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Mathematical modeling of GTAW process parameter forIncoloy825 by using factorial design approach

C.Prabaharan 1, Dr.A. Asha 2. 1 Assistant Professor, Department of Mechanical Engineering,Jayaram college of engineering and Technology

Trichy, Tamilnadu, India 621 0142Professor and Head, Department of Mechanical Engineering ,Kamaraj college of engineering and Technology

Virudhunagar Tamilnadu ,India 626 001

AbstractGas Tungsten Arc Welding (GTAW) is an electric arc welding process that produces an arc between anon consumable electrode and the work to be welded. The weld is shielded from the atmosphere by ashielding gas that forms an envelope around the weld area. This experimental study aims at optimizingvarious Gas Tungsten Arc welding parameters including welding voltage (V), welding current (I), gasflow rate (GFR), nozzle to plate distance (NPD) and Torch angle () by developing a mathematicalmodel for Bead geomentry of a Incoloy825 specimen. Factorial design approach has been applied for finding the relationship between the various process parameters and weld deposit volume The studyrevealed that the welding voltage, current and gas flow rate varies directly with weld deposit volume

and inverse relationship is found between torch angle and NPD with weld deposit volume.

Keywords : Gas Tungsten Arc Welding; Factorial Design Approach; Weld Deposit Volume .

1. Introduction

In this competitive world, the customers perceive the most reliable, high quality withlow cost product. In order to satisfy thecustomers demand, the manufacturingindustries are being forced to continuouslyoptimize their process parameters .GasTungsten Arc Welding famously abbreviatedGTAW, is one of the most important metal

joining process in manufacturing industries.The selection of improper GTAW process

parameters increases the power consumption,man power and cost of the product. So thatoptimization of GTAW process parameters ismust, to produce effective products. Modernfabrication process the newer materialstailarability is one of the challenging tasks for the development of the quality orientedfabrication equipments. In this way theINCONEL825 have a wide range of applications in many industries. TheINCONEL825 have an excellent weldabilitythrough a GTAW process. However to make aquality and economical aspects satisfied findingthe optimum parameters is much essential, thenonly the critical applications of INCONEL825tailarability will be a successful one. This

experimental aims at optimizing various gastungsten arc welding parameters such aswelding Voltage(V), Current(I), Nozzle ToPlate Distance(NPD), Gas Flow Rate(G), TorchAngle( ).Factorial design approach has been applied for finding the relationship between the variousinput process parameters and output

parameters. To develop a mathematical modelfor various output parameters with respect tothe input parameters through the factorialdesign approach.

1.1 Principle of GTAW

Gas Tungsten Arc Welding (GTAW), alsoknown as tungsten inert gas (TIG) welding is a

process that produces an electric arc maintained

between a non-consumable tungsten electrodeand the part to be welded. The Heat-AffectedZone, the molten metal and the tungstenelectrode are all shielded from atmosphericcontamination by a blanket of inert gas fedthrough the GTAW torch. Inert gas (usuallyArgon) is inactive or deficient in activechemical properties. The shielding gas serves to

blanket the weld and exclude the active properties in the surrounding air .

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2. Previous ResearchManoj Singla, Dharminder Singh,

Dharmpal Deepak et al. [1] investigated therelationship between the input parameters suchas current, voltage , speed, torch angle , gasflow rate, nozzle to plate distance and output

parameter weld deposit volume. By usingfactorial design approach.

Farhad Kolahan1, Mehdi Heidari. [2] Inthis research, the regression modeling is used inorder to establish the relationships betweeninput and output parameters for Gas Metal ArcWelding (GMAW) process

Ugur Esme et al. [3] investigated themulti response optimization of TIG welding

process to yield favorable bead geometry usingTaguchi method and Grey relation analysis.The significance of the factors on overallquality characteristics of the weldment has been

evaluated quantitatively by ANOVA. Theexperimental results show that the tensile load,HAZ, area of penetration, bead width, and beadheight are greatly improved by using greyrelation analysis in combination with Taguchimethod.

Bandhita Plubin et al. [4] determinedthe optimal factors of FCAW for steel ST37using response surface methodology andcentral composite design for optimizing thetensile strength of weldments .

T.Senthil Kumar et al. [5] studied theeffect of pulsed TIG welding parameters and pitting corrosion potential of aluminium alloys.ANOVA method was used to find significant

parameters and regression analysis has beenused to develop the mathematical model todetermine the pitting corrosion potential. It wasfound that peak current and pulse frequencyhave direct proportional relationship, while

base current and pulse-on-time have inverse proportional relationship with the pittingcorrosion resistance.

V. Vel murugan and v. Gunaraj et al.[6] In this study, the statistical method of three-factors, five-levels factorial central compositerotatable design has been used to developmathematical models to correlate angular distortion with multi-pass GMAW process

parameters. Further, these mathematical modelshelp to optimize the GMAW process and tomake it a cost-effective one by eliminating theweld defects due to angular distortion.

L.Suresh Kumar et al. [7] discussed themechanical properties of austenitic stainlesssteel AISI 304 and 316 and found out thecharacteristics of welded metals using TIG &MIG welding process. Voltage was takenconstant and various characteristics such asstrength, hardness, ductility, grain structure,HAZ were observed in two processes, analyzed

and finally concluded.Farhad Kolahan et al. [8] establishedinput-output relationships for metal active gaswelding for gas pipelines. Regression analysiswas performed on data collected as per Taguchidesign of experiments. Data adequacy wasverified using ANOVA method.

S.Kumanan et al. [9] determinedsubmerged arc welding process parametersusing Taguchi method and regression analysis.The % contribution of each factor is validated

by analysis of variance method. The plannedexperiments were conducted in the semi-automatic submerged arc welding machine andSN ratios are computed to determine theoptimum parameters.

P.Atanda et al. [10] conductedsensitization study of normalized 316Lstainless steel. The work was concerned withthe study of the sensitization anddesensitization of 316L steel at the normalizingtemperatures of 750-950C and soaking timesof 05, 1, 2 and 8 hours.

Sunniva R. Collins et al. [11] conductedweldability and corrosion studies of AISI 316Lelectro polished tubing and were orbitally andautogenously welded with welding parametersvaried to achieve an acceptable weld.

M. Aghakhani, E. Mehrdad, and E.Hayati et al. [12] In this research paper usingTaguchi's method of design of experiments amathematical model was developed using

parameters such as, wire feed rate (W), weldingvoltage (V), nozzle-to-plate distance (N),welding speed (S) and gas flow rate (G) onweld dilution.

Joseph I. Achebo et al.[13] This studyis intended to investigate the inadequacies of existing GMAW welding process parametersutilized by the investigated industrial firm in itssignature welding protocol, by suggestingalternative, uniquely crafted, and improved

process parameters to replace its existingsignature welding protocol, thereby improvingthe weld results by attaining higher UTS.

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3. Factorial Design Approach andTerminology

3.1Introduction

In statistics, a full factorial experiment is anexperiment whose design consists of two or

more factors, each with discrete possible valuesor "levels", and whose experimental units takeon all possible combinations of these levelsacross all such factors. A full factorial designmay also be called a fully crossed design. Suchan experiment allows studying the effect of each factor on the response variable, as well asthe effects of interactions between factors onthe response variable.

For the vast majority of factorial experiments,

each factor has only two levels. For example,with two factors each taking two levels, afactorial experiment would have four treatmentcombinations in total, and is usually called a22 factorial design. If the number of combinations in a full factorial design is toohigh to be logistically feasible, a fractionalfactorial design may be done, in which some of the possible combinations (usually at least half)are omitted

3.2Notation

To save space, the points in a two-levelfactorial experiment are often abbreviated withstrings of plus and minus signs. The stringshave as many symbols as factors, and their values dictate the level of each factor:conventionally, for the first (or low) level,and for the second (or high) level. The pointsin this experiment can thus be represented as

, , , and .The factorial points

can also be abbreviated by (1), a, b, and ab,where the presence of a letter indicates that thespecified factor is at its high (or second) leveland the absence of a letter indicates that thespecified factor is at its low (or first) level (for example, "a" indicates that factor A is on itshigh setting, while all other factors are at their low (or first) setting). (1) is used to indicate thatall factors are at their lowest (or first) values

3.3 Implementation

For more than two factors, a 2 k factorialexperiment can be usually recursively designedfrom a 2 k-1 factorial experiment by replicatingthe 2 k-1 experiment, assigning the first replicateto the first (or low) level of the new factor, andthe second replicate to the second (or high)level. This framework can be generalized to,e.g. , designing three replicates for three levelfactors, etc . A factorial experiment allows for estimation of experimental error in two ways.The experiment can be replicated, or thesparsity-of-effects principle can often beexploited. Replication is more common for small experiments and is a very reliable way of assessing experimental error. When the number

of factors is large (typically more than about 5factors, but this does vary by application),replication of the design can becomeoperationally difficult. In these cases, it iscommon to only run a single replicate of thedesign, and to assume that factor interactions of more than a certain order (say, between three or more factors) are negligible. Under thisassumption, estimates of such high order interactions are estimates of an exact zero, thusreally an estimate of experimental error. When

there are many factors, many experimental runswill be necessary, even without replication. For example, experimenting with 10 factors at twolevels each produces 2 10=1024 combinations.At some point this becomes infeasible due tohigh cost or insufficient resources. In this case,fractional factorial designs may be used.Aswith any statistical experiment, theexperimental runs in a factorial experimentshould be randomized to reduce the impact that

bias could have on the experimental results. In

practice, this can be a large operationalchallenge.Factorial experiments can be usedwhen there are more than two levels of eachfactor. However, the number of experimentalruns required for three-level (or more) factorialdesigns will be considerably greater than for their two-level counterparts. Factorial designsare therefore less attractive if a researcher wishes to consider more than two levels.

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3.4 Analysis

A factorial experiment can be analyzed usingANOVA or regression analysis. It is relativelyeasy to estimate the main effect for a factor. Tocompute the main effect of a factor "A",subtract the average response of allexperimental runs for which A was at its low(or first) level from the average response of allexperimental runs for which A was at its high(or second) level. Other useful exploratoryanalysis tools for factorial experiments includemain effects plots, interaction plots, and anormal probability plot of the estimated effects.

4. Methodology

In this project sixteen trials were conducted practically as per the fractional factorial design

approach and results were tabulated. Themathematical model for weld deposition volumewas developed based upon the experimentationand the ultimate aim of this research is toinvestigate the most influencing factor on welddeposit volume by plotting the results in thegraph.

4.1 Treatment Variables : Voltage (V) Current (I) Gas flow rate(GFR) Nozzle To Plate Distance (NPD) Torch angle( )

For conducting trial runs values or levels of thesevariables were chosen randomly from an infinite

potential level i.e. the sampling fraction for thesetrials runs was equal to zero, however, we got a

rough range of these factors from the literaturewe surveyed. With the help of these trials runseffective, representative s levels were develope dfor each factor (variables).The numbers of levels for to be included in theexperiment were chosen for each factor as per thedesign. These numbers of levels were two for each so as per the definition it is a 2 n (2*2*2*2*2)

factorial experiment. Where n is number of factors. If full factorial approach had been practiced, the number treatment combinationwould have been 16. But without affecting theaccuracy of the model and the objective of thetest we went for half factorial approach accordingto which the number of treatment combinations

becomes 2 n-1 (25-1 = 2 4 = 16). The levels for eachfactor were the highest value and the lowest valueof the factors in between and at which theoutcome was acceptable. These values wereoutcomes of trials runs. Highest value has beenrepresented by + and the lowest value has beenrepresented by - as mentioned in Table 2. As

per the design matrix the final runs wereconducted and the response i.e. the weld depositarea was measured and noted down against eachcombination. Then the values of differentcoefficients were calculated as per the modeling.These values of coefficients represent thesignificance of corresponding factors (variable)on the response.Higher the value of coefficients, higher the

influence of the variable on the response. Negative value of coefficients indicates theinverse relationship between variable andresponse.The calculation was done as per the followingmodel

.4.2 Design MatrixTable 1:design matrix of welding parameters

S.NOVoltage(V)

X1Current (I)

X2

Gas flowrate(GFR)

X3

Nozzle To PlateDistance (NPD)

X4

Torchangle( )

X5

1. + + + + +2. - + + + -3. + - + + -4. - - + + +5. + + - + -6. - + - + +7. + - - + +

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8. - - - + -9. + + + - -10. - + + - +11. + - + - +12. - - + - -13. + + - - +14. - + - - -15.

+ - - - -16. - - - - +

4.3 MATHEMATICAL MODELDEVELOPEDAssuming the values of responses as y1, y2, y3,y4, y5, y6, y7, y8,.y15 ,y16 against thetreatment Combinations 1, 2, 3, 4, 5, 6, 7,8,15, 16 respectively (as per the S. No. inthe matrix design) Y as the optimized value of

response (i.e. left hand side in the equation usedfor the showing the relation among the factors and the response). Relation

between main effects interactions effects andthe response has been shown in the followingequation .

Y=b 0+b1+b2+b3+b4+b5+b1X1+b2X2+b3X3+b4X4+b5X5++b 12(X1X2)+b 13(X1X3)+b 14(X1X4)+b 15(X1X5)+b 23(X2X3)+b 24(X2X4)+b 25(X2X5)+b 34(X3X4)+b 35(X3X5)+b 45(X4X5) ..Eqn -(i)Here,Y is the optimized weld deposit areayi (i = 1 to 16) is the response of the i th treatment combination.

b0 is the mean of all the responses bj (j =1 to 5) is the coefficient of j th mainFactor (j = 1 for voltage, 2 for current, 3 for NPD, 4 for Gas flow rate, 5for Torch angle)

b jk ( j, k=1 to 5) is the coefficient for interaction factor .

Values of all these coefficients were calculated as followings:

b0 = yi / 16

= [(y1+y2+y3+y4+y5+y6+y7+y8+y9+y10+y11+y12+y13+y14+y15+y16)] / 16 ..Eqn -(ii)

b1 = [(y1-y2+y3-y4+y5-y6+y7-y8+y9-y10+y11-y12+y13-y14+y15-y16)] / 16 ..Eqn -(iii)

b2 = [(y1+y2-y3-y4+y5+y6-y7-y8+y9+y10-y11-y12+y13+y14-y15-y16)] / 16 ..Eqn -(iv)

b3 = [(y1+y2+y3+y4-y5-y6-y7-y8+y9+y10+y11+y12-y13-y14-y15-y16)] / 16 ..Eqn -(v)

b4 = [(y1+y2+y3+y4+y5+y6+y7+y8-y9-y10-y11-y12-y13-y14-y15-y16)] / 16 ..Eqn -(vi)

b5 = [(y1-y2-y3+y4-y5+y6+y7-y8-y9+y10+y11-y12+y13-y14-y15-y16)] / 16 ..Eqn -(vii)

b12 = [(y1-y2-y3+y4+y5-y6-y7+y8+y9-y10-y11+y12+y13-y14-y15+y16)] / 16 ..Eqn -(viii)

b13 = [(y1-y2+y3-y4-y5+y6-y7+y8+y9-y10+y11-y12-y13+y14-y15+y16)] / 16 ..Eqn -(ix)

b14 = [(y1-y2+y3-y4+y5-y6+y7-y8-y9+y10-y11+y12-y13+y14-y15+y16)] / 16 ..Eqn -(x)

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b15 = [(y1+y2-y3-y4-y5-y6+y7+y8-y9-y10+y11+y12+y13+y14-y15+y16)] / 16 ..Eqn -(xi)

b23 = [(y1+y2-y3-y4-y5-y6+y7+y8+y9+y10-y11-y12-y13-y14+y15+y16)] / 16 ..Eqn -(xii)

b24 = [(y1+y2-y3-y4+y5+y6-y7-y8-y9-y10+y11+y12-y13-y14+y15+y16)] / 16 ..Eqn -(xiii)

b25 = [(y1-y2+y3-y4-y5+y6-y7+y8-y9+y10-y11+y12+y13-y14+y15+y15)] / 16 ..Eqn -(xiv)

b34 = [(y1+y2+y3+y4-y5-y6-y7-y8-y9-y10-y11-y12+y13+y14+y15+y16)] / 16 ..Eqn -(xv)

b35 = [(y1-y2-y3+y4+y5-y6-y7+y8-y9+y10+y11+y12-y13+y14+y15+y16)] / 16 ..Eqn -(xvi)

b45 = [(y1-y2-y3+y4-y5+y6+y7-y8+y9-y10-y11+y12-y13+y14+y15+y16)] / 16 ..Eqn -(xvii)

5. EXPERIMENTAL PROCEDURE AND TESTRESULTS

5. 1 Experimental Details

The experiments were conducted according tothe design matrix the response was recorded to

join 6 mm Inconel 825 Base metal for carryingout bead on inconel825 plate of 6 mm thicknesscorresponding to the material specification. Theappropriate chemical compositions of basematerial are shown in the table.2

Table:2: chemical compositions inconel825Element % Element %

Nickel 38.0 46.0 Carbon 0.05 maxChromium 19.5 23.5 Manganese 1.0 maxIron 22.0 min (~33%) Sulfur 0.03 maxMolybdenum 2.5 3.5 Silicon 0.5 maxCopper 1.5 3.0 Aluminium 0.2 maxTitanium 0.6 1.2

The test specimen having 6 mm thickness is used as the welding trails. The dimension of the work piece is shown in the figure.1

FIGURE.1. Dimensions of the work piece

120

6 6

100

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MOSFET Inverter DC TIG200ET weldingmachine has been used for the welding trials.Solid wire of 1.6 mm diameter TIG-521 (ER-

NiCr-3) as per the AWS/SFA - 5.14 standards

is used as the filler metal. Based on the preliminary test trails upper and lower valuesare fixed Weld beads are deposited using the

welding conditions stipulated by the designmatrix. The welding gun is allowed to cool inroom temperature and the spatter is cleanedfrom the nozzle after each run. After finishing

the welding trails to calculate the weld depositVolume (WDV) for all the welded pieces andare tabulated.

5.2 EXPERIMENTAL PARAMETERS

The parameters and ranges were selected for conduct the experiment shown in the table 3

Table:3 Parameters Range

5.3EXPERIMENTAL RESULTSUsing the factorial approach following results were tabulated after experimentation were shown intable 4.Table 4: Experimental results

S.no

VoltageX1

(volts)

Current(I)

X2 (Amp

s)

NPDX3

(mm)

Gas flowrate(G)

X4 (lt/hr)

Torchangle( )

X5

BWmm

BDmm

1 18 130 2 1400 75 5.8 6.4

2 14 130 2 1400 60 5.9 6.9

3 18 90 2 1400 60 5.8 6.3

Parameters Range

Polarity DCSP(Direct Current Straight Polarity)

Voltage 14-18 volts

Current 90-130 Amps

Torch angle 60-75 Degree

NPD 1.7-2 mm

Type of gas 100% ArgonTungsten Electrode EW-Th2 (2% Thoriated)

Gas flow rate 400-1400 liter/hour

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4 14 90 2 1400 75 5.2 6.7

5 18 130 1.7 1400 60 5.1 6.7

6 14 130 1.7 1400 75 5.6 6.8

7 18 90 1.7 1400 75 5.9 6.7

8 14 90 1.7 1400 60 5.1 6.7

9 18 130 2 400 60 5.8 6.9

10 14 130 2 400 75 6.0 6.9

11 18 90 2 400 75 5.7 6.6

12 14 90 2 400 60 5.9 6.8

13 18 130 1.7 400 75 5.3 6.2

14 14 130 1.7 400 60 5.7 6.9

15 18 90 1.7 400 60 5.5 6.916 14 90 1.7 400 75 5.6 6.5

Now as per the equations mentioned earlier the values of different effects for the BEAD

HEIGHT (BH) and BEAD WIDTH (BW) can be calculated as below:

S.NO C BH BW

1 b0 6.68125 5.61875

2 b1 0.09375 -0.00625

3 b2 -0.03125 0.03125

4 b3 -0.00625 0.14375

5 b4 0.03125 -0.06875

6 b5 -0.08125 0.01875

7 b12 -0.06875 -0.14375

8 b13 -0.04375 0.01875

9 b14 -0.03125 0.10625

10 b15 0.03125 0.04375

11 b23 0.05625 0.08125

12 b24 0.01875 0.01875

13 b25 0.05625 0.00625

14 b34 -0.08125 -0.01875

15 b35 -0.04375 -0.10625

16 b45 -0.08125 0.05625

The actual mathematical model for weld BEAD WIDTH

BW =+5.62-6.250E-003A+0.03B+0.14C-0.069D+0.019 E-0.14 AB+0.019AC+0.11AD+0.044AE+0.081BC+0.019BD+6.250E-003BE-0.019CD-0.11 CE+0.056DE

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The actual mathematical model for weld BEAD HEIGHT

BH =+6.68+0.094* A-0.031* B-6.250E-003 C+0.031D-0.081 E-0.069 AB-0.04A C-0.031AD+0.031AE+0.056 BC+0.019B D+0.056B E-0.081C D-0.044C E-0.081 D E

The results of present investigation in shows the influence of treatment variables (Current, Voltage, NPD,GFR,TA) on welding deposition area (WDA) as shown in Fig. 2.

-0.1

-0.08

-0.06-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

A B C D E

BH

BH

-0.1

-0.05

0

0.05

0.1

0.15

0.2

A B C D E

Series1

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6.CONCLUSION1. Results indicate that processes variables

influence the weld bead area to asignificant extent.

2. Various welding variables whichinfluence WDV were identified and their quantitative

Influence on the same was investigated.

3. Welding current ,Voltage ,GFR ,TA werefound to be most influencing variable toWDV

4. The weld deposit rate were increased inthe increment value of parameters suchas voltage, current and gas flow rate.

5. The weld deposit rate is decreased at themaximum value of torch angle.

6. The nozzle to plate distance was nothaving any significant effect on welddeposit rate.

REFERENCES

[1] Parametric Optimization of Gas Metal ArcWelding Processes by Using Factorial DesignApproach - Manoj Singla, Dharminder Singh,Dharmpal Deepak Journal of Minerals &Materials Characterization & Engineering, Vol.9, No.4, pp.353-363, 2010

[2] A New Approach for Predicting andOptimizing Weld Bead Geometry in GMAW -Farhad Kolahan1, Mehdi Heidari InternationalJournal of Aerospace and MechanicalEngineering 5:2 2011

[3] Ugur Esme, Melih Bayramoglu, YugutKazancoglu, Sueda Ozgun, Optimiz ation of weld bead geometry in TIG welding processusing grey relation analysis and Taguchimethod, Materials and technology 43 (2009)3, 143-149.

[4] Bandhita Plubin, NarongchaiSathavornvichit, Putipong Bookkamana,Central composite design in optimiz ation of the factors of automatic flux cored arcwelding, Proceedings of the 2nd IMT -GTRegional Conference on Mathematics,Statistics and Applications, June 13-15, 2006. .

[5]. T.Senthil Kumar, V.Balasubramanian,M.Y.Sanavullah, S.Babu, Effect of pulse dcurrent TIG welding parameters on pittingcorrosion behavior of AA6061 aluminiumalloy, Journal of Material ScienceTechnology, Vol.23 No.2, 2007.

[6]. Effects of process parameters on angular Distortion of gas metal arc welded Structural

steel plates - by V. Vel murugan and v.Gunaraj. Supplement to the welding journal,november 2010

[7] L.Sureshkumar, S.M.Verma,P.Radhakrishna Prasad, P.Kiran Kumar, T.SivaShanker, Experimental investigation for welding aspects of AISI 304 & 316 by Taguchitechnique for the process of TIG & MIGwelding, International Journal of EngineeringTrends and Technology, Vol.2 issue 2, 2011.

[8] Farhad Kolahan, Mehdi Heidari,Modeling and optimization of MAG weldingfor gas pipelines using regression analysis andsimula ted annealing algorithm, Journal of Scientific & Industrial Research, Vol.69, April2010, pp.259-265.

[9] S.Kumanan, J.Edwin Raja Dhas &K.Gowthaman , Determination of submergedarc welding process parameters using Taguchimethod and regression analysis, Indian Journalof Engineering & Material Sciences Vol.14,June 2007, pp.177-183.

[10] P.Atanda, A.Fatudimu, O.Oluwole,Sensitization study of nor malized 316Lstainless steel, Journal of Minerals &Materials Characterization & Engineering,Vol.9, no.1, pp.13-23, 2010.

[11] Sunniva.R.Collins, Peter.C.Williams,Weldability and corrosion studies of AISI316L electropolished tubing, Swagelok Research

[12] Parametric Optimization of Gas Metal ArcWelding Process by Taguchi Method on Weld

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Dilution - M. Aghakhani, E. Mehrdad, and E.Hayati International Journal of Modeling andOptimization, Vol. 1, No. 3, Aug 2011

[13] Optimization of GMAW Protocols andParameters for Improving Weld Strength

Quality Applying the Taguchi Method - JosephI. Achebo Proceedings of the World Congresson Engineering 2011 Vol I WCE 2011, July 6 -8, 2011, London, U.K.