Effect of Process Parameters in MIG Welding on Mild Steel IS 2062 · 2018-02-21 · evaluated the parameters; welding current, wire diameter and wire feed rate to investigate their

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) pp. 2046-2054

© Research India Publications. http://www.ripublication.com

2046

Effect of Process Parameters in MIG Welding on Mild Steel IS 2062

R.SUDARSHAN1, Dr. M.Devaiah2, 1, 2Geethanjali College of Engineering and Technology, Cheeryal (V), Keesara (M), Medchal, Telangana, India.

Corresponding Author

Abstract

The main objective of this paper is to predict and optimize

MIG welding of some economically important similar

materials or dissimilar materials in industry through applying

a statistical approach to SPSS software, develop mathematical

models and optimize the welding operation. This was

achieved by controlling selected welding parameters; V-butt

angles, welding current and welding voltage position, to relate

the ultimate tensile strength to the selected input welding

parameters. The materials studied in this work are Mild steel

IS2062.

The experimental results which are obtain corresponding to

the effect of different V-angles such as 300, 450 and 600 at,

different welding current such as 80 Amp, 100 Amp and 110

Amp and various welding voltage 17 Volt, 19 Volt and 20

Volt on ultimate tensile strength of welding of Mild Steel

IS2062, are used to find out the significance of input

parameter on output by using SPSS and ANSYS method,

static analysis and thermal analysis.

This result shows better ultimate tensile strength prediction

capability and applicability to such industrial MIG welding

leading to effective selection of machining parameter for

better ultimate tensile strength.

Keywords: Ultimate tensile strength, Analysis of object

(ANSYS), Modelling in Pro-E, Metal inert gas (MIG)

welding, SPSS Software.

INTRODUCTION

Welding is a manufacturing process of creating a permanent

joint obtained by the fusion of the surface of the parts to be

joined together, with or without the application of pressure

and a filler material. The materials to be joined may be

similar or dissimilar to each other. The heat required for the

fusion of the material may be obtained by burning of gas or

by an electric arc. The latter method is more extensively used

because of greater welding speed.

Welding is extensively used in fabrication as an alternative

method for casting or forging and as a replacement for bolted

and riveted joints. It is also used as a repair medium e.g. to

reunite a metal at a crack or to build up a small part that has

broken off such as a gear tooth or to repair a worn surface

such as a bearing surface.

MonikaK.et al. [1], analyzed the Mechanical Properties of

MIG Welded Dissimilar Joints under the effect of heat input.

Welding current, voltage and speed of wire determines the

heat input. The IS2062, IS45C8, IS103Cr1 were used as a

base material. 1.2 mm diameter copper coated mild steel was

used as a filler wire. The both joints (IS2062 & IS45C8) and

(IS2062 & IS103Cr1) increased the tensile strength when

increased with the heat input and also increased the hardness

value when decreased with the heat input. M.Aghakhani et al. [2] have done work on optimization of gas metal arc welding

process parameter for increase quality and productivity of

weldment. In this research work for increasing quality and

productivity of weldment they have considered weld dilution

as output parameter and effect of input parameter wire feed

rate (W), welding voltage (V), nozzle-to-plate distance (N),

welding speed (S)and gas flow rate (G)was found on it. The

base material use for experiment is ST-37 steel plate and the

mixture of 80% argon and 20% CO2 is use as shielding gas.

The experiment was designed by Taguchi's L25 orthogonal

array and analysis was carried out by ANOVA method also

they develop mathematical model for weld dilution. From the

experimental result they found that the wire feed rate has the

most significant effect on the weld dilution while gas flow

rate has no effect on weld dilution. C. N. Patel et al. [3]

evaluated the parameters; welding current, wire diameter and

wire feed rate to investigate their influence on weld bead

hardness for MIG welding and TIG welding by Taguchi’s

method and Grey Relational Analysis (GRA). From the study

it was concluded that the welding current was most significant

parameter for MIG and TIG welding. By use of GRA

optimization technique the optimal parameter combination

was found to be welding current, 100 Amp; wire diameter 1.2

mm and wire feed rate, 3m/min for MIG welding.

EXPERIMENTAL WORK

The machine use for performing the experiment is

AUTOK600 SYNERGIC, manufactured by ESAB India

Limited in Gujarat Apollo Equipments Ltd.

Work piece material

From the literature survey of past researchers it is show that

the material selection in manufacturing process is most

important think as per process availability and customer’s

requirement. There is number of material used in modern

mailto:[email protected]



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industry but steel have corrosion resistive property and IS

2062 steel are shown in table 3.2 to 3.3

IS: 2062 Specification of Structural Steel for Fabrication

Chemical Composition of IS 2062 steel

Grade C % Mn % S % P % Si C. E.

%

Max. Max. Max. Max. Max. Max.

A 0.23 1.5 0.05 0.05 - 0.42

B 0.22 1.5 0.045 0.045 0.04 0.41

C 0.2 1.5 0.04 0.04 0.4 0.39

Mechanical Properties IS 2062 steel

Grade UTS

(MPA)

Y.S.(MPA) Min. EI. %

Min

Bend

A Min. < 20 mm 20-40 mm >

40 mm

5.65 So Test

B 410 250 240 230 23 3T

C 410 250 240 230 23 2T &

3T*

* 2T - Less than 25 mm.

* 3T - More than 25 mm.

Filler metal

The filler material use for the experiment is copper coated MS

material electrodes with size of 1.20 mm diameter.

Shielding Gas

A shielding gas mixture is selected for the experiments. It

contains 20%CO2 and 80% Argon.

Sample Preparation

Mild steel IS 2062 plates with the dimensions of 120 x 50 x 8

mm are prepared with the bevel angle of 300, 450 and 600.

These specimens are then welded with a root gap distance 1

mm. Figure shows the single V groove butt joint preparations.

Material Mild Steel

Grade IS2062

Width 50 mm

Length 120 mm

Thickness 8 mm

Figure 1: Schematic view of samples used for testing

Input Parameter Level and Value

Parameter

Level

Gas Pressure

(Psi)

Current

(Amp)

Voltage

(Volt) Angles

(A) (B) (C) (D)

1 100 80 17 30

2 100 100 19 45

3 100 110 20 60

Parameter considered for experiment

Input Parameters

1 Angles 300, 450 and 600

2 Current (Amp)

3 Voltage (V)

Constant Parameters

1 Electrode size

2 Shielded Gas

3 Gas Pressure (Psi)

Output Parameter

1 Ultimate strength (KN)

2 Yield strength (KN)



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UTM Machine frame

Figure 2: Schematic view of UTM and tensile test

SPSS SOFTWARE:

The SPSS software originally stood for statistical package for

the social sciences, reflecting the original market, although the

software is now popular in other fields as well, including the

health sciences and marketing.

SPSS STATISTICS is a software package used for statistical

analysis. Long produced by SPSS inc., it was acquired by IBM

in 2009. The current versions (2015) are officially named IBM

SPSS STATISTICS. Companion products of same family are

used for survey authoring and deployment (IBM SPSS DATA

COLLECTION), data mining (IBM SPSS MODELER), text

analysis, and collaboration and deployment (batch and

automated scoring services).

SPSS is widely used program for statistical analysis in social

science. It is also used by market researchers, health

researchers, survey companies, government, education

researchers, marketing organizations, data miners, and others.

The original SPSS manual has been described as one of

sociology’s most influential books” for allowing ordinary

researchers to do their own statistical analysis. In addition to

statistical analysis, data management (case selection, file

reshaping, creating derived data) and data documentation ( a

metadata dictionary was stored in the data file) are features of

the base software.

Statistics included in the base software:

Descriptive statistics: Cross tabulation, frequencies, descriptive,

explore, descriptive ratio statistics

Bi varieties statistics: Means, t-test, ANOVA, Correlation

(bivariate, partial, distances), nonparametric tests

Prediction for numerical outcomes: Linear regression

Prediction for identifying Groups: Factor analysis, cluster

analysis (two-step, k-means, hierarchical), and discriminate

RESULTS AND DISCUSSIONS

Mid welded steel IS2062 samples which are prepared with

different V-angles such as 300, 450 and 600 with variable current



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and voltage by using Universal Testing Machine (UTM) model

UTS -100. The test results such as Ultimate Strength and Yield

Strength values are listed in table 1 below

Table. 1: Ultimate Strength and Yield Strength values at different angle, voltage and current

S.

No Sample

Gas

pressure

V –Angle

used Current (A)

Voltage

(V)

Yield

Strength

Ultimate

Strength

1 1 100 30 80 17 100 150

2 2 100 30 100 19 156 204

3 3 100 30 110 20 157 205

4 1 100 45 80 17 125 169

5 2 100 45 100 19 158 195

6 3 100 45 110 20 182 203

7 1 100 60 80 17 105 150

8 2 100 60 100 19 105 159

9 3 100 60 110 20 105 167

The test results of yield strength with different parameters such as V-angle, current and voltage are represented in the bar charts as

shown in figure below

0

50

100

150

200

30 45 60

Yie

ld S

tren

gth

V-Angle

Sample 1

Sample 2

Sample 3

0

25

50

75

100

125

150

175

200

80 100 110

Yie

ld S

tren

gth

Current

Sample 1

Sample 2

Sample 3



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Figure 3: Test results of Yield strength with different V-angle, current and voltage

The test results of ultimate strength with different parameters

such as V-angle, current and voltage are represented in the bar

charts as shown in figure below

0

50

100

150

200

17 19 20

Yie

ld S

tren

gth

Voltage

Sample 1

Sample 2

Sample 3

0

25

50

75

100

125

150

175

200

225

30 45 60

Ult

imate

Str

ength

V- Angle

Sample 1

Sample 2

Sample 3

0

25

50

75

100

125

150

175

200

225

80 100 110

Ult

imate

Str

ength

Current

Sample 1

Sample 2

Sample 3



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Figure 4 Test results of Ultimate Strength with different V-angle, current and voltage

From figure 3 and 4 shows that the Ultimate Strength and Tensile Strength variation with respect to V-Angle, Current and voltage.

Model Summary

Regression

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .459a .211 -.052 31.690

a. Predictors: (Constant), angles, voltage

MODEL: SPSS allows you to specify multiple models in a

single regression command. This tells you the number of

model being reported

R: R is the square root of R-square and is the correlation

between the observed and predicted values of dependent

variable.

R-SQUARE: This is the proportional of variance in the

dependent variable (yield) which can be explained by the

independent variables (angles, voltage). This is an overall

measure of the strength of association and does not reflect the

extent to which any particular independent variable is

associated with the dependent variable.

ADJUSTED R-SQUARE: This is an adjustment of the R-

squared that penalizes the addition of extraneous predictors to

the model. Adjusted R-squared is computed using the formula

1-((1-Rsq) (N-1) / (N-K-1)), Where K is the number of

predictors.

STD.ERROR OF THE ESTIMATE: This is also referred

to as the root mean squared error. It is the standard deviation

of the error term and the square root of the mean square for

the residual in the ANOVA table

ANOVAa

Model Sum of

Squares df

Mean

Square F Sig

1

Regression 1608.794 2 804.397 .807 .492b

Residual 6025.429 6 1004.238

Total 7634.222 8

a. Dependent Variable: yield, b. Predictors: (Constant),

angles, voltage



model being reported.

REGRESSION, RESIDUAL, TOTAL: Looking at the

breakdown of variance in the Outcome variable, these are the

categories we will examine: Regression, Residual and Total.

The Total variance is partitioned into the variance which can

be explained by the independent variables (Model) and the

variance which is not explained by the independent variables

(Error).

SUM OF SQUARES: These are the sum of squares

associated with the three sources of variance, Total model and

residual. The total variance is partitioned into the variance

which can be explained by the independent variables

(Regression) and the variance which is not explained by the

independent variables (Residual).

Df: These are the degrees of freedom associated with the

sources of variance. The total variance has N-1 degree of

freedom. The Regression degree of freedom corresponds to

the no. of coefficients estimated minus 1. Including the

intercept, these are three coefficients, so the model has 3-1=2

0

25

50

75

100

125

150

175

200

225

17 19 20

Ult

imate

Str

enth

Voltage

Sample 1

Sample 2

Sample 3



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D.o.f. The error d.o.f is the Df total minus the DFmodel, 6.

F AND SIG. : This is the F-statistic the P-value associated

with it. The f-statistic is the mean square (Regression )

divided by the mean square (Residual ).The P-value is

compared to some alpha level in testing the null hypothesis

that all of model coefficients are 0.492

Coefficients

Model Unstandardized Coefficients Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant) 167.333 163.133 1.026 .345

voltage .762 8.469 .033 .090 .931

angles -1.089 .862 -.458 -1.263 .254

a. Dependent Variable: yield, b. Predictors in the Model:

(Constant), angles, voltage




B: These are the value for the regression equation for

predicting the dependent variable from the independent

variable. The regression equation is presented in many

different ways.

Ypredicted = b0+b1**1+b2**2+b3**3+b4**4.

Voltage: The coefficient of voltage is 0.762

Angles: The coefficient of angles is -1.089.

STD.ERROR: These are the standard errors associated with

the coefficients.

BETA: These are the standardized coefficients. These are the

coefficients that you would obtain if you standardized all of

the variables in the regression, including the dependent and all

of the independent variables and ran the regression. By

standardizing the variables before running the regression, you

have put all of the variables on the same scale, and you can

compare the magnitude of the coefficients to see which one

has more of an effect. You will also notice that the larger

betas are associated with the larger t-values and lower p-

values.

T AND SIG.: These are the t-statistics and their associated 2-

tailed p-values used in testing whether a given coefficients is

significantly different from zero.

y= A+B*x1+c*x2

Model Summary

Regression

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .612a .374 .165 21.547

a. Predictors: (Constant), angles, voltage

ANOVAa

Model Sum of

Squares df

Mean

Square F Sig

1

Regression 1664.238 2 832.119 1.792 .245b

Residual 2785.762 6 464.294

Total 4450.000 8

a. Dependent Variable: ultimate, b. Predictors:

(Constant), angles, voltage

Coefficients

Model Unstandardized Coefficients Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant) 106.167 110.922 .957 .375

voltage 6.071 5.759 .341 1.054 .332

angles -.922 .586 -.508 -1.573 .167

a. Dependent Variable: ultimate,

Excluded Variablesa

Model Beta

In

t Sig. Partial

Correlation

Collinearity

Statistics

Tolerance

1 current .b . . . .000

a. Dependent Variable: ultimate, b. Predictors in the

Model: (Constant), angles, voltage




R: R is the square root of R-square and is the correlation

between the observed and predicted values of dependent



2053

variable.

R-SQUARE: This is the proportional of variance in the

dependent variable (yield) which can be explained by the

independent variables (angles, voltage). This is an overall

measure of the strength of association and does not reflect the

extent to which any particular independent variable is

associated with the dependent variable.

ADJUSTED R-SQUARE: This is an adjustment of the R-

squared that penalizes the addition of extraneous predictors to

the model. Adjusted R-squared is computed using the formula

1-((1-Rsq) (N-1) / (N-K-1)), Where K is the number of

predictors.

Correlations

Table – 2. Correlations Obtained from SPSS Software

Current Voltage Angles Yield Ultimate

Current

Pearson Correlation 1 1.000** .000 .033 .341

Sig. (2-tailed) .000 1.000 .934 .370

N 9 9 9 9 9

Voltage

Pearson Correlation 1.000** 1 .000 .033 .341

Sig. (2-tailed) .000 1.000 .934 .370

N 9 9 9 9 9

Angles

Pearson Correlation .000 .000 1 -.458 -.508

Sig. (2-tailed) 1.000 1.000 .215 .163

N 9 9 9 9 9

Yield

Pearson Correlation .033 .033 -.458 1 .913**

Sig. (2-tailed) .934 .934 .215 .001

N 9 9 9 9 9

Ultimate

Pearson Correlation .341 .341 -.508 .913** 1

Sig. (2-tailed) .370 .370 .163 .001

N 9 9 9 9 9

**. Correlation is significant at the 0.01 level (2-tailed).

CONCLUSION

In this project work, experiments are carried out for Yield

strength and Ultimate Tensile Strength with respect to

variation of current, voltage and angles of V-butt joint,

keeping the gas constant. There are 9 experimental readings

taken for all variation of input parameter and they are use for

conduct the parametric study for optimization of welding

process parameter during welding of MS IS 2062 material.

The experimental result shows that the Yield strength and

Ultimate Tensile Strength ultimate tensile strength will

increase or decrease for the different angles. It is show that the

Yield strength and Ultimate Tensile Strength ultimate tensile

strength is increase with increase of current and voltage. Also

from the experimental result it show that the ultimate tensile

strength is increase initially with increase of voltage up to 20

Volt and then it decrease as increase the value of voltage.

From SPSS software we can observe that angles will make

influence and have significant in1st level interference than

voltage and current, in 2nd level interference we can observe

that the voltage, angles—angles will have significance, in

angle and current –angle will have influence i.e, least value in

coefficients table. where as in 3rd level interference among

voltage, current , angles we can observe from the correlation

table that angles have more significance over ultimate and

yield strength but in opposite direction.

References

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(2011), Vol.1, No. 3, pp.-216-220.

[3] C.N.Patel and Chaudhary.S, “Parametric Optimization

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Tungsten Inert Gas Welding by using Analysis of

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[4] Sheikh Irfan and Prof. Vishal Achwal, “An

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Documents

Effect of Process Parameters in MIG Welding on Mild Steel IS 2062 · 2018-02-21 · evaluated the parameters; welding current, wire diameter and wire feed rate to investigate their