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Journal of Materials Processing Technology 213 (2013) 1433– 1439

Contents lists available at SciVerse ScienceDirect

Journal of Materials Processing Technology

jou rna l h om epa g e: www.elsev ier .com/ locate / jmatprotec

oupled Eulerian Lagrangian finite element modeling of friction stir weldingrocesses

adi Al-Badour ∗, Nesar Merah, Abdelrahman Shuaib, Abdelaziz Bazouneechanical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

r t i c l e i n f o

rticle history:eceived 30 November 2012eceived in revised form 8 February 2013ccepted 25 February 2013

a b s t r a c t

A 3-dimensional localized finite element model (FEM) is developed to predict likely conditions thatresult in defect generation during friction stir welding (FSW). The workpiece is modeled using Eulerianformulation, while the tool is modeled using Lagrangian. Coulomb’s frictional contact model is adopted todefine the tool workpiece interaction, while the welding speed is defined by material inflow and outflow

vailable online 4 March 2013

eywords:riction stir weldinginite element modelingoupled Eulerian Lagrangian

velocities. The numerical results show that the coefficient of friction has a major effect on void formation;the lower the friction coefficient is applied, the larger the void is formed. Furthermore, welding usingforce control (FC) at lower welding speed results in smaller void size and wider plastic zone, leading tohigher quality weld.

© 2013 Elsevier B.V. All rights reserved.

oid formation

. Introduction

FSW problem is a multi-physics problem that includes excessiveaterial deformation and heat flow. Different modeling tech-

iques such as computational fluid dynamics (CFD) and arbitraryagrangian Eulerian formulation (ALE) have been used to simu-ate FSW process. Ulysse (2002) was the first to model FSW usingommercial CFD software FIDAP. He studied the effects of weld-ng and tool rotational speeds on temperature, loads on the toolin, and flow of particles near the rotating pin. Ignoring heat gen-rated by friction contact and heat loss to the backing plate, heound that the forces on the pin increase with increasing weldingpeed, and decrease with increasing rotational speed. Tempera-ures predictions were overestimated compared to experimentally

easured ones. Colegrove and Shercliff Hugh (2005) used FLU-NT (commercial CFD package) to study FSW process, consideringigid visco-plastic material behavior as in Ulysse (2002). But unlikelysse, they considered tool pin threads with full sticking condi-

ion, and included heat dissipation to backing plate. The authorsddressed a number of drawbacks in the model, such as the pre-icted size of the deformation zone which was found to be much

arger than observed experimentally, overestimation of the weldemperature, and underestimation of tool traversing force. Kim

t al. (2010) showed that the implementation of a proper thermaloundary condition at the interface between the workpiece and theacking plate is important for prediction of accurate results.

∗ Corresponding author. Tel.: +966 569163321; fax: +966 3 860 2949.E-mail addresses: fbadour@kfupm.edu.sa, fbodour@hotmail.com (F. Al-Badour).

924-0136/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.jmatprotec.2013.02.014

With the advancement in computational power, researchersalso used ALE re-meshing methodology with explicit solver to sim-ulate steady state FSW. Deng and Xu (2004) modeled FSW andsimulated the metal flow pattern around the FSW tool assum-ing plane strain conditions. The model considered experimentallymeasured temperatures which were applied as body loads. Theauthors used Abaqus Dynamic Explicit, to compare two toolpin-workpiece contact interaction models: modified Coulomb’sfrictional model and constant rate slip model. The comparison ofthe two models, based on tangential velocity, showed no largedifference between the two models. Schmidt and Hattel (2005)developed a localized thermo-mechanical model to study thesteady state FSW of 2xxx Aluminum alloy. Their model was gen-erated using commercial FEM package Abaqus, and solved usingcoupled temperature–displacement dynamic explicit where thematerial behavior was assumed to obey Johnson–Cook rule (1983).The results of their investigation shows that the cooling rate playsa significant role in defect formation with higher cooling rate lead-ing to faulty deposition of material behind the tool pin. Zhang et al.(2007), employed a model similar to that of Deng and Xu (2004),but considering 3D geometry. They found that the nugget zonewas much affected by the axial force than thermo-mechanical andheat affected zones. Zhang (2008) used both classical and modifiedCoulomb’s frictional model conditions. He found that both modelspredicted similar results at low tool rotational speeds. The classicalCoulomb’s model failed to work for higher tool rotational speeds

due to the increase in the dynamic effect of the welding tool. Later,Zhang and Zhang (2009) used an approach similar to that of Schmidtand Hattel (2005) to study the effect of welding parameters onmaterial flow and residual stresses in friction stir butt welded Al

1 ocessing Technology 213 (2013) 1433– 1439

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Table 1Mechanical and thermal properties of AL6061-T6.

T (◦C) E (GPa) � Cp (J kg−1 ◦C−1) � (kg m−3) ̨ (�m m−1 ◦C−1)

25 66.94 0.33 945 2690 23.5100 63.21 0.334 978 2690 24.6149 61.32 0.335 1000 2670 25.7204 56.8 0.336 1030 2660 26.6260 51.15 0.338 1052 2660 27.6316 47.17 0.36 1080 2630 28.5371 43.51 0.4 1100 2630 29.6427 28.77 0.41 1130 2600 30.7482 20.2 0.42 1276 – –

Table 2Johnson–Cook plasticity model constants for Al-6061-T6.

A (MPa) B (MPa) C n m Tref (◦C) Tmelt (◦C)

Zhang (2009). The domain thickness is kept equal to the plate thick-ness (6 mm) used in the experimental tests (Shuaib et al., 2012), andthe void region thickness is taken as 1 mm.

434 F. Al-Badour et al. / Journal of Materials Pr

061-T6. The material flow around the FSW tool was investigatedsing tracer nodes. The results showed that the increase of the rota-ional speed and the decrease of the welding speed can improve theriction stir weld quality, but flash formation would be more obvi-us when rotational speed is increased. Estimated residual stressesndicated that the longitudinal residual stresses are higher than theransverse ones.

From the above, it can be concluded that among the drawbacksn CFD simulations is its inability to include material hardenings it only considers rigid-viscoplastic material behavior. Contactonditions are usually assumed to be full sticking, leading to over-stimation of weld peak temperature and tool reaction loads.urthermore, CFD models ignored material’s elasticity. On the otherand, ALE technique can make use of sliding boundary conditionso define the tool pin workpiece interaction, assuming different val-es for the coefficient of friction or constant slip rate. ALE allowslso inclusion of material temperature and rate dependency as wells material hardening. But, in Lagrangian implementation or ALE,oid formation cannot be simulated because Lagrangian elementsave to be completely filled with material and defect formationimulation using ALE can only show a lack of material depositionehind the FSW tool pin.

In the current work, a coupled Eulerian Lagrangian (CEL) models developed using Abaqus (6.11-2, 2011) environment to simulatehe two phases of FSW process (plunging and welding). Eulerianlements which can include multi-materials, in addition to voidre employed in the FE model. The model is used to predict vol-metric defects and material flow during the FSW process and tostimate tool reaction loads. The investigation takes into accounthree parameters: (a) contact frictional coefficient �, (b) weldingpeed V, and (c) plunging control method (position vs. force). TheEM is validated using experimentally measured forces and torques well as matching the estimated processed zone shape and voidize with those obtained experimentally by Shuaib et al. (2012) andl-Badour (2012).

. Problem idealization and implementation

A localized three dimensional (CEL) FE model is developed andolved using Abaqus explicit to simulate void formation for givenrocess or welding conditions in addition to tool reaction forces,orques and the state of the processed material during and afterSW.

Simulation of welding phase is performed by employing a con-rol volume approach, whereas the welding speed is defined asnflow and outflow over Eulerian domain boundaries. In the FSWrocess, heat is generated due to frictional contact and plasticeformation. Only material softening due to inelastic heat genera-ion is considered. Due to limitations of the used version of Abaqus2011), adiabatic heating effect is assumed and heat dissipation intoorkpiece material or surrounding is not considered.

The material selected for this investigation is Al 6061-T6 wherehe relationship between flow stress �0, strain rate ε̇ and temper-ture T, is governed by Johnson–Cook’s semi-empirical formula:

0 = (A + Bε̄npl)

(1 + C ln

˙̄εpl

ε̇o

) (1 −

(T − Tref

Tmelt − Tref

)m)(1)

here ε̄pl is the effective plastic strain, ˙̄εpl the effective plastic strainate, ε̇0 the normalizing strain rate (typically 1.0 s−1) and A, B, C, n,nd m are material constants. The parameter n takes into account

he effect of strain hardening, the parameter m models the thermaloftening effect, and C represents strain rate sensitivity. Tref repre-ents the temperature where parameters A, B and n are evaluated,hile Tmelt is material solidus temperature.

324 114 0.002 0.42 1.34 24 583

Elastic and thermal properties are considered as temperaturedependent while 90% of plastic work is assumed to be convert-ing into heat. The thermal and mechanical properties as wellas Johnson–Cook parameters of Al-6061-T6 used in this model(Tables 1 and 2, respectively) can be found in (Soundararajan et al.,2005; Lesuer et al., 2001).

The AISI H13 FSW tool steel is modeled as rigid isothermalLagrangian body and is constrained to a reference point (RP)in order to assign tool physical properties such as; mass, massmoment of inertia, as well as to apply process conditions (tool rota-tional speed and plunging conditions). The FSW tool geometry anddimensions are given in Fig. 1, considering a flat shoulder tool witha cylindrical pin. Further details of the tool geometry and dimen-sions used in the experimental work can be found in Shuaib et al.(2012).

The Eulerian domain has been defined as a cuboidal shape thatincludes two regions: “full” and “void”. The lower region “full” isassigned with the workpiece material (Al-6061-T6), using uniformmaterial assignment tool representing a localized part of the work-piece. The upper region “void” is left with no material. The voidregion on the upper side of the workpiece is created to visualizeflash formation during welding process.

The dimensions of the Eulerian domain are considered based oninflow conditions, as stress free boundaries. Moreover, the domainsize has to compromise between accuracy and computational time.It is therefore considered to be four times the FSW tool shoul-der diameter; similar to Schmidt and Hattel (2005) and Zhang and

Fig. 1. FSW tool geometry and dimensions in mm.

F. Al-Badour et al. / Journal of Materials Processing Technology 213 (2013) 1433– 1439 1435

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heat effect are expected to lead to overestimations of reaction loads.Further validation is performed by comparing the equiva-

lent plastic strain zone at the conditions used above to themicrostructure of the processed zone obtained experimentally. The

Table 3Comparison between numerically estimated and experimentally measured toolloads during welding.

ig. 2. FSW geometric model and Eulerian domain (a) idealization (b) zoomed andectioned numerical meshed model with material assignment.

The Eulerian domain is meshed with 64,896 multi-materialhermally coupled 8-node (EC3D8RT) Eulerian elements. The ele-

ent has four degrees of freedom at each node. In order to avoidulerian material flowing through Lagrangian mesh (tool) and tonhance the computational efficiency, bias meshing technique haseen used to generate fine mesh at the tool-workpiece interactionone with coarse mesh at the sides. Fig. 2a and b illustrates the ide-lization of the proposed model and mesh and material assignment,espectively.

. Interaction, loads and boundary conditions

Eulerian body is coupled to Lagrangian through contact inter-ction. Tool-workpiece contact is defined using general contactormulation with Coulomb’s frictional law. Both modified and clas-ical forms of Coulomb’s law were tried. The classical model hashown better estimation of tool torque. Three different coefficientsf friction (� = 0.3, 0.58 and 0.8) were used to show their effectn void formation. These values represent the minimum, the esti-ated value based on torque measurement, and maximum. The

alues obtained respectively from references (Schmidt and Hattel,005; Al-Badour, 2012; Javadi and Tajdari, 2006).

Force control welding condition is simulated by applying a con-entrated force on the tool reference point, while for positionontrol displacement constraints are used. Because the Eulerianesh is rigid, velocity constraints at the boundaries are applied

n order to avoid material escaping from sides and bottom of theulerian domain, while welding speed is applied as inflow veloc-ty. Fig. 3 illustrates schematically the velocity boundary conditionssed in the present model.

. FEA validation

As mentioned earlier, the finite element model is validated

sing experimentally measured tool forces and torque as well asy matching the estimated processed zone shape and void sizeith those obtained experimentally by Shuaib et al. (2012). In the

xperimental work, RM-1 FS welder developed by Manufacturing

Fig. 3. Velocity boundary conditions.

Technology Inc. (MTI) is used to perform bead on plates and recordtool loads during the process. The welding conditions consideredfor validation are:

(a) Plunging phase: tool rotational speed N = 1000 rpm, and plung-ing feed fp = 20 mm/min.

(b) Welding phase: N = 1500 rpm, and welding speedV = 175 mm/min.

During plunging stage, the estimated axial force profile (Fig. 4a)does not exactly match the experimental measured one, while theestimated and measured torque profiles (Fig. 4b) are found to bematching. Furthermore, compared to maximum experimentallyrecorded values, the model was found to be underestimating tooltorque (11.9 N m) by about 10%, while the maximum plunging force(10 kN) is overestimated by about 6%.

Welding phase simulation is validated using average recordedtorque, axial, transverse, and crossfeed loads, measured duringwelding under position control. The estimated forces and torqueduring the welding phase showed fluctuations that are a result ofstick-slip phenomena. Therefore, the mean values are comparedto the ones measured experimentally and presented in Table 3.These results show that the model tends to overestimate all toolreaction forces and underestimate the tool torque. It is worth men-tioning that the tool used in experiments has a profiled pin andshoulder, while in the FEM the tool has a cylindrical pin and a flatshoulder. Scrolled shoulders are known to reduce axial tool load,and threaded tool pin reduces the transverse load as well (Fuller,2007). On the other hand, for similar tool dimensions scroll andthread features on the FSW tool produce a larger area of contactbetween the tool and the processed material, leading to an increasein tool torque. Assumptions of rigid body FSW tool and adiabatic

Load Plunging (N) Traverse (N) Crossfeed (N) Torque (N m)

Exp. 2974.6 722.2 50 8.64FEM 3386 898 869 6.25Error % +13.8 +24.3 – −27.7

1436 F. Al-Badour et al. / Journal of Materials Processing Technology 213 (2013) 1433– 1439

nd nu

crvswoFkabg(iasf

toFaam

Ff

Fig. 4. Comparison between experimental a

omparison reveals that the present model describes quite accu-ately the shape of the plasticized zone as well as the presence ofoid in the weld as shown in Fig. 5. However, because of the rea-ons explained, the model is found to overestimate the void sizeith a defect diameter around 0.29 mm compared the measured

ne of about 0.11 mm. Many reasons may lead to this discrepancy.irstly, material flow, which is affected by tool profile. It is wellnown that threaded tools produce better material flow and morexial flow which leads to smaller void like defects. Secondly, adia-atic heating effect is assumed; ignoring the cooling rate which hasreat effect on developing voids as reported by Schmidt and Hattel2005). Moreover, the assumption of constant coefficient of frictionn the contact model and the limitation of material surface visu-lization in CEL post processing tool as well as other phenomenauch as grain size, grain recrystallization and microstructure trans-ormation may have contributed to such discrepancy.

Finally, the estimated slip rate �̇ is found to be about 0.33 athe tool pin and 0.13 at the tool shoulder. The above values werebtained from the predicted average material velocity around the

SW tool (0.05 m/s) the maximum expected velocity near the pinssuming sticking condition (0.16 m/s) and the maximum velocityt tool shoulder (0.41 m/s). Schmidt et al. (2006) reported experi-ental values that are in the range of 0.1 to 0.3.

ig. 5. Matching estimated equivalent plastic strain and void with experimentallyound.

merical, (a) plunging force, and (b) torque.

5. Results and discussion

5.1. Influence of coefficient of friction on void formation

Fig. 6 illustrates the effect of coefficient of friction on the devel-oped volumetric defect. Here, the welding conditions (N = 1500 rpmand V = 175 mm/min) are held constant while the friction coeffi-cient is varied (� = 0.30, 0.58 and 0.80). The results show that adefect is generated for all three conditions. Using � of 0.3 leads toan unsuccessful weld as no material is deposited behind the toolpin and the FSW tool has produced a key like channel in the work-piece. On the other hand, results obtained using a coefficient of 0.58and 0.80 show defected welds with different void sizes and shapes(Fig. 7). While only an internal defect is formed for � = 0.80, anda surface defect in addition to an internal void are developed for� = 0.58. From Coulomb’s law, the larger the value of � the greaterthe sticking zone is formed. Stick-slip phenomenon is known to beresponsible for defect formation. Thus, in order to have a soundweld, tool design and welding conditions should guarantee havingmore sticking than slipping.

It is also found that the average value of the axial force for� = 0.58 is higher than that for � = 0.8, but the latter resulted ingreater force oscillation. The estimated average axial force for �0.58 and 0.80 are around 5200 N and 3400 N, respectively. Sim-ilar observations were found in estimated torque. The averagevalues of the torque for � = 0.58 and � = 0.80 are 5.66 N m and6.25 N m, respectively. Results for � = 0.3 are excluded from thecomparison because the condition resulted in an unsuccessfulweld.

5.2. Effect of welding parameters

The welding speed and the plunging control method are the twowelding parameters investigated using this model, and both con-ditions are simulated with a frictional contact of � = 0.80.Effect oftool welding speed: two welding speeds of 125 and 175 mm/minhave been selected to assess the speed effect on developed plas-tic zone and void formation. As can be seen in Fig. 8, the CEL FEresults show that the welding speed has a limited effect on devel-oped void size as the void size is found to decrease by about 20% forlow welding speed. The void shape is more affected by the weldingspeed. The void formed at 175 mm/min has elliptical shape while

that formed at 125 mm/min is more circular. Moreover, welding at175 mm/min, produces a greater void under the tool shoulder. Theestimated equivalent plastic strain values showed a slight increasewhen the welding speed is reduced. The plasticized zones are found

F. Al-Badour et al. / Journal of Materials Processing Technology 213 (2013) 1433– 1439 1437

Fig. 6. Effect of coefficient of friction on void size; (a) � = 0.3, (b) � = 0.58 and (c) � = 0.8, (top view).

n-voi

trmlcm

tlt

Fig. 7. Eulerian volume fractio

o be larger extending more under the tool shoulder toward theetreating side (Fig. 8). These findings are similar to the experi-ental ones reported by Gharacheh et al. (2006). The presence of

arger plastic zone and smaller void size at lower welding speedould be attributed to the increase in heat input leading to easieraterial flow.

In terms of dependent process parameters; axial force and

orque, the welding speed seems to have a negligible effect on bothoads; reducing the welding speed has reduced both axial force andorque by an average of 5%.

Fig. 8. Equivalent plastic strain for (a) V = 125 mm/m

d (a) � = 0.58, and (b) � = 0.8.

Force vs. position plunging control: in this comparison a weldingspeed of 125 mm/min is used to study the effect of force control andposition control on the void and plastic zone sizes. In both simula-tions tool rotational speed of 1500 rpm is employed and the contactmodel assumes a coefficient of friction of 0.80.

The effect of plunging control method on dependant process

parameters is presented in Table 4. It is found that using force con-trol plunging method increases the maximum tool torque. This isbelieved to be due to the increase of tool penetration depth dur-ing force control leading to greater tool workpiece contact area.

in, (b) V = 175 mm/min, weld zone zoom in.

1438 F. Al-Badour et al. / Journal of Materials Processing Technology 213 (2013) 1433– 1439

Table 4Maximum estimated loads and temperatures at investigated welding conditions.

Welding conditions Maximum axial load (N) Maximum torque (N m) Maximum temperature (◦C)

Speed V (mm/min) Plunging control method

175 Displacement 3800 8 583125 Displacement 3600 7.5 583125 Force 5500 12.5 583

Fig. 9. Equivalent plastic strain: (a) position control and (b) force control.

a spe

Bmt

atttffptrmcitA

Fig. 10. Microstructural analyses from two locations of beads performed at

ecause the model considers adiabatic heating effect, the maxi-um estimated temperature remains equal to the material solidus

emperature (583 ◦C) set in the Johnson–Cook material model.Fig. 9 qualitatively compares the void size developed at force

nd position control after 4.2 s of welding. The illustrated weld sec-ions, taken directly behind the FSW tool shoulder, indicate thathe generated void cross-sectional area during welding using posi-ion control is larger (0.16 mm2) than the one developed usingorce control (0.02 mm2). These results also indicate that usingorce control develops larger processed zone, as the equivalentlastic strains are 1.5 times higher than those developed with posi-ion control. These observations are supported by the experimentalesults obtained by Al-Badour (2012) and Shuaib et al. (2012). Theicrostructure analysis of beads produced at 150 mm/min (Fig. 10)

learly shows that force control produced no volumetric defects. Its believed that during force control welding the material resistanceo axial load drops when the void is formed close to the tool pin.s a result, the tool over plunging depth increases to compensate

ed of 150 mm/min in (a and b) position control and (c and d) force control.

for the decrease in the force, leading to forging and closing the voiddefect. Soundararajan et al. (2006) studied the effect of tool overplunging depth on surface defect formation and showed that anintermediate (0.4 mm) tool over plunging produced zero surfacedefects while both small (0.2 mm) and large (0.6 mm) over plung-ings produced welds with surface defects. This is an indication thatgreater tool over plunging may not necessarily lead to defect freeweld.

6. Conclusions

In this study a localized Coupled Eulerian Lagrangian modelwith adiabatic heat effect was implemented to simulate frictionstir welding of Al-6061-T6. The experimental validation of the CEL

model showed that:

• In tool plunging phase, the estimated maximum plunging forceand torque were in close agreement with the experimental

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F. Al-Badour et al. / Journal of Materials Pr

results while the axial, transverse and cross feed forces wereoverestimated by the CEL model.The FE model seemed to underestimate tool torque in the weldingphase.The CEL model described quite accurately the shape of the plas-ticized zone as well as the presence of void in the weld.

The analysis of FSW process parameters lead to the followingonclusions:

. The developed void size was directly affected by the frictionalcontact value and tool features; the higher the coefficient offriction the smaller the produced void size is formed.

. Because of tool over plunging, FSW using force control producedsmaller void defects.

. The welding speed had more effect on void shape than on voidsize.

cknowledgements

The authors would like to acknowledge the support provided bying Abdulaziz City for Science and Technology (KACST) through

he Science and Technology Unit at King Fahd University ofetroleum and Minerals (KFUPM) for funding this work throughroject No. NSTIP (080ADV66-04) as part of the National Science,echnology and Innovation Plan.

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