predicting weld bead geometry in the novel cwgmaw process · 2016-01-13 · predicting weld bead...

Introduction One of the main issues concerningthe conventional arc welding processesis the fact that to improve their pro-ductivity and deposition rate, thewelding current imposed on the basemetal must increase, then subjectingthe workpiece to higher temperaturesthat cause distortions and oblige thewelded product to be reworked to re-move the distortions occasioned by

the excessive heat input. This increas-es the postprocessing total costs. To increase the deposition withoutsubjecting the workpiece to excessiveheat inputs or decrease the heat inputwithout compromising depositionrates, modifications on conventionalgas metal arc welding (GMAW) wereproposed as alternative processes. With the purpose of increasing dep-osition, some modifications of conven-tional GMAW were created such as

tandem GMAW (Ref. 1) and variablepolarity GMAW (VP–GMAW) (Ref. 2). Tandem GMAW consists of twowelding guns united into one usingtwo energized wires. One particularfeature of the tandem process is thatthe maximum current could be select-ed to one particular welding gun. Inaddition, VP–GMAW presents a varia-tion of polarity of the electrode that isstill melted when positive, but duringthe negative polarity phase can bemelted more rapidly, increasing deposition. Those processes achieve the aim ofincreasing the productivity through anincrease in the deposition rate. How-ever, they offer increased amounts ofheat input to the base metal. In applications such as weldinghigh-strength low-alloy (HSLA) steels,pipeline steels for example, thoseprocesses are not suitable becausehigh heat inputs deteriorated the finestructure of the base metal in theheat-affected zone (HAZ) and there-fore make this region the weakest inthe structure and likely to be the pointwhere the failure of the structure willtake place. To overcome this issue of increasingthe deposition rate without increasingthe heat input to the workpiece, therewere technology developments. Oneof those welding processes recently de-veloped to control the heat input onthe workpiece while not compromisingthe deposition is double-electrodeGMAW (DE-GMAW), a variant of con-ventional GMAW. This process was

WELDING RESEARCH

Predicting Weld Bead Geometry in the Novel CWGMAW Process

The regressive analysis showed that dilution and penetration are negatively correlatedwith the welding current in Cold Wire GMAW

BY R. A. RIBEIRO, E. B. F. SANTOS, P. D. C. ASSUNÇÃO, R. R. MACIEL, AND E. M. BRAGA

ABSTRACT One of the major concerns in welding is achieving an adequate weld bead with ageometry that is capable of resisting imposed stresses. Hence, predicting the geometrical features of a new welding process is of fundamental importance. Statisticalmethods are the most commonly used for predicting such geometries, and they provide particular confidence regarding the accuracy of the obtained regression. In thisstudy, the geometries of weld beads produced using cold wire gas metal arc welding(CWGMAW) were predicted using regressive and sensitivity analyses. This newprocess aims to increase the productivity of GMAW through the addition of a coldwire in the arc region, thereby increasing the deposition rate. The influence of thecold wire addition on the final weld bead geometry was investigated using curvilinearregressive and sensitivity analyses. The inputs considered in this study were the electrode feed rate, electric current, and nonelectrode feed rate ratio. The measuredgeometric parameters of the resulting weld were the bead penetration, bead width,bead height, and dilution. The results demonstrated that the regressive analysis couldpredict the geometry of the weld bead with good accuracy, with an error of less than15%, and the sensitivity analysis indicated that dilution and penetration have a negative sensitivity to the welding current in CWGMAW.

KEYWORDS • Geometry • Cold Wire Gas Metal Arc Welding (CWGMAW) • Regressive Analysis • Sensitivity Analysis • Analysis of Variance (ANOVA)

R. A. RIBEIRO is a graduate student, E. B. F. SANTOS ([email protected]) is an undergraduate student, P. D. C. ASSUNÇÃO is a researcher, R. R. MACIELis an undergraduate student, and E. M. BRAGA is an associate professor at the Faculty of Mechanical Engineering (FEM), Federal University of Pará (UFPA),Belém do Pará, Brazil.

SEPTEMBER 2015 / WELDING JOURNAL 301-s

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originally developed at the Universityof Kentucky (Ref. 3) and subsequentlystudied in a series of papers (Refs.4–8). Double-electrode GMAW was origi-nally developed using a nonconsum-able gas tungsten arc welding (GTAW)second torch as a bypass torch, callednonconsumable DE-GMAW. In thissystem, the existence of the currentbypass allows control of the heat inputon the base metal used in welding op-erations because the current flowingthrough the base metal is decoupledinto the base metal current (Ibm) andbypass current (Ibp), which implies thatthe base metal current is less than the

melting current (total current, I = Ibm +Ibp). Decreasing the heat input withouta reduction in the deposition changesthe former features of conventionalGMAW. Knowing that much of the heatgenerated by the electric arc is lost, asecond variant of DE-GMAW with aconsumable electrode (Refs. 5, 6) wasproposed to increase even more depo-sition while the heat input is con-trolled by decoupling the melting cur-rent, as mentioned earlier. In this ver-sion, the second bypass torch, origi-nally GTAW (Refs. 3, 4), is replaced bya GMAW gun with a consumable elec-trode (Refs. 5, 6).

More recently, another modifica-tion of the conventional weldingprocesses was proposed and developedby ESAB. ICE technology (Ref. 9), ap-plied to submerged arc welding (SAW),conjugates three wires, two energizedand one nonenergized, making possi-ble the deposition increase without increasing the heat input in the workpiece. Following the efforts to increaseproductivity of the welding process(deposition rates) without increasingthe heat input into the base metal, theLaboratory of Materials Characteriza-tion (LCAM) from the Federal Univer-sity of Pará (UFPA) developed cold

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WELDING JOURNAL / SEPTEMBER 2015, VOL. 94302-s

Fig. 1 — Detail of the cold wire addition in the arc regionfor CWGMAW.

Fig. 2 — Angle configuration for the nonelectrode wire feedingsystem for CWGMAW.

Fig. 4 — General scheme for CWGMAW.Fig. 3 — Weld bead geometric characteristics.

Table 1 — Nominal Chemical Compositions of the Base Metal and Wires

Material Elements (wt%)C Si Mn P S Cr Mo Ni Cu

ASTM A131A 0.21 0.50 2.5C 0.035 0.035 0.02 0.02 0.02 0.02AWS ER70S6 0.06–0.15 0.80–1.50 1.40–1.85 0.025 0.035 0.15 0.15 0.15 0.50

Single values are the maximum levels, and the copper content in the welding consumable is due to the coating on the electrode plus the copper present in the filler metal itself(Refs. 12, 13).


wire GMAW (CW–GMAW). Since the heat generated by theelectric arc in conventional GMAW isused to melt the electrode and the restof it is wasted, a nonelectrode consum-able wire (cold wire) was introducedinto the arc, increasing the depositionregarding conventional GMAW. By theintroduction of this cold wire, theoriginal GMAW is changed into a newprocess, presenting intrinsic featuressuch as different arc stability dynamicsand, no less important, the effect ofthis cold wire on the geometry of weldbeads. The configuration for the intro-duction of the cold wire in the regionof the arc in CW-GMAW is illustratedin Figs. 1 and 2. Regarding the final geometry ofweld beads performed by CW-GMAW,it is necessary to understand how theamount of cold wire affects the geo-metric bead characteristics such as

bead width,height, pen-etration, anddilution.Further-more, it isnecessary to investigate whether thosegeometric features are influenced inthe same way by welding parameterssuch as welding current, voltage, travelspeed, and feeding speed when com-pared to conventional GMAW. Only once the input parameter’s in-fluence on the final weld geometry iscompletely understood can a model topredict the geometry be built, and theprocess can be applied in a fully auto-mated setup in manufacturing indus-tries such as shipbuilding. Therefore, itis necessary to complete a modelingtreatment to build a model that can pre-dict the final geometry of weld beadsgenerated by this process, which in turn

will enable an alternative method thatallies all the strong features of conven-tional GMAW to an increased deposi-tion with fairly the same apparatus ofGMAW.

Weld Bead GeometricParameters and TheirPrediction

An important consideration inwelding is to achieve an adequate weldbead with a geometry that is capableof resisting imposed stresses. Geomet-ric characteristics such as the width(W), height (H), and penetration (P),as shown in Fig. 3, as well as the dilu-tion (D), as shown in Equation 1, arefundamental for assessing the qualityof a weld bead. In general, these characteristics arefunctions of the input parameters,such as current and voltage, amongothers. Hence, predicting and under-standing how the input parameters in-fluence these characteristics is of fun-damental importance for a new weld-ing process. Penetration refers to howthe bead penetrated into the originalbase metal. In this paper, bead-on-plate welds were performed to prima-rily assess the geometric characteris-tics and weld cladding quality. The width is a measure of howmuch the bead spreads over the plate,height refers to how much metal isabove the plate surface reference line,

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Fig. 5 — Experimental setup used to perform the experiments in the present work.

Fig. 6 — Fluxogram of the parameters used in the experiments.

Table 2 — Experimental Input Geometric Parameters Used to Perform the Curvilinear Regressive Analysis

Specimen Electrode Current Nonelectrode Width Height Penetration DilutionNumber Feed Rate (A) Feed Rate Ratio (mm) (mm) (mm) (%)

(m/min) (%)

1 10 300 20 15.0 3.43 2.49 40.282 10 300 40 14.5 3.56 3.31 46.883 10 300 60 14.2 3.84 2.99 40.284 10 292 80 13.5 4.36 2.50 31.315 10 285 100 13.3 4.66 2.21 28.876 12 335 20 16.2 3.46 3.93 47.697 12 340 40 15.7 3.87 3.86 43.918 12 340 60 15.5 4.12 3.17 39.429 12 360 80 15.9 4.80 2.97 29.83

10 12 340 100 15.5 4.96 3.06 25.7311 14 356 20 15.7 3.96 5.90 48.1012 14 370 40 16.4 4.53 6.28 44.7513 14 370 60 16.6 4.79 3.58 35.2714 14 380 80 16.3 5.03 3.78 34.3015 14 375 100 16.2 5.30 2.91 28.04

Fig. 7 — Macrographs of weld bead cross sections.

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and dilution corresponds to the quan-tity of base metal incorporated intothe weld bead. Dilution is mathemati-cally defined in Equation 1. The math-ematical definition of dilution (%)measures the amount of the weld beadcross-sectional area that came fromthe base metal; in other words, thequantity of the base metal that was in-corporated into the weld bead. The pa-rameters A and B are shown in Fig. 3.

Statistical methods are the mostcommonly used approaches for pre-dicting such geometries, and they pro-vide particular confidence regardingthe regression accuracy. However, theefforts to model weld bead geometryare not confined to the use of statisti-cal techniques. The use of dimensionalanalysis is also employed with such intention. Reference 10 represents an ap-proach to model penetration using di-mensionless numbers, based on a gen-eral assumption that it can be relatedto heat and mass transfer to the weldpool. Reference 11 represents thesame dimensionless approach to mod-el experimental data to determine ana-lytical relations to determine the sta-ble ranges of welding parameters tocontrol weld bead geometry. Kim (Ref. 12) performed a multi-variate regression analysis (MRA) us-ing the least squares method to esti-mate the regressors for identifyingwhich parameters influence the beadpenetration on a 12-mm SS400 plateusing GMAW. These authors conclud-ed that current, voltage, weldingspeed, and welding angle affect thepenetration based on the comparisonof two regression models, linear andcurvilinear. In another study, Kim (Ref. 13) de-termined the relationships of penetra-tion, width, and height with all of theaforementioned variables except thewelding angle. Furthermore, a sensi-tivity analysis was conducted to under-stand how a discrete change in thevariables would discretely affect theoutput variables. The authors conclud-ed that width and height were moreaffected than penetration by changesin the input variables during conven-

tional GMAW. Sangül (Ref. 14) stated that thesensitivity analysis is a very useful tooldue to its simplicity in implementingall mathematical models and the inclu-sion of information about cross corre-lations between the process parame-ters. Therefore, considering that weld-ing is highly dependent on the inputvariables and the increasing industrialneed to implement new methods thatcombine adequate economic advan-tages in automated versions, this sta-tistical analysis can guarantee that theresulting geometry would be in accor-dance with the design requirements. Furthermore, in an optimizationprocess, this sensitivity analysis canindicate which variable has the great-est influence on the objective parame-ters, as well as the variables that arenot affected by the input variables,thereby providing a very clear indica-tion of what must be performed to ob-

tain a specific geometry for the manu-facturing requirements. Additionally,this sensitivity analysis also providesan indication of how much the objec-tive variable is affected, in contrast tothe one-factor-at-a-time approach,which consists of incrementally in-creasing the input variables to deter-mine their effects on the objective out-put functions (Ref. 15). Palani (Ref. 16) developed a modelfor predicting the weld bead geometryin cladding produced using flux coredarc welding (FCAW). In another study,Karaoğlu (Ref. 17) performed thesame sensitivity analysis for SAW andconcluded that the width of the weldbead is very sensitive to the appliedcurrent, voltage, and welding speed;the height is primarily affected by thevoltage; and the penetration is prima-rily affected by current. “The sensitivity information shouldbe interpreted using mathematical defi-

DB

A B×=

+100 (1)

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Fig. 8 — Normal distribution of the residuals. A — Width; B — height; C — penetration;and D — dilution.

Table 3 — Regression Parameters and ANOVA Responses for the Models

Geometry Feature R2 RSS MRS Significance F

Width 0.9453 0.0043 0.0003 5.08 10–7

Height 0.9329 0.0192 0.0017 9.62 10–7

Penetration 0.7202 0.3311 0.0301 2.23 10–3

Dilution 0.7377 0.1611 0.0149 1.58 10–3

A B

DC


nition of derivatives. Namely, positivesensitivity values imply an increment inthe objective function by a small changein design parameter, whereas negativevalues state the opposite” (Ref. 17).Therefore, a positive sensitivity valueindicates that the dependent parame-ters increase as the independent param-eters increase, whereas a negative sensi-tivity value indicates that the depend-ent variables decrease as the independ-ent variables increase. Furthermore, Saltelli (Ref. 18) stat-ed the sensitivity analysis can be de-

fined as “the study of how the varia-tion in the output of a model (numeri-cal or otherwise) can be apportioned,qualitatively or quantitatively, to dif-ferent sources of variation.” It can alsobe inferred from Ref. 17 that a param-eter is said to be nonsensitive whenthe absolute value of its sensitivity isless than 0.2. Luo (Ref. 19) utilized regressionanalysis to predict and analyze the fea-tures of resistance spot welding (RSW)on galvanized steel sheets. Huang(Ref. 20) used the neural network and

multiple regression methods to char-acterize and study the relationship be-tween the weld penetration depth andacoustic signal acquired during laserwelding (LW) of high-strength steels. Models to predict the geometry ofbead performed by GTAW have beeninvestigated as well by several ap-proaches. Zhang et al. (Ref. 21) used avisual identification procedure to ob-tain images of the weld pool in GTAW,then reconstructed these images usingimage processing algorithms to obtaina 3D image of it. After image process-ing, through experimentation, the fol-lowing input parameters were select-ed: width, length, and convexity of thewelding pool. Then, a least-squaremethod was applied to establish re-gression equations between the back-side bead width, which is proportionalto the penetration of the weld bead,and the selected parameters as input,taken independently and in a com-bined way. Liu et al. (Ref. 22) used the same vi-sual acquisition system to generate a 3Dimage of the weld pools in GTAW andthen correlate backside width to correctpredicted penetration, but instead ofusing a least-square method to calculatethe equations, it uses a neuro-fuzzy in-ference system (ANFIS) due the nonlin-earity of the phenomenon studied. Liu et al. (Ref. 23) also modeled theGTA weld pool to predict its width,length, and convexity to obtain equa-tions to predict these parameters tak-ing into account the welding currentand speed as characteristic parame-ters. A predictive control algorithm isdeveloped to control the aforemen-tioned features of the weld pool, whichdoes not need online optimization. More recently, Shi (Ref. 24) em-ployed the multiple curvilinear regres-sion method and sensitivity analysisto investigate the influence of weldingparameters on the geometry of the re-sulting weld bead in underwater wetflux cored arc welding (FCAW). Xiong (Ref. 25) used a statistical ap-proach consisting of a neural networkmodel and a second-order regressionanalysis to predict the bead geometryfor robotic GMAW-based rapid manu-facturing. Although MRA and sensitivity analy-sis have long been used for predictingthe weld bead geometries of severalwelding processes, such as GMAW

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Fig. 9 — Values estimated by the curvilinear regression model plotted against the measuredvalues. A — Width; B — height; C — penetration; and D — dilution.

Table 4 — Geometric Parameters Calculated Using the Curvilinear Regressive Model

Specimen Bead Bead BeadNumber Width Height Penetration Dilution

(mm) (mm) (mm) (%)

1 14.77 3.21 3.29 49.262 14.42 3.69 2.80 39.793 14.21 4.01 2.55 35.124 13.69 4.29 2.44 32.185 13.25 4.53 2.37 30.086 15.81 3.54 4.30 49.587 15.66 4.05 3.61 40.028 15.44 4.40 3.29 35.329 16.21 4.56 2.92 32.23

10 15.17 4.88 2.92 30.1811 16.19 3.90 5.56 49.9312 16.44 4.42 4.57 40.2513 16.21 4.80 4.16 35.5314 16.49 5.04 3.79 32.4715 16.14 5.30 3.65 30.34

A B

DC


(Refs. 12, 13, 25), SAW (Ref. 15), LW(Ref. 20), RSW (Ref. 19), GTAW (Refs.21–23), and FCAW (Ref. 24), there areno reports in the literature where thesemethods were applied for predicting thegeometric features of weld beads pro-duced using CW-GMAW. Therefore, following the aforemen-tioned studies, in the current study,MRA was applied to CW-GMAW to es-tablish curvilinear regressions that canrelate the weld bead geometry to the

independent variables of the process.Furthermore, a sensitivity analysiswas performed to determine how thedependent variables react to discretechanges in the independent variables. Analysis of variance (ANOVA) wasperformed to assess the quality of thecalculated models, determine how wellthe models can explain the originaldataset, and determine how the resid-uals were calculated using the em-ployed method.

Experimental Procedures

Cold Wire Gas Metal ArcWelding (CWGMAW)

The CW-GMAW process essentiallyconsists of introducing a nonelectrodewire into the electric arc region, whichis subsequently melted and depositedon the base metal. According to Ref.26, the primary advantages of CW-GMAW are that it has all the advan-tages of GMAW plus a cooler weldpool, it can be utilized in all weldingpositions, the additional wire can beconventional or flux-cored, and it canachieve good weld bead quality andhigh deposition rates. The general scheme for CW-GMAWis illustrated in Fig. 4. This process consists of the follow-ing input variables: welding speed,shielding gas flow rate, contact tip-to-workpiece distance, plus current, volt-age, and feed rate of the electrode andcold wire. However, in this study, onlythe electrode feed rate, current, andnonelectrode feed rate ratio will beconsidered as input variables. The coldwire feed rate ratio is a percentage ofthe electrode feed rate, and it is calledthe nonelectrode feed rate ratio (%),defined as follows:

where Ws is the cold wire feed rate inm/min and E is the electrode feed ratein m/min. These variables and the arccurrent in amperes (A) play a majorrole in the geometry of the resultingweld. Still, regarding the process, it isworthy to mention that wires can bedifferent in diameter, and the coldwire is thinner than the hot wire. Asthe cold wire is melted by the heat thatotherwise would be lost and/or trans-mitted to the base metal, it is believedthat having a minor diameter wouldimprove the melting, while having alarger diameter would decrease theheat input to the workpiece. An interesting feature of CW-GMAW is that the process just needsan independent wire feeder to injectthe cold wire into the arc, so the weld-ing apparatus is slightly conventionaland does not represent a huge impact

RWEs ×= 100 (2)

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Fig. 11 — Accuracy analysis of mathematical model equation per class of percent relativeerror.

Table 5 — Measured Geometrical Features for the Verification Experiments

Verification E I R Measured Measured Measured MeasuredExperiment (m/min) (A) (%) Width Height Penetration Dilution

(mm) (mm) (mm) (%)

1 10 300 60 15.55 3.94 4.21 42.772 14 365 40 17.13 4.29 5.78 46.443 14 377 40 17.11 4.49 5.86 46.894 14 380 60 17.97 4.61 5.34 47.635 12 340 100 17.64 5.50 2.56 32.576 12 355 60 17.66 4.31 4.16 47.257 10 329 40 16.78 3.52 3.10 31.958 12 326 100 16.52 3.88 5.10 54.119 14 342 20 17.02 4.01 6.57 53.93

10 12 339 20 16.20 4.65 3.15 39.59

Fig. 10 — Percent relative errors for the objective variables.


on costs regarding the welding systemconfiguration. Figure 5 illustrates the actual CW-GMAW experimental setup used toperform the experiments for the pres-ent study.

Equipment and Materials

In this study, a Digiplus A7 elec-tronic welding power supply operated

at a current of 400 A was used to pro-duce the weld bead. The electrodefeeder was an IMC STA-20D, and anadditional wire feeder (ESAB MEF 30)was used to feed the cold wire. Theemployed welding gun was an auto-mated, water-cooled GMAW TBi 511with a maximum current of 400 A.The shielding gas was a mixture of25% CO2 and 75% argon (Ar) with aconstant volumetric flow rate of 15

L/min. The base metal used in this workwas a normalized structural steel withan ASTM A131 grade A classification,which is normally used in ship con-struction. This steel possessed a lowlevel of carbon, and its chemical com-position is shown in Table 1 (Ref. 27).The wires had an AWS ER70S-6 classi-fication, and two different diametersof wires were used. The electrode wirehad a diameter of 1.2 mm, and thenonelectrode wire had a diameter of1.0 mm. The chemical composition ofthe wires is shown in Table 1 (Ref. 28).

Experimental Procedure

The experiments were conductedusing an average constant voltage of37 V; electrode feed rates of 10, 12,and 14 m/min; and nonelectrode rateratios of 20, 40, 60, 80, and 100%, asshown in the fluxogram presented inFig. 6. Average current levels of 300,340, and 380 A were used during theexperiments. It was performed as 15experimental runs with one replicatefor each, totaling 30 experiments. The welding position was flat (1G)with automatic horizontal displace-ment in the wire feed direction, whichguaranteed the injection of wire infront of the electric arc and weldingpool. The angle between the weldinggun and cold wire feeder tip was =61 deg, and the work angle and the an-gle of attack were 90 deg, as measuredrelative to the metal base plate; thisconfiguration is illustrated in Fig. 2. To measure the output variables,transverse cross sections were createdin every weld using a Powermaq BS-912B hacksaw from the mid-length ofthe welds. The cross sections were pol-ished and etched with 6% Nital tomeasure the width, height, penetra-tion, and dilution. Note that dilutionis an important parameter in deposi-tion welding because a coating withexcessive dilution could alter thechemical composition of the depositedmetal and consequently change its me-chanical behavior during service. Therefore, accurately predicting thedilution of a weld bead produced usingCW-GMAW is fundamental for guar-anteeing the quality of the process.Hence, this paper provides a regres-sion for predicting dilution. The influences of the volumetric

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Fig. 12 — Sensitivity of bead width to the following: A — Electrode feed rate; B — current; and C — nonelectrode feed rate ratio.

Fig. 13 — Sensitivity of bead height to the following: A — Electrode feed rate; B — current;and C — nonelectrode feed rate ratio.

Table 6 — Residuals Between the Measured Values from the Verification Experimental Runs and the Predicted Values

Residuals Residuals Residuals Residuals Width (mm) Height (mm) Penetration (mm) Dilution (%)

1.34 –0.07 1.53 8.670.92 –0.15 0.87 3.900.35 0.10 1.10 3.971.31 –0.14 1.04 8.512.47 0.62 –0.53 13.531.52 –0.02 0.82 8.650.94 –0.04 0.39 4.341.99 –1.08 1.89 13.471.48 0.05 0.45 –5.430.20 1.13 –1.35 –5.09

A

A

B

B

C

C


flow rate, welding speed, and weldingangle were not investigated in thisstudy and thus maintained constantduring the experiments.

Mathematical Model

Chandel (Ref. 29) was the first topropose such mathematical models forinvestigating the relationships be-tween the bead geometry and processparameters for GMAW. Regarding thecurvilinear model, according to Mc-Glone (Ref. 30) and Kim (Refs. 12, 13),the relationship between the inputand the output variables follows a rela-tion such as that in Equation 3.

where f might be any of the outputweld bead geometric parameters men-tioned earlier, I is the current in am-peres (A), E is the electrode feed ratein (m/min), R is the nonelectrode(wire) feed rate ratio in (%), as defined

by Equation 1 in the introduction, andb1, b2, b3, and b4 are the regressor coef-ficients to be estimated by the regres-sive method. Equation 3 can be written as follows:

Equation 4 is linear and can be rewritten as follows:

where F = ln(f) and K is the natural logof the input variables. The b coefficientscan ultimately be estimated using a lin-ear regressive method. In the presentwork, Microsoft Excel software wasused to perform this analysis. ANOVA was also performed to determine how realistic the models

are, and similar to the regressive rela-tions, ANOVA was performed usingMicrosoft Excel.

Results and Discussion Multivariate regressive curvilinearanalysis was performed based on theresults shown in Table 2. Figure 7 shows the macrograph forall the samples from where the meas-urements presented in Table 2 weretaken. Applying the above-described pro-cedure yielded the following equationsfor predicting the geometric featuresof the weld bead:

Table 3 presents the statistical out-put indices from the regression analy-sis and ANOVA. The RSS is the sum ofthe squares of the residuals and indi-cates a better fit when it is closer to 0.The MRS is the mean root square ofthe residuals and indicates the modelis good as it approaches 0. The signifi-cance F value is an indication of howstrong the regression is, and a smallsignificance F value confirms the valid-ity of the regression output. As shown in Table 3, although theR2 values for penetration and dilutionare less than 0.90, the significance Fvalues for these two parameters arestill small, which indicates a meaning-ful correlation. Furthermore, according to Mont-gomery (Ref. 15), it is better to checkthe model adequacy based on theanalysis of variance only after exam-

F b b K I

b K E b K R

= + ( )

+ ( ) + ( ) (5)

1 2

3 4

PE

I R

Penetration (P) [mm]:

= 11.6279 (6)2.0247

0.9166 0.2324

WI

E R

Width (W) [mm]:

= 0.0847 (7)1.0230

0.2472 0.0349

HI

E R

−

−

Height (H) [mm]:

= 2.6191 (8)0.3840

0.7756 0.2026

DI

E R

−

−

Dilution (D) [%]:

= 140.8586 (9)0.0486

0.0486 0.3061

ln f b b ln I

b ln E b ln R

( ) = + ( )

+ ( ) + ( ) (4)

1 2

3 4

f b R E Ib b b= (3)14 3 2

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Fig. 14 — Sensitivity of penetration to the following: A — Electrode feed rate; B — current; and C — nonelectrode feed rate ratio.

Fig. 15 — Sensitivity of dilution to the following: A — Electrode feed rate; B — current;and C — nonelectrode feed rate ratio.

A

A

B

B

C

C


ining the residuals, which should notcontain an obvious pattern in theirdistribution; they should be struc-tureless. One approach to examiningthe residuals is to assume the errorsare normally and independently dis-tributed with a mean equal to zeroand a constant but unknown vari-ance. To verify this assumption, plot-ting the residuals in a normal proba-bility chart with a mean equal to 0and unknown variance is a usefulprocedure. If this assumption of theresidual distribution is true, this plotwill resemble a straight line. Thisprocedure was employed, and the re-sults are shown in Fig. 8. Table 4 presents the values of theoutput geometric parameters calculat-ed using Equations 6–9. In Fig. 9, the calculated values forbead width, height, penetration, anddilution are plotted against the meas-ured values. The percent relative error is definedaccording to Equation 10, where Mv

represents the measured values and Cv

represents the calculated values.

Figure 10 presents the percent rela-tive errors for each geometric featurepredicted for each specimen. Note thattaking the number of specimens andtheir errors into account, few speci-mens had a nonnegligible error ofmore than 15%, considering that, aspreviously stated, experiment designmethods were not used in this work.Therefore, it is possible to concludefrom the data in Table 3, as well asFigs. 8–10, that the model can predictthe weld bead geometry with a con-trolled error for the engineering re-quirements (e < 15%). Note that in thepresent study, a full factorial designwas not conducted, which will be thefocus of a future study because it willhelp to fully understand the relation-ships between the input variables fordetermining the output weld beadgeometry in CW-GMAW. Kim (Ref. 13) presented a chartsimilar to that shown in Fig. 11, inwhich it was possible to observe thatthe vast majority of this predicted geo-metric output features had a maximalerror of 15%.

The results of the present work aresimilar to those obtained by Ref. 13,and the qualities of the regressionspresented here are also similar tothose found in Ref. 13, consideringthat the lowest R2 presented for a giv-en regression is 72.02%, which is stillhigher than the value of 70.38% re-ported by Kim (Ref. 13). This indicatesthat the model proposed in this workcould be employed for predicting thegeometries of weld beads with rela-tively good accuracy. To assess the model, 10 new weldswere performed. The welding sequenceof the specimens as well as the parame-ters for each bead were determined ran-domly using the software Minitab®.The welds were performed using inputparameters in the range of the originalones used in the experiment for theconstruction of the model. The reason why 10 welds were donewas that 10 corresponds to 2/3 of the15 original welds. The authors consid-ered that if the statistical model canpredict 67% of the original data, whichis seen as a random variable normallydistributed, the model can be calledsatisfactory. Table 5 presents the measured val-ues from the model verification exper-imental runs. Table 6 shows the residual, for eachgeometric feature investigated, be-tween the measured and predicted val-ue of width, height, penetration, anddilution. Although there are points ofhigh relative residual error, this doesnot invalidate the model, because inthe majority of the weld’s geometrywas cases predicted with regular accuracy.

Sensitivity Analysis

Using Equations 6–9, sensitivityanalyses were performed for the inde-pendent process variables investigatedin this study. To calculate these rela-tions, Equations 6–9 were differentiat-ed with respect to the independentprocess variables. The sensitivity equations for widthare as follows:

The sensitivity equations for heightare as follows:

The sensitivity equations for pene-tration are as follows:

The sensitivity equations for dilu-tion are as follows:

Figure 12 shows the sensitivity ofthe bead width to each of the input pa-

dWdE

I

E R

−=

0.0209(11)

1.023

1.2472 0.0349

dWdI

I

E R=

0.0866(12)

0.023

0.2472 0.0349

dWdR

I

E R

−=

0.0029(13)

1.023

0.2472 1.0349

dHdE

R

E I=

2.0313(14)

0.2026

0.2244 0.3840

dHdI

. E

I R

.

. .= −−

1 0057(15)

0 7756

1 3840 0 2026

dHdRE

I R=

0.5306(16)

0.7756

1.3840 0.7974

dPdE

E

I R=

23.543(17)

1.027

0.9166 0.2324

dPdI

E

I R

−=

10.6581(18)

2.0247

1.9166 0.2324

dPdR

E

I R

−=

2.7023(19)

2.0247

0.9166 1.2324

dDdE

E

I R=

9.12763(20)

-0.9352

0.0.486 0.3081

dDdI

E

I R .−

=6.8457

(21)0.0648

1.0486 0 3081

dDdR

. E

I R

.= −43 3985

(22)0 0648

0.0486 1.3081

eM CMv v

v

−×= 100 (10)

WELDING RESEARCH



rameters, i.e., E, I, and R. As shown in Fig. 12A, the width hasa negative sensitivity to E, and it is ap-proximately constant for each value ofR but decreases with increasing current. Figure 12B shows the sensitivity ofthe bead width to current, which ispositive, indicating that an increase incurrent increases W, but it is approxi-mately constant for variations in R. Figure 12C shows the sensitivity ofthe bead width to R, which is negative,indicating that an increase in R will de-crease W. In addition, the sensitivitydecreases as R increases. However, ac-cording to the criterion establishedabove, the sensitivity is negligiblewhen its absolute value is less than0.2. Therefore, the width is said to benonsensitive to I and R. Figure 13 shows the sensitivity ofthe bead height to E, R, and I. Figure 13A shows the sensitivity ofthe bead height to E, which is positive,and it increases as R increases but decreases as the average current increases. Figure 13B shows the sensitivity ofthe bead height to I, which is negativeand practically equal to that of R. Figure 13C shows the sensitivity ofthe bead height to R, which is positiveas R decreases but practically constantwith different levels of E and averagecurrent. The absolute values of thesensitivity of the bead height to I andR are small, < 0.2, which can be consid-ered nonsensitive. Figure 14 shows the sensitivities of penetration to each of the inputvariables. Figure 14A shows that penetrationis more sensitive to E than to R and I,indicating that I and R practically donot affect penetration. Figure 14B shows the sensitivity ofpenetration to current I, which is prac-tically constant and equal to zero. Figure 14C shows the sensitivity ofpenetration to R, and it can be ob-served that penetration is more sensi-tive to the lowest value of R; further-more, this sensitivity is negative, indi-cating that P decreases as R increases. Figure 15 shows the sensitivity ofdilution to E, I, and R. Figure 15A shows the sensitivity ofdilution to E, which is positive and de-creases as R and E increase. It can beobserved that dilution is more sensi-

tive to lower values of E and R. Figure 15B shows that the sensitivi-ty of dilution to current is negative,this negative sensitivity increases as Rincreases, and dilution is more sensi-tive to higher values of R and to lowervalues of E. The sensitivity of dilution to R isshown in Fig. 15C and is negative,which indicates a negative relationamong dilution and R that diminishesas R increases, indicating that the sen-sitivity of dilution to R is higher forlower values of R and is approximatelynonsensitive after a certain value of R.It can also be observed that the sensi-tivity of dilution to R is almost not in-fluenced by the value of E. As previously mentioned, in CW-GMAW, dilution has a negative sensi-tivity to current, as shown in Fig. 15B.This is a characteristic of CW-GMAW.Note that this increase in current isused to melt the extra wire being fedrather than to provide more heat di-rectly to the base metal. Furthermore,because the power supply used was aconstant voltage machine and due tothe dynamic control of arc length, theintroduction of the second wire in theelectric arc region shortened the arclength and decreased its voltage.Therefore, the power supply was re-quired to increase the current to estab-lish the previously set welding voltage.

Conclusions In this study, the input variablescurrent, electrode feed rate, and non-electrode (wire) feed rate ratio were in-vestigated to establish a regression be-tween them that could explain or pre-dict the geometric features of a weldbead. The regressions in this study result-ed in good accuracy between the calcu-lated data and measured data, and theregressions were submitted to a sensi-tivity analysis to determine how theobjective functions are influenced bythe independent variables. The errors of the regressions usedin this study were less than 15% forthe majority of the specimens, indicat-ing that the results obtained in thepresent study are trustworthy and canbe employed in engineering applica-tions to predict the geometries of weldbeads. The relationship between the pene-

tration and electrode feed rate showeda positive sensitivity, which decreasedwith the increasing nonelectrode ratio. Regarding CW-GMAW, it was foundthat the current has an inverse rela-tionship to the penetration, whichcould be explained by the introductionof another wire that must be heated tobe deposited on the plate, thereby tak-ing heat from the electric arc, acting asa heat sink, and consequently decreas-ing the energy transferred to the basemetal compared with that in conven-tional GMAW. There are sensitivities, such as thewidth to E (dW/dE), width to I(dW/dI), height to I (dH/dI), and dilu-tion to E (dD/dE), that are minimallyinfluenced by the values of R, remain-ing essentially constant for the rangeof R investigated in this study. According to the criterion inferredfrom Ref. 17, the sensitivity is negligi-ble if its absolute value is less than 0.2.The width, height, and penetration areonly sensitive to the electrode feedrate (E), whereas dilution is sensitiveto all three input variables investigat-ed in this study. Dilution is most influ-enced by the welding current and non-electrode feed rate ratio (R). The novelty of this study is the ap-plication of already consolidated sta-tistical methods used to predict the re-sulting weld geometry of well-estab-lished welding processes to predict thegeometries of weld beads produced us-ing CW-GMAW, which is still underdevelopment. Thus, this study con-tributes to a better understanding ofhow the process variables influencethe resulting weld bead geometry. The results of the present study canbe used as a basis for optimizing thegeometries of weld beads produced us-ing CW-GMAW in a future study, con-sidering that one of the major con-cerns in industry is the optimizationof welding processes to obtain highproductivity, similar to how CW-GMAW achieves high productivitythrough a second wire being meltedand deposited. The analyses performed in thepresent work are also a necessarystep toward automation of CW-GMAW, as prediction is the first stepfor implementing a closed-loop feed-back system. In the present study, the influenceof parameters such as the type of gas

WELDING RESEARCH



and its volumetric flow rate, weldingspeed, welding angle, angle of injec-tion of the cold wire, and plate thick-ness were not included as variables inthe regressive model. Therefore, theyrepresent limitations of the study andshould be addressed in future studies. As the present study configures aninitial work on the prediction of CW-GMAW, the authors recognize that adeeper investigation on the predictionof the final geometry generated byCW-GMAW should be conducted forthe development of more precise pre-diction models. The new study shouldinclude variables such as weldingspeed and voltage, and employ the de-sign of experiments (DOE) technique.

This work was supported by theBrazilian Innovation Agency (FINEP),the National Counsel of Technologicaland Scientific Development (CNPq),and the Brazilian Ministry of Scienceand Technology through agreementnumber 01.11.0024.00. The authorswould like to thank the PROPESP/UFPA and FADESP for the financialsupport for the English editing serv-ice. The authors would also like to pro-vide special thanks to the team fromthe Laboratory of Materials Character-ization (LCAM) of the Federal Univer-sity of Pará (UFPA), where all the ex-perimental tests were performed.

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