is not very easy (though it may be easy for the

mathematician/statistician) and thus effects of various parameters on the observed data are not readily

apparent. In many cases, particularly those in which some optimization is required, the method does not

point to the BEST settings of parameters. A classic example illustrating the drawback of design of

experiments is found in the planning of a world cup event, say football. While all matches are well

arranged with respect to the different teams and different venues on different dates and yet the planning

does not care about the result of any match (win or lose)!!!! Obviously, such a strategy is not desirable for

conducting scientific experiments (except for co-ordinating various institutions, committees, people,

equipment, materials etc.).

3.3. Taguchi Method

Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on

“ORTHOGONAL ARRAY” experiments which gives much reduced “variance” for the experiment with

“optimum settings “of control parameters. Thus the marriage of Design of Experiments with optimization

of control parameters to obtain BEST results is achieved in the Taguchi Method. "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

Taguchi Method treats optimization problems in two categories,

Raj Kumar Yadav, Sandhaya Yadav, Pankaj Singh and Anurag Singh


3.3.1. Static Problems

Generally, a process to be optimized has several control factors which directly decide the target or desired

value of the output. The optimization then involves determining the best control factor levels so that the

output is at the the target value. Such a problem is called as a "STATIC PROBLEM".

This is best explained using a P-Diagram which is shown below ("P" stands for Process or Product).

Noise is shown to be present in the process but should have no effect on the output! This is the primary

aim of the Taguchi experiments - to minimize variations in output even though noise is present in the

process. The process is then said to have become ROBUST.

3.3.2. Dynamic Problems

If the product to be optimized has a signal input that directly decides the output, the optimization involves

determining the best control factor levels so that the "input signal / output" ratio is closest to the desired

relationship. Such a problem is called as "DYNAMICPROBLEM".

This is best explained by a P-Diagram which is shown below. Again, the primary aim of the Taguchi

experiments - to minimize variations in output even though noise is present in the process- is achieved by

getting improved linearity in the input/output relationship.

4. STATIC PROBLEM (BATCH PROCESS OPTIMIZATION)

There are 3 Signal-to-Noise ratios of common interest for optimization of Static Problems;

4.1. Smaller- The Better

This is usually the chosen S/N ratio for all undesirable characteristics like " defects " etc. for which the

ideal value is zero. Also, when an ideal value is finite and its maximum or minimum value is defined (like

maximum purity is 100% or maximum Tc is 92K or minimum time for making a telephone connection is 1

sec) then the difference between measured data and ideal value is expected to be as small as possible. The

generic form of S/N ratio then becomes,

n = -10 Log10 [mean of sum of squares of {measured - ideal}]

4.2. Larger-The Better

This case has been converted to SMALLER-THE-BETTER by taking the reciprocals of measured data and

then taking the S/N ratio as in the smaller-the-better case.

4.3. Nominal-The Best

This case arises when a specified value is MOST desired, meaning that neither a smaller nor a larger value

is desirable.

N = -10 Log10 [squares of mean variance]

Example are;

(i) Most parts in mechanical fittings have dimensions which are nominal-the-best type.

(ii) Thickness should be uniform in deposition /growth /plating /etching.

5. STEPS IN TAGUCHI METHODOLOGY

Step-1: Identify the Main Function, Side Effects, and Falure Mode.

Step-2: Identify the Noise Factor Testing Condition and Quality Characteristics.

Step-3: Identify the Object Fun to be Optionized.

Step-4: Identify The Control Factors And Their Level.

Step-5: Select The Orthogonal Arry Matrix Experiment.

Step-6: Conduct The Matrix Experiment.



Step-7: Analyze Data Predict the Optimum Levels and Performance.

Step-8: Performation the Verification Experiment and Plan the Future Action.

5.1. Optimization Parameter Types

• Continuous parameter

• Discrete parameter

• Design parameter

5.2. Optimization Techniques

• Taguchi method

• Genetic method

• Regression analysis method

6. TENSILE STRENGTH

A measure of the ability of material to resist a force that tends to pull it apart. It is expressed as the

minimum tensile stress (force per unit area) needed to split the material apart.


http://www.iaeme.com/ijptm

7. TORSIONAL STRENGTH

Measure of the ability of a material to withstand a twisting load. It is the ultimate strength of a material

subjected to torsional loading, and is the maximum torsiona

Alternate terms are modulus of rupture and shear strength.

Grade C% Max

I 0.25


ijptm/index.asp 20

TORSIONAL STRENGTH


subjected to torsional loading, and is the maximum torsional stress that a material sustains before rupture.

Alternate terms are modulus of rupture and shear strength.

Chemical Composition of Mild Steel

CHEMICAL COMPOSITION

Mn % Max. S% Max.

1.30 0.05

Grade of Filler Wire


[email protected]


l stress that a material sustains before rupture.

P% Max.

0.05



Wire Feeder MIG Machine

MIG Welding Equipments

8. WORK PIECE

Table 1 Calculation

Test. No

Voltage

Wire Feed

Rate

Current

Torsional Testing

T(kgM) θθθθ L(mm)

1 350 40 340 7 3 100

2 350 60 340 6.5 5 90



3 350 80 340 10 5 85

4 360 100 350 6.5 6 89

5 360 120 350 11 6 76

6 360 140 350 7 5 89

7 370 160 360 6 4 95

8 370 180 360 8.5 4 86

9 370 200 360 6 5 87

Calculation for Torsional Rigidity

R.NO. T(N-M) θθθθ(Radian) L (m)

1 68.6 0.0523 0.1

2 63.7 0.0872 0.09

3 98 0.0872 0.085

4 63.7 0.1047 0.089

5 107.8 0.1047 0.076

6 68.6 0.0872 0.089

7 58.8 0.0698 0.095

8 83.3 0.0698 0.086

9 58.8 0.0872 0.087

Torsional Rigidity

GJ= Torsional Rigidity

T/J=Gθ/L=τ/R

T/J=CQ/L

CJ=TL/Q

1. 68.6x0.1/0.0523 = 131.66 NM2

2. 63.7x0.090/0.0872 = 65.74 NM2

3. 98x0.085/0.0872 = 95.52 NM2

4. 63.7x0.089/0.1047 = 54.14 NM2

5. 107.8x0.076/0.1047 = 78.25 NM2

6. 68.6x0.089/0.0872 = 70.01 NM2

7. 58.8x0.095/0.0898 = 80.02 NM2



8. 83.3x0.086/0.0698 = 102.63 NM2

9. 58.8x0.087/0.0872 = 58.66 NM2

S/N Ratio For Torsional Rigidity

S/N Ratio = -10 Log ∑ (1/ y2)

1. S/N Ratio = -10 log (1/131.66)2

= 42.38

2. S/N Ratio = -10 log ( 1/65.74 )2

=36.35

3. S/N Ratio = -10 log ( 1/95.52 )2

= 39.60

4. S/N Ratio = -10 log ( 1/54.14 )2

= 34.67

5. S/N Ratio = -10 log ( 1/78.25 )2

= 37.86

6. S/N Ratio = -10 log ( 1/70.01)2

= 36.93

7. S/N Ratio = -10 log (1/ 80.02 )2

=38.07

8. S/N Ratio = -10 log ( 1/ 102.63 )2

= 40.22

9. S/N Ratio = -10 log (1/ 58.66)2

= 35.66

Table 2

Parameter Level 1 Level 2 Level 3

Voltage 350 360 370

Current 340 350 360

WFR 80 100 120

Table 3 Calculation of S/N Ratio

Voltage Current WFR S/N Ratio

1 1 1 42.38

1 2 2 36.25

1 3 3 39.60

2 1 2 34.67



2 2 3 37.86

2 3 1 36.93

3 1 3 38.06

3 2 1 40.25

3 3 2 35.36

Table 4 for Torsional Rigidity and S/N Ratio

S.NO VOLTAGE CURRENT W.F.R TORSIONAL

RIGIDITY ( NM2)

S/N RATIO

FOR T.R.

1 350 340 80 131.66 42.38

2 350 340 100 65.74 36.25

3 350 340 120 95.52 39.60

4 360 350 100 54.14 34.67

5 360 350 120 78.25 37.86

6 360 350 80 70.01 36.93

7 370 360 120 80.02 38.06

8 370 360 80 102.63 40.25

9 370 360 100 58.66 35.36

Table 5 Response Table

S.NO. VOLTAGE CURRENT W.F.R

1 39.41 38.37 39.85

2 36.48 37.39 35.42

3 37.89 37.29 38.50

Calculation For Table 5

m = mean of three levels = 37.84

SUM OF SQUARE = [ (η1-m)2 + ( η2 – m)

2 + ( η3 –m)

2] x 3

For Voltage

Sum of Square = [(39.41- 37.84)2

+ (36.48-37.84)2 + (37.89 -37.84)

2] x 3

= 13.61



For Current

Sum of Square = [(38.37- 37.84)2

+ (37.39 -37.84)2 + (37.29 -

37.84)2] x 3

= 2.35

For F.W.R

Sum of Square = [(39.85- 37.84)2

+ (35.42 -37.84)2 + (38.50-

37.84)2] x 3

= 10.23

ERROR = Sum of Square – Sum of Square ( Actual )

Table 6 Result of Anova

C.F. D.O.F S.S. M.S. % C

V 2 13.61 6.80 34.83

I 2 2.35 1.17 6.01

W.F.R 2 10.23 5.11 26.18

ERROR 2 12.89 6.44 32.98

TOTAL 8 39.08 19.52

9. SIGNAL TO NOISE ( S/N ) RESPONSE LEVEL GRAPHS

370360350

48 .0

47 .5

47 .0

46 .5

46 .0

360350340

12010080

48 .0

47 .5

47 .0

46 .5

46 .0

V o lt age

Me

an

of

Me

an

s

Curre nt

W ire Feed Rate

Main Effects Plot for Means

Data Means

This Graph Describe the straight connecting the various level of voltage , current and wire feed rate.

This Graph Describe Straight Line Connecting the various level of voltage , current and wire feed rate.



The main effect of these parameters on mean S/N Ratio .

370360350

33.25

33.00

32.75

32.50

360350340

12010080

33.25

33.00

32.75

32.50

Voltage

Me

an

of

SN

ra

tio

s

Current

Wire Feed Rate

Main Effects Plot for SN ratios

Data Means

Signal-to-noise: Larger is better

10. RESULTS

In this Experiment we see that the main effective parameter is Voltage. The feed rate is less effective as

compare to voltage. The current is not very effective as compare to other parameters. So the voltage is very

effective parameters. The contribution of voltage is high. So the important parameter is voltage.

11. CONCLUSION

This experiment has presented an investigation on the optimization and the effect of welding parameters on

the Torsional Rigidity of MIG welded mild steel cicular rod. The level of importance of the welding

parameter on the Torsional Rigidity is determined by ANOVA. Based on the ANOVA method the highly

effective parameter on Torsional Rigidity was found as voltage and wire feed rate where as current was

less effective factor. An optimum parameter combination for the maximum Torsional Rigidity was

obtained by using analysis of S/N ratio.

REFERENCE

[1] G. S. Peace, Taguchi method, “A hands-on approach,” MA ,Addision-Wesley, 1992.

[2] J. Z. Zhang, J. C. Chen, E. D. Kirby, “Surface roughness optimization in an end-milling operation using

the Taguchi design method, ” Journal of Materials Processing Technology 184, pp. 233-239, 2007.

[3] P. J. Ross, “ Taguchi technique for quality engineering, ” New York: McGraw-Hill, 1988.

[4] Onkar N. panday “total quality management”

[5] m.aghakhani,e.mehrdad,and e.hayati “parameteric optimization of gas metal arc welding process by

taguchi method on weld dilution” M. Nalbat, H. Gokkaya, G. Sur, “Application of the Taguchi method

in the optimization of cutting parameters for surface roughness in turning,” Material and Design, vol. 28,

pp. 1379-1385, 2007.



[6] P.J. Ross. 2005. Taguchi Techniques for Quality Engineering. 2nd Ed. Tata McGraw Hill.

[7] Ugur Esme. 2009. Application of Taguchi method for the optimization of resistance spot welding

process. The Arabian Journal for Science and Engineering. 34(28): 519-528.

[8] Fundamentals of American Welding Society. 1980. Welding Handbook Volume 1.

[9] American Welding Society. 1980. Welding Handbook Volume 3.

[10] S. Aslanlar. 2006. The effect of nucleus size on mechanical properties in electrical resistance spot

welding of sheets used in automotive industry. Materials and Design. 27: 125-131.

[11] Mr. Harshal K. Chavan, Mr. Gunwant D. Shelake & Dr. M. S. Kadam, Effect of Heat Input and Speed

of Welding on Distortion in MIG Welding, International Journal of Industrial Engineering Research

and Development (IJIERD), 3(2), 2012, pp. 42–50.

[12] Murat Vural, Ahmet Akkus. 2004. On the resistance spot weldability of galvanized interstitial free steel

sheets with austenitic stainless steel sheets. Journal of Materials Processing Technology. 153-154: 1-6.

[13] R. K. Roy, “ A primer on the Taguchi method, ” New York: Van Nostrand Reinhold, 1990

[14] P. J. Ross, “ Taguchi technique for quality engineering, ” New York:McGraw-Hill, 1988.

[15] R. K. Roy, “ A primer on the Taguchi method, ” New York: Van Nostrand Reinhold, 1990

[16] A.S. Shahi., S. Pandey., “Modeling of the effects of welding conditions on dilution of stainless steel

claddings produced bygas metal arc welding procedures, ” Journal of Materials Processing Technology,

196.,pp. 339–344, 2008.

[17] Mr. Harshal K. Chavan, Mr. Gunwant D. Shelake, Dr. M. S. Kadam, Finite Element Model to Predict

Residual Stresses in MIG Welding. International Journal of Mechanical Engineering and Technology

(IJMET), 3(3), 2012, pp.350–361.

[18] K. Kishore., P.V.G. Krishna., K. Veladri., S.Q. Ali., “Analysis of defects in gas shield arc welding of

AISI 1040 steel usingTaguchi Method,” ARPN Journal of Engineering and Applied Sciences. vol. 5.

No. 1, January 2010.

OPTIMIZATION OF MIG WELDING TECHNIQUE PARAMETERS WITH …€¦ · Optimization of MIG Welding...

Documents

Transcript of OPTIMIZATION OF MIG WELDING TECHNIQUE PARAMETERS WITH …€¦ · Optimization of MIG Welding...