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

ISSN 1999-8716

Printed in Iraq

Vol. 08, No. 02, pp.  -  , June 2015

STUDY THE EFFECT OF CO2 MAG WELDING PROCESS

PARAMETERS ON THE HEAT INPUT AND JOINT

GEOMETRY DIMENSIONS USING EXPERIMENTAL AND

COMPUTATIONAL METHODS  2

Alkarem-Salah Sabeeh Abed ,

1

ii bSamir Ali Amin Alra 

1 Assistant Professor, Mechanical Engineering Department, University of Technology.

2 Lecturer, Department of Machines and Agricultural Equipment, University of Baghdad.

E-mail: arabiee2002@yahoo.com 1 , dr.salah2007@yahoo.com 2

(Received: 20/1/2014; Accepted: 14/4/2014)

ABSTRACT:-In this paper, predicted models for heat input and joint geometry

dimensions after CO2- MAG welding process have been developed. Before welding, steel

specimens were first prepared and then butt welded using electrode wire melted and supplied

into the molten pool by applying heat input continuously. Weld bead dimensions were first

measured, and then the results were analyzed to check the adequacy of the models by

Response Surface Method using DOE technique. These models were found capable of

predicting the optimum performance dimensions required for the joint geometry in terms of

weld bead width, reinforcement height and penetration. The obtained results indicated that

the heat input depends on voltage, wire feed speed and gas flow rate, while for the weld bead

dimensions; the gas flow rate has less effect. A comparison between the experimental and

predicted results was made, and a good agreement was found between them.

Keywords: Low Carbon Steel, CO2-MAG Process, Heat input, Joint Geometry, Modeling,

Experimental and Computational Methods, Optimization.

1. INTRODUCTION

CO2 -MAG is an arc welding process where heat is generated for arc between the

workpiece and a consumable metal electrode with an externally supplied gaseous shield of

gas either inert, such as CO2. Itis a versatile process, gives very little loss of alloying

elements and can be operated as semi as well as fully automated. A bare solid wire called

electrode is continuously fed to the weld zone, it becomes filler metal as it is consumed.

Electrical energy is supplied from the welding generator for melting wire and workpiece to be

welded. The weld is made by falling successive drops on the weld puddle. The arc and the

molten puddle are protected from contamination by the atmosphere (i.e., oxygen and

Diyala Journal

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Sciences

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Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

nitrogen) with an externally supplied gaseous shield of gas, such as CO2 which is a reactive

gas and is about 1.5 times heavier than air (1) .

CO2 gas is an odorless, colorless gas with a slightly pungent, acid taste and slightly

toxic. Differing from other reactive gases such as oxygen, CO2 can be used alone for GMAW

shielding gas applications. Pure CO 2 is the cheapest of the shielding gases and can be used as

a shield for welding steel up to 0.4% C and low-alloy steel. All the major commercial metals

can be welded by the process CO 2-MAG, including carbon steels, stainless steels, aluminum,

copper, titanium, Because there is some dissociation of the CO2 in the arc resulting in carbon

monoxide and oxygen being formed, the filler wire is triple deoxidized to prevent porosity,

and this adds somewhat to its cost and results in some small areas of slag being present in the

finished weld (2) .

Gas flow rate can greatly affect the quality of the weld, since too low a flow rate gives

inadequate gas shielding and leads to the inclusion of oxides and nitrides, while too high flow

rate can introduce a turbulent flow of the CO2 which occurs at a lower rate than with argon (3).

This affects the efficiency of the shield and leads to a porosity in the weld. Also, gas flow

rate, which can range from a few cubic feet per hour (cfh) to more than 60 cfh, depends on

the current developed, the torch size, the shielding gas composition and the surrounding

environment (drafts, etc.). In general, a higher current will require a larger torch and higher

flow rates. In addition, gas density, or the weight of the gas relative to air, has a major

influence on the minimum flow rate required to effectively shield the weld (3) .

Welding with the recommended heat input results in good mechanical properties in

the heat affected zone (HAZ). The heat supplied by the welding process affects the

mechanical properties of the welded joint. Heat input can be referred to as "the electrical

energy supplied by the welding arc to the workpiece. The most important characteristic of

heat input is that it governs the cooling rates in welds and thereby affects the microstructure

of the weld metal and the heat affected zone. A change in microstructure directly affects the

mechanical properties of welds. Therefore, the control of heat input is very important in arc

welding in terms of quality control (4) .

Quality of the welded joint in CO2-MAG welding process depends on number of

parameters, like type and thickness of base metal, design type, welding position, etc., but the

proper selection of welding parameters is also very important. Due to that, the weld bead

geometry in CO2-MAG welding process and heat input with regard to weld voltage, wire

feeding speed and gas flow rate were experimentally investigated in the present work, since

the proper selection of gas flow and heat input will provide a weld joint with satisfactory

geometrical characteristics (5) .

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A large amount of research works have been carried out to find out the most suitable

combination of input process parameters for a desired output using different welding

processes and various computer software as tools for modeling and optimization the weld

bead geometry, such as Taguchi (6) , Artificial neural networks (ANN) (7) , and Response

surface methodology (RSM) (8). Das et al. (9) studied the effect of arc voltage, current and

welding speed on the weld joint geometry, while Shoeb et al. (10) considered also the

influence of gas flow rate. In addition, Patel and Patel (11) in vestigated also the wire diameter

and wire feed rate during CO2-MAG welding process. But, there is a little work about

modeling and computational optimization of the closed butt weld bead geometry by using

Design of Experiment (DOE) with (RSM) technique to predict mathematical models that can

be used to obtain the optimum responses for any given input parameters. Therefore, the aim

of this paper is to study the influence of main welding parameters (voltage, wire feeding

speed and gas flow rate) on the heat input and final weld pool geometry during CO2-MAG

welding using DOE and RSM method.

2. EXPERIMENTAL PROCDURES

2.1 Material and Specimens Preparation

The material used in the present work is low carbon steel (LCS) plate with 5 mm

thickness in the hot rolled condition. This material was chemically analyzed in State

Company for Inspection and Engineering Rehabilitation (SIER) in Baghdad, and its chemical

composition is given in Table (1), showing that the experimental material conforms to the

standard low carbon steel type AISI 1010 (12) . The plate was cut to provide specimens with

size 50 mm× 25 mm×5 mm to be welded in a closed Butt weld joint design by CO2-MAG

process. Specimens from the as-received material were tensile tested according to ASTM E8

in Strength Laboratory / University of Technology-Baghdad, and the results are given in

Table (2).The results in this table represent the average of three readings (three samples).

2.2 Selection of Welding Parameters

Despite the use of CO 2- MAG welding process is influenced by number of

Parameters, three of them were only selected in this investigation: voltage, wire feeding

speed and gas flow rate in two levels (input parameters), as shown in Table (3). These

parameters were chosen according to the capacity of CO2- MAG welding machine and

practical experience of the welder skill.

2.3 Welding Procedure

Twenty specimens were welded by CO2-MAG process at different values of voltage,

wire feeding speed and gas flow rate according to design matrix established by Design of

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Experiment Version 8 Software as given in Table (4). The experiments were performed in

random manner to avoid any systematic error. The welding machine type 'INVERTER CO 2

MAG - BEAM-350'was used for welding the specimens in Korea-Iraq Vocational Training

Center in Baghdad, with wire a filler type 'AWS ER70S -6'1.2 mm diameter which is

specifically used for welding low carbon steel. The welding machine set up is shown in Fig.

(1) together with the specimens before and after welding process.

2.4 Measurements of Joint Geometry Dimensions

After each welding test, the weldment were cut, sectioned, ground, polished and

finally etched to see the profile of the joint geometry with necessary dimensions for

measuring purpose, which is schematically similar to that was shown in reference (13) , see

Fig. (2).The weld joint geometry dimensions in terms of bead width, reinforcement height

and bead penetration were measured after sectioning all specimens by using a digital caliper

with accuracy ± 0.01 mm. The results of measurements for these three dimensions as

responses are also listed in Table (4).

Since the heat input parameter has a significant effect on the quality of the joint

geometry, therefore it was decided to calculate the values of heat input for all weldments by

using the following equation (4) :

Where, Q = Heat input (kJ/mm), V = Voltage (volt), I = Current (Amp.) S = Welding

speed (mm/min) and η = Thermal efficiency.

For modeling and optimization the heat input at the same levels of used voltage, wire

feed speed and gas flow rate, the current reading was taken during the welding process from

the machine. Also, the welding speed was calculated for each test. Therefore, the heat input

value was calculated for each welding test taking into account that the thermal efficiency is

equal to 0.8 for MAG welding type (5). The results of calculated welding speed and heat input

are listed in Table (5).

3. RESULTS & DISCUSSION

The response surface methodology was achieved using the Design of Expert version 8

software to determine the predicted models for the dimensions of the weld joint geometry

(bead width, reinforcement height and depth of penetration), as responses in terms of the

selected input parameters (arc voltage, wire feeding speed and gas flow rate). The analyses of

variance (ANOVA) for RSM reduced quadratic models were determined for the bead

dimensions as given in Tables (6, 7, and 8). The results in these tables showed that the

voltage (A) and wire feeding speed (B) are statistically significant, since their P-values were

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Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

very small (< 0.5). This means that these two parameters contributed the highest effect on the

weld joint geometry, while the gas flow rate has no influence on the bead width and

reinforcement height [Tables (6 and 7)], since the gas flow rate term (C) is not in the model,

except that it affects the penetration depth due to the appearance of this term in the model, as

shown in Table (8).

The ANOVA analyses also pointed out that the quadratic effect was useful to

incorporate into bead width and reinforcement models, since the second order terms were

highly significant with a P-value lower than 0.05. In addition, it was noticed in Table (8) that

the interaction (AB) of voltage and wire feeding speed and the interaction (BC) of wire speed

rate and gas flow rate have the greatest impact on the weld penetration. Moreover, because

the lack of fit was insignificant (P-value > 0.05) in Tables (6, 7 and 8), these three models are

adequate and significant at 95% confidence. So, the final predicted equations for the weld

geometry dimensions in terms of the actual input factors are:

Bead width = - 447.67946 + 41.63455 * Voltage + 0.36693 * Wire feeding speeed

2 ……. (2)

003 * Wire feeding speed-1.07286E -

2

1.01179 * Voltage - 

Bead rienforcement height = + 117.50250 - 9.62500 * Voltage - 0.19505 * Wire feeding

2

Voltage 003 * Voltage * Wire feeding speed + 0.21063 *-speed + 5.70000E

) 2 ……….…..….… (3

004 * Wire feeding speed-+ 2.63000E 

Bead Penetration = + 17.22963 - 1.56938 * Voltage - 0.081675 * Wire feeding speed

+ 1.49031*Gas flow rate + 9.85000E-003 * Voltage * Wire feeding speed

- 0.010525 * Wire feeding speed * Gas flow rate …….…………. (4)

After the models were established, checking the adequacy of each model was

conducted to examine the predicted model. Two types of model diagnostics, the normal

probability plot and residuals versus the actual values plot, were used for verification, as

shown in Figs. (3 and 4) for bead width. It can be seen from these plots that there was no

violation of the normality assumption, since the normal probability plot followed a straight

line pattern, the residual was normally distributed, and as long as the residual versus the

predicted values show no unusual pattern and no outliers. Similar trends were observed in the

plots related to the reinforcement and penetration models. Also, these three models showed a

good agreement between the predicted and actual values for bead width, reinforcement height

and penetration, as depicted in Fig. (5).

In order to see all input factors on one plot to provide silhouette views of the response

surfaces, it helps to view the perturbation of the predicted responses caused by changing only

one factor at a time from the center point of the experimental region. In other words, for

response surface designs, the perturbation plot shows how the response changes as each

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factor moves from the chosen reference point (at the middle of the design space), with all

other factors held constant at the reference value. Accordingly, the perturbation plots for

these three models are illustrated in Fig. (6). So, this figure indicates that, individually, both

voltage and wire feeding speed largely affect the bead width and reinforcement, but they have

a slight influence on the penetration. This is likely due to the higher heat input that increased

the fusion of the material at the top surface of the joint. While, the gas flow rate has no effect

on the bead width and reinforcement but slightly affect the penetration and this is may be due

to the chemical affinity of the CO2 gas with the molten material of the joint.

Since the diagnosis of the residuals reveals no statistical problems with the models, so

the design of experiment generates the response surface plots in form of 2D contour, 3D

surface and cube plots. Figures (7 and 8) show the 2D contour plots for the bead width and

reinforcement, respectively as a function of voltage and wire feeding speed at gas flow rate of

10 L/min. It was found that welding at a gas flow rate of 8 and 12 L/min had no effect on

these responses. It can be noticed from Fig.(7) that increasing both voltage and wire feeding

speed increases the bead width due to the increase of quantity of the molten material that

resulted by the increasing of the thermal input. Also, Fig. (8) shows that the increase of both

voltage and wire speed decreases the reinforcement height, and this could be due to the

higher fluidity effect of the molten material with increasing heat input.

Regarding the penetration model, Fig. (9) manifests the 3D surface plot for the weld

penetration as a function of voltage and wire feeding speed at different gas flow rates. This

figure depicts that all input parameters are effective in this model and have a slight increase

on the bead penetration. However, the CO2 gas is more effective than the other parameters at

gas flow rate 8 L/min, Fig. (9a) due to the occurrence of higher penetration. And, this is

attributed to the less chemical reaction of this gas with the molten material of the joint at this

lower flow rate. Eventually, these observations are confirmed by the cube plot for

penetration, as shown in Fig. (9d).

3.2 Modeling of Heat Input

Similarly for the heat input, the analysis of variance (ANOVA) for response quadratic

model was constructed by DOE software as given in Table (9). This table shows that the

input parameters as individual in addition to the quadratic terms of wire feeding speed and

gas flow rate are all statistically significant and have the greatest influence on the heat input

response according to their P-values (< 0.05). The lack of fit test indicates a good model,

since it is insignificant with P-value greater than 0.05. So, this analysis indicates that this

model is significant at 95% confidence. Also, this model showed a good agreement between

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the predicted and actual values for heat input, as depicted in Fig. (10). so, the final predicted

equation for the heat input in terms of the actual input factors is:

Heat input = - 7.06339 - 0.090188 * Voltage + 0.080047 * Wire feeding speed+ 0.74389 

2 .. . (5)

0.039065 * Gas flow rate -

2

004 * Wire feeding speed-2.42014E -* Gas flow rate 

In order to diagnose the statistical properties of this model, it was found that the

residuals that falling on a straight line implying errors are normally distributed. Also, the

residuals versus predicted actual for heat input data exhibited no obvious pattern or unusual

structure implying models are accurate.

To gain perspective on the model, it is necessary to present the perturbation of the

predicted response resulted by varying only one parameter at a time from the center point of

the investigated region. Fig. (11) demonstrates the perturbation plot for the heat input model,

indicating that, individually, all input parameters affect the heat input response. The wire

feeding speed largely increased the heat input because of more molten material accumulated

in the weld joint at higher feeding speed, whereas both voltage and gas flow rate slightly

reduced the heat input due to the higher wire speed and higher and more chemical reaction of

CO2 gas with the higher accumulated molten metal at the weld joint.

Because the diagnosis of the residuals manifested no statistical problems, the response

surface plots were generated in terms of 3D surface plot, since all input parameters are

significant in this model. Fig. (12) Depicts the 3D surface plot for the heat input response as a

function of voltage and wire feeding speed at various gas flow rates. This figure shows the

wire feeding speed is more effective on the heat input response at 10 L/min gas flow rate

[Fig. (12b)] because of the higher molten material accumulated in the weld joint at higher

feeding speed. Whereas both voltage and gas flow rate have a slight influence on heat input,

and this is possibly ascribed to the higher wire speed and more chemical reaction of CO2

with the more accumulated molten material in the weld joint. Finally, these observations are

confirmed by the cube plot for penetration, as shown in Fig. (12d) for the heat input response.

3.3 Computational Optimization

A computational optimization method was used in this work by selecting the desired

goals for each factor and response. This computational optimization is provided by the

Design of Experiment software to find out the optimum combinations of parameters in order

to fulfill the requirements as desired. Therefore, this software used for the optimization

purpose; based on the data from the predictive models for four responses, weld bead width,

reinforcement height, penetration and heat input as a function of three factors: arc voltage,

wire feeding speed and gas flow rate.

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The computational optimization process involves combining the goals into an overall

desirability function. To develop the new predicted models, a new objective function, named

'Desirability' which allows to properly combining all the goals, was evaluated. Desirability is

an objective function, to be maximized through a computational optimization, which ranges

from zero to one at the goal. A higher value for desirability indicates the response value is

more desirable. If it is equal to zero, this means a completely undesired response (14) .

Adjusting its weight or importance may alter the characteristics of a goal, and the aim of the

optimization is to find a good set of conditions that will meet all the goals. Usually, the

weights are used to establish an evaluation of the goal's 3Dimportance when maximizing

desirability function; in this work, weights are not changed since the four responses have the

same importance and are not in conflict within each other.

The ultimate goal of this optimization was to obtain the maximum response that

simultaneously satisfied all the variable properties. Table (10) lists the constrains of each

variable for computational optimization of the weld bead width, reinforcement height,

penetration and heat input. According to this table, five possible runs fulfilled this specified

constrains to obtain the optimum values for weld bead width, reinforcement, penetration, heat

input and desirability, as given in Table (11). It can be seen that these runs gave a desirability

of 0.686 with the optimum values of the weld bead width (9.4793 mm), reinforcement

height(3.53625 mm), penetration (3.03997 mm), and heat input (1.27885 KJ/mm). Fig. (13)

shows the 3D surface plot for desirability as a function of voltage and wire feeding speed at 8

L/min gas flow rate.

4. CONCLUSIONS

1. RSM achieved by DOE technique has shown its effectiveness and usefulness as a tool to

predict the responses in MAG-CO2 welding technique for any given input parameters.

2. Qu adratic models were obtained by RSM achieved by DOE technique for the optimum

heat input with the optimum dimensions of the weld joint geometry of the welded parts

by the CO2-MAG process.

3. The arc voltage and wire feeding speed are found the most effective welding parameters

in the predicted quadratic models of weld bead width and reinforcement height, while gas

flow rate is only influential in the predicted models of bead penetration and heat input.

4. Wire feeding speed is the most effective welding parameter in predicted quadratic model

of the heat input, whereas both voltage and gas flow rate are less influential on this

response.

5. Efficient weld joints could be achieved using the welding conditions drawn from the

computational optimization. The optimum values of the weld bead width, reinforcement

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height, penetration and heat input are (9.4793 mm), (3.53625 mm), (3.03997 mm),

(1.27885 kJ/mm), respectively with a desirability of 0.686.

6. The results indicated that the process input parameters influence the heat input and the

weld bead joint geometry to a significant extent.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the technical support provided by the Korea-

Iraq Vocational Training Center in Baghdad for achieving this research work. All technical

facilities and kind assistance from Engineer Aziz Ibriheem Khlil (the general director of this

center), Mr Firas Mohmmed and Oday Aboud are gratefully acknowledged.

REFERENCES

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Parameters on Depth of Penetration of Welded Joint in MIG Welding Process",

International Journal of Research in Engineering and Technology (IJRET), Vol. 2, pp.

220-224.

2. Digvijay V. Jadeja and Satyam P. Patel, (2013), "A Review on Parametric Optimization

by Factorial Design Approach of MAG-CO2 Welding Process", International Journal of

Engineering Research and Applications (IJERA), Vol. 3, Issue 2, pp. 420-424.

3. Praxair's International Locations, (1998), at: www.praxair.com, Inc. Praxair Technology.

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Ltd.,pp.351.

5. Štefanija Klarićm, Ivan Samardžić E. W. E, and Ivica Kladarić, (2008), "MAG Welding

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Research/Expert Conference", Trends in the Development of Machinery and Associated

Technology, TMT, Istanbul  Turkey  pp. 26-30.

6. S. C. Juang and Y. S. Tarng (2002) ,"Process Parameters Selection for Optimizing the

Weld Pool Geometry in the Tungsten Inert Gas Welding of Stainless Steel ", Journal of

Materials Processing Technology, Vol. 122, pp. 33-37.

7. D. S. Nagesh and G. L. Datta, (2002), "Prediction of Weld Bead Geometry and

Penetration in Shielded Metal-arc Welding Using Artificial Neural Networks" , Journal of

Materials Processing Technology, Vol. 123 pp.303-312.

8. V. Gunaraj and N. Murugan, (1999), "Application of Response Surface Methodology for

Predicting Weld Bead Quality in Submerged Arc Welding of Pipes", Journal of Materials

Processing Tech., Vol. 88, pp. 266-275.

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9. Biswajit Das, B. Debbarma, R. N. Rai and S. C. Saha, (2013),"Influence of Process

Parameters on Depth of Penetration of Welded Joint in MIG Welding Process "

International Journal of Research in Engineering and Technology, Volume: 02, pp. 220-

224.

10. Mohd. Shoeb, Mohd. Parvez and Pratibha Kumari, (2013), "Effect of MIG Welding Input

Process Parameters on Weld Bead Geometry on HSLA Steel" International Journal of

Engineering Science and Technology (IJEST) Vol. 5 No.01 pp. 200 – 212.

11. Parth D Patel, Sachin P Patel, (2011), "Prediction of Weld Strength of Metal Active Gas

(MAG) Welding Using Artificial Neural Network", International Journal of Engineering

Research and Applications (IJERA), Vol. 1, pp. 36 - 44.

12. Thomas G. Digges and Samuel J. Rosenberg, (1960), "Heat Transfer and Properties of

Iron and Steel", National Bureau of Standards Monograph 18.

13. Vinod Kumar, (2010),"Development and Characterization of Fuxes for Submerged Arc

Welding" A thesis Doctor of Philosophy in Mechanical Engineering College of

Engineering – Punjabi University, Patiala-Indian.

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and Product Optimization UsingDesigned Experiment", John Wiley & Sons.

Table (1): Chemical Composition for Used LCS with Standard Type (wt%). 



Table (2): Mechanical Properties for Used LCS with Standard Type (wt%). 



Table (3): Levels of input parameters used with respective coding.

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Table (4): Design Matrix for Input Factors and Experimental Values of Output (Responses). 

Table (5): The Results of Calculated Welding Speed and Heat Input.

Culcalated Heat

input (KJ/mm)

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Table (6): Analysis of Variance (ANOVA) for Response Surface Reduced Quadratic Model

(Bead width).

Table (7): Analysis of Variance (ANOVA) for Response Surface Reduced Quadratic Model

(Bead Rienforcement).

Table (8): Analysis of Variance (ANOVA) for Response Surface Reduced Quadratic Model

(Bead Penetration).

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Table (9): Analysis of Variance (ANOVA) for Response Surface Reduced Quadratic Model

(Heat Input).





 Table (10): Constrains Used for the Computational Optimization.



Table (11): The Optimum Solutions of the Desirability.

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

Fig. (1): CO2 -MAG Welding Machine and Specimens Before and After Welding. 



Fig. (2): A schematic Illustration Profile of the Joint Geometry (13).

Fig. (3): Normal Probability Plot of Residuals for Bead Width Data.

Design-Expert® Software

Bead width

Color points by value of

Bead width:

12.58

5.05

Internally Studentized Residuals

N o r m a l % P r o b a b i li ty

Normal Plot of Residuals

-3.00 -2.00 -1.00 0.00 1.00 2.00

1

5

10

20

30

50

70

80

90

95

99

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Fig. (4): Residual versus Predicted Responses for Bead Width Data.

 (A)

(B)

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DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

 C 

Fig. (5): Predicted versus actual for (A) bead width data, (B) reinforcement and (C)

Penetration.

(A)

Design-Expert® Software

Factor Coding: Actual

Bead width

Actual Factors

A: Voltage = 20

B: Wire feeding speed = 150

*C: Gas flow rate = 10

Factors not in Model

C

Perturbation

Deviation from Reference Point (Coded Units)

B e a d w i d th

-1.000 -0.500 0.000 0.500 1.000

9

9.5

10

10.5

11

11.5

12

A

A

B

B

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

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STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

(B)

(C)

Fig. (6): Perturbation of (A) Bead Width, (B) Reinforcement and (C) Penetration on Wire

Feeding Speed and Gas Flow Rate.

Design-Expert® Software

Factor Coding: Actual

Bead rienforcement

Actual Factors

A: Voltage = 20

B: Wire feeding speed = 150

*C: Gas flow rate = 10

Factors not in Model

C

Perturbation

Deviation from Reference Point (Coded Units)

B e a d r ie n f o rc e m e n t

-1.000 -0.500 0.000 0.500 1.000

2.8

3

3.2

3.4

3.6 A

A

B

B

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

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STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

Fig. (7): Contour Graph of Bead Width as A functions of Voltage and Wire Feeding Speed at

10 L/min Gas Flow Rate.

Fig. (8): Contour Graph of Bead Reinforcement as A functions of Voltage and Wire Feeding

Speed at 10 L/min Gas Flow Rate.

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015













A) 

B) 

Design-Expert® Software

Factor Coding: Actual

Penetration

Design points above predicted value

Design points below predicted value

3.34

1.4

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 10

125

135

145

155

165

175

19

20

20

21

21

1

1.5

2

2.5

3

3.5

P e n e t ra tio n

A: Voltage B: Wire feeding speed

Design-Expert® Software

Factor Coding: Actual

Penetration

Design points above predicted value

Design points below predicted value

3.34

1.4

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 8

125

135

145

155

165

175

19

20

20

21

21

1

1.5

2

2.5

3

3.5

P e n e tr a tio n

A: Voltage B: Wire feeding speed

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

(C)









(D)

Fig. (9): 3D Graph of Bead Penetration as A function of Voltage and Wire Feeding Speed at

(A, B, C) Gas Flow Rate 8, 10 and 12 L/min, respectively and (D) Cube Shape for

Penetration. 

Design-Expert® Software

Factor Coding: Actual

Penetration

Design points above predicted value

Design points below predicted value

3.34

1.4

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 12

125

135

145

155

165

175

19

20

20

21

21

1

1.5

2

2.5

3

3.5

P e n e t ra t io n

A: Voltage

B: Wire feeding speed

Design-Expert® Software

Factor Coding: Actual

Penetration

X1 = A: Voltage

X2 = B: Wire feeding speed

X3 = C: Gas flow rate

Cube

Penetration

A: Voltage

B : W ir e fe e d i n g s p e e d

C: Gas flow rate

A-: 19 A+: 21

B-: 125

B+: 175

C-: 8

C+: 12

1.99338

2.69213

3.05713

1.65088

1.31713

2.01587

3.36588

1.95963

6

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

Fig. (10): Predicted Versus Actual Heat Input Data. 



Fig. (11): Perturbation of Heat Input on Wire Feeding Speed and Gas Flow Rate.

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015





(A) 























(B) 



Design-Expert® Software

Factor Coding: Actual

Heat input

Design points above predicted value

Design points below predicted value

1.408

0.3

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 8

125

135

145

155

165

175

19

20

20

21

21

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

H e a t in p u t

A: Voltage

B: Wire feeding speed

Design-Expert® Software

Factor Coding: Actual

Heat input

Design points above predicted value

Design points below predicted value

1.408

0.3

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 10

125

135

145

155

165

175

19

20

20

21

21

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

H e a t i n p u t

A: Voltage

B: Wire feeding speed

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015







Fig. (12): 3D Graph of Heat Input as A function of Voltage and Wire Feeding Speed (A, B,

C) at Gas Flow Rates 8, 10 and 12 L/min, respectively and (D) Cube Shape for Heat Input 

Design-Expert® Software

Factor Coding: Actual

Heat input

Design points above predicted value

Design points below predicted value

1.408

0.3

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 12

125

135

145

155

165

175

19

20

20

21

21

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

H e a t in p u t

A: Voltage

B: Wire feeding speed

Design-Expert® Software

Factor Coding: Actual

Heat input

X1 = A: Voltage

X2 = B: Wire feeding speed

X3 = C: Gas flow rate

Cube

Heat input

A: Voltage

B : W ir e fe e d i n g s p e e d

C: Gas flow rate

A-: 19 A+: 21

B-: 125

B+: 175

C-: 8

C+: 12

0.902313

0.752688

1.26944

1.11981

0.721938

0.572313

1.08906

0.939438

6

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

Fig. (13): 3D Surface Plot for Desirability.

Design-Expert® Software

Factor Coding: Actual

Desirability

1.000

0.000

X1 = A: Voltage

X2 = B: Wire feeding speed

Actual Factor

C: Gas flow rate = 8

125

135

145

155

165

175

19

20

20

21

21

0.200

0.300

0.400

0.500

0.600

0.700

D e s i ra b il it y

A: Voltage B: Wire feeding speed

0.6860.686

AND JOINT GEOMETRY INPUT THE HEAT MAG WELDING PROCESS PARAMETERS ON

2

STUDY THE EFFECT OF CO

DIMENSIONS USING EXPERIMENTAL AND COMPUTATIONAL METHODS

Diyala Journal of Engineering Sciences, Vol. 08, No. 02, June 2015

 CO2 

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ResearchGate has not been able to resolve any citations for this publication.

• Digvijay V. Jadeja
• Satyam P. Patel

timizing various Gas Metal Arc welding parameters including welding voltage, and nozzle to plate distance (NPD) by developing a mathematical model for weld deposit area of a mild steel specimen. And this mathematical model is developed with the help of the design of Matrix. MAG-CO2 is a process in which the source of heat is an arc format between consumable metal electrode and the work piece with an externally supplied gaseous shield of gas either inert such as CO2. This experimental study aims at Factorial design approach has been applied for finding the relationship between the various process parameters and weld deposit area. And after that we can easily find out that which parameter will be more affect OR which parameter will be more influence variable to WDA (Welding Deposition area) in the MAG-CO2 welding Process.

• D.S. Nagesh
• G.L. Datta

Bead geometry (bead height and width) and penetration (depth and area) are important physical characteristics of a weldment. Several welding parameters seem to affect the bead geometry and penetration. It was observed that high arc-travel rate or low arc-power normally produced poor fusion. Higher electrode feed rate produced higher bead width making the bead flatter.Current, voltage and arc-travel rate influence the depth of penetration. The other factors that influence the penetration are heat conductivity, arc-length and arc-force. Longer arc-length produces shallower penetration. Too small arc-length may also give rise to poor penetration, if the arc-power is very low.Use of artificial neural networks to model the shielded metal-arc welding process is explored in this paper. Back-propagation neural networks are used to associate the welding process variables with the features of the bead geometry and penetration. These networks have achieved good agreement with the training data and have yielded satisfactory generalisation. A neural network could be effectively implemented for estimating the weld bead and penetration geometric parameters. The results of these experiments show a small error percentage difference between the estimated and experimental values.

• V. Gunaraj
• N. Murugan

Response surface methodology (RSM) is a technique to determine and represent the cause and effect relationship between true mean responses and input control variables influencing the responses as a two or three dimensional hyper surface. Submerged arc welding (SAW) is used extensively in industry to join metals in the manufacture of pipes of different diameters and lengths. The main problem faced in the manufacture of pipes by the SAW process is the selection of the optimum combination of input variables for achieving the required qualities of weld. This problem can be solved by the development of mathematical models through effective and strategic planning and the execution of experiments by RSM. This paper highlights the use of RSM by designing a four-factor five-level central composite rotatable design matrix with full replication for planning, conduction, execution and development of mathematical models. These are useful not only for predicting the weld bead quality but also for selecting optimum process parameters for achieving the desired quality and process optimization.

• S. C. Juang
• Y.S. Tarng

In this paper, the selection of process parameters for obtaining an optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel is presented. Basically, the geometry of the weld pool has several quality characteristics, for example, the front height, front width, back height and back width of the weld pool. To consider these quality characteristics together in the selection of process parameters, the modified Taguchi method is adopted to analyze the effect of each welding process parameter on the weld pool geometry, and then to determine the process parameters with the optimal weld pool geometry. Experimental results are provided to illustrate the proposed approach.

A textbook of Welding Technology

• O P Khanna

O.P. Khanna, (2006), "A textbook of Welding Technology", Dhanpat Rai Publications Ltd.,pp.351.

Posted by: elliotellioteskewe0274321.blogspot.com

Source: https://www.researchgate.net/publication/328015741_STUDY_THE_EFFECT_OF_CO2_MAG_WELDING_PROCESS_PARAMETERS_ON_THE_HEAT_INPUT_AND_JOINT_GEOMETRY_DIMENSIONS_USING_EXPERIMENTAL_AND_COMPUTATIONAL_METHODS

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