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Journal of Materials Processing Technology 211 (2011) 688–694

Contents lists available at ScienceDirect

Journal of Materials Processing Technology

journa l homepage: www.e lsev ier .com/ locate / jmatprotec

tudy on welding temperature distribution in thin welded plates throughxperimental measurements and finite element simulation

.J. Attarha ∗, I. Sattari-Farechanical Engineering Department, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran

r t i c l e i n f o

rticle history:eceived 7 October 2010eceived in revised form7 November 2010

a b s t r a c t

The welding temperature distributions in the HAZ were measured using K-type thermocouples in sim-ilar and dissimilar thin butt-welded joints which experienced one-pass GTAW welding process. Threedimensional finite element simulations were also implemented to predict the temperature distributionsthroughout the plates using ABAQUS software. Comparison between experimental and simulation results

ccepted 2 December 2010vailable online 8 December 2010

eywords:elding temperature distribution

inite element simulation

reveals very good agreement. The results provide good evidence for prediction of the HAZ microstructureconsidering the fact that the thermocouples have been attached very closely to the weld line, and provideobjective cooling slopes. The absence of filler materials in the welded joints is helpful to observe the peaktemperature and cooling slope differences in relation with material properties differences.

© 2010 Elsevier B.V. All rights reserved.

hermal analysis-type thermocouple

. Introduction

Welding is the most reliable, efficient and practical metal join-ng process which is widely used in industries such as nuclear,erospace, automobile, transportation, and off-shore. In spite of theany advantages, there are some limitations affecting this pro-

ess. Welding defects influence the desired properties of weldedoints. Thermal cycles significantly affect parameters such as resid-al stresses, deformations, weld microstructure, HAZ hardness.ecause of local heating during welding process, controlling thehermal cycles is critical.

Murugan et al. (1998) measured temperature distributionn type 304 stainless steel welded plates using thermocouples.heir results show that the temperature distributions at mea-uring points are clearly dependent on welding conditions. Dengnd Murakawa (2006) experimentally obtained temperature dis-ributions in butt-welded pipe joints using thermocouples andompared the results with finite element simulations. Referringo their results, it is obvious that the temperature distributionround the heat source is very steady when the welding torch

oves around the pipe. In another work, Deng and Murakawa

2008) measured the temperature cycles and residual stresses in.25Cr–1Mo steel pipes incorporating solid-state phase transfor-ation. Kermanpur et al. (2008) studied the temperature cycles

∗ Corresponding author at: No. 56, Asayesh Ave., Alborz Town, Tehran, Iran.el.: +98 2122444068.

E-mail address: javad attarha@aut.ac.ir (M.J. Attarha).

924-0136/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.jmatprotec.2010.12.003

due to different welding sequences and parameters of inconel 800welded pipes. They showed that volumetric heat source providesthe best results for temperature cycles throughout the pipe. Kasuyaet al. (2000) suggested an analytical heat conduction model forpredicting temperature histories of bidirectional multi-pass weld-ing samples with short bead lengths, and verified the model withexperimental results using thermocouples. Lee and Wu (2009)studied the effect of peak temperature and cooling rate on the sus-ceptibility to intergranular corrosion of alloy 690 by laser beamand gas tungsten arc welding. In their welding time, the tem-perature was recorded continuously at various points within theHAZ and base metal, and the resulting thermal profiles were thencorrelated with microstructural observations of the tested speci-mens in order to investigate the influences of peak temperatureand cooling rate on the susceptibility of the GTAW and LBW weldpieces to IGC. Robot simulation has been used by Ericsson andNylen (2007) in combination with finite element simulations tooptimize robot speed in order to minimize distortions while keep-ing complete joint penetration. They provided an iterative methodfor robot speed optimization. The proposed method allows foroptimization of the heat input to the component, and thereby, mini-mizes component deformation for parts with complex shapes. Theirmodel is validated comparing temperature distribution predictionswith experimental measurements. In another article, variation

of transient temperatures and residual stresses in a friction stirwelded plate of 304L stainless steel has been measured by Zhu andChao (2004). Three-dimensional nonlinear thermal and thermo-mechanical numerical welding simulations were used to predicttemperature and residual stress distributions. Their results show

M.J. Attarha, I. Sattari-Far / Journal of Materials Processing Technology 211 (2011) 688–694 689

the l

tattwutptrt

ssttwtt

2

twarwmat

TW

Fig. 1. Specifications of three butt-welded joints and

hat due to unknown heat energy input from the process, theirnalysis method is unique and effective for the calculation ofemperature fields. Liang and Yuan (2008) investigated weldingemperature fields by non-contact temperature measurement inelding of AZ31B magnesium alloy and obtained cooling curvessing thermocouples. Zhu and Chao (2002) in another work triedo investigate the effect of each temperature-dependent materialroperty on the transient temperatures, residual stresses and dis-ortions in the computational simulation of welding process. Theiresults show that the thermal conductivity has certain effects onhe distribution of transient temperature fields during welding.

The present work is concerned with the calculation of the tran-ient temperature distributions developed in welded plates. Twoimilar and one dissimilar GATW one-pass joints are chosen hereo measure the temperature cycles throughout welding. K-typehermocouples are utilized for this purpose. Based on ABAQUS soft-are, 3D finite element models are developed in order to predict

he temperature cycles. Experimental results are then conductedo validate simulations.

. Experimental procedure

The temperature measurement was implemented using K-typehermocouples. This method was used during the gas tungsten arcelding (GTAW) of two similar joints from stainless steel type 304

nd St37 carbon steel, and one dissimilar joint from these mate-

ials, all without application of filler materials, and the resultsere compared with those of finite element method. The ther-ocouples were located in drilled holes in the work pieces, fixed

t mid plane level. Temperatures were measured at different dis-ances from the weld melt line on both left and right side of the

able 1elding parameters.

Experiment code Welding voltage (V) Welding current (I) Welding speed (m

E1 14.6 101 1.8E2 14.8 100 3.125E3 15.5 101 2.3

ocations of thermocouples throughout weld pieces.

plates. Fig. 1 shows schematic of the test specimen and the ther-mocouple locations. As presented, the weld piece was comprisedof 200 mm × 200 mm × 3 mm plates. To record the measured tem-peratures, the collected signals were transferred to a data loggerand a PC. The data logger was set to record at least 10 readings persecond from the thermocouples. Lab View software was used todisplay the thermal curves.

When a thermocouple is attached to a plate, the following fac-tors must be taken into consideration as well. The temperature isrecorded at the first point along the thermocouple at which the twowires touch. If any other junction is there along the thermocouplefor any reason, the thermal cycle measurement will not be accurate.

The work pieces were welded in one pass without filler material.During welding, argon backing gas was used for protecting weld ofhot cracking. Voltage and current were noted during welding bymeans of welding apparatus. Furthermore, the duration of weldingpass was recorded and through which, welding speed was calcu-lated. The voltage (V), current (I) and the traveling speed (v) of theweld passes in each joint are given in Table 1. Considering an arcefficiency of 0.5 (�) for GTAW process (Zhu and Chao, 2002), theheat input per mm length of weld (Qw) was calculated using thefollowing equation:

Qw = �VI

v(1)

Transient temperature distributions are reported in the follow-ing sections in detail for each joint mentioned above. The variationsof temperature vs. distance from the weld melt line have also beenconsidered. The chemical compositions of steels S304 and St37 areprovided in Table 2.

m s−1) Arc efficiency Shielding gas Ar (Lit/miri) Heat input (kJ mm−1)

50% 10 0.40950% 10 0.23750% 10 0.341

690 M.J. Attarha, I. Sattari-Far / Journal of Materials Processing Technology 211 (2011) 688–694

Table 2Chemical composition of AISI type 304 stainless steel and St37 carbon steel.

Grade C Mn Si P

S304 0.046 1.5 0.7 0.025St37 0.12 0.57 0.02 0.01

3

cpjtcw

m3TobmtITbymtotmtfl

TT

Fig. 2. 3D finite element model and meshing in welding simulation.

. Numerical procedure

Based on using ABAQUS software, a thermal finite elementomputational procedure was developed to calculate welding tem-erature fields during welding of three one-pass butt-welded

oints. The heat conduction problem has been solved using heatransfer analysis to obtain temperature histories. The formulationonsiders the contributions of the transient temperature field, asell as temperature-dependent thermo-physical properties.

In the finite element simulation, GTAW process with no filleretal has been considered for three butt-welded joints of AISI type

04 Stainless Steel and St37 carbon steel, as illustrated in Fig. 2.he material properties included are presented in Table 3. Becausef the symmetry condition of the two similar joints, one plate haseen modeled; while in the dissimilar joint, both plates have beenodeled. The model mesh is shown in Fig. 2. The dimensions of

he simulation model are the same as the experimental specimen.n the weld zone and its vicinity, a fine mesh has been considered.he number of nodes is 9999, and that of elements is 6400. 8-nodedrick elements of type DC3D8 have been used in the thermal anal-sis code. The effect of mesh size has been studied through theethod proposed by Malik et al. (2008). In this method, the peak

emperature is the parameter being studied in sensitivity analysis

f the mesh size. Application of a finer mesh in this work led to lesshan 2% difference in the peak temperatures. Thus, the presented

esh was used. DFLUX user subroutine has been employed usinghe FORTRAN language and called in the model to calculate the heatux. A moving volumetric heat source has been considered for the

able 3hermo-physical properties of St37 carbon steel and AISI type 304 stainless steel.

S304

Temperature (◦C) Density (kg/m3) Specific heat (J/kg ◦C) Con

0.0000 7900.00 462 14100.00 7880.00 496 15200.00 7830.00 512 16300.00 7790.00 525 17400.00 7750.00 540 18600.00 7660.00 577 20800.00 7560.00 604 23

1200.0 7370.00 676 321300.0 7320.00 692 331500.0 7320.00 700 120

S Cr Mo Ni Co

0.003 18.42 0.08 8.13 0.060.012 0.002 0.01 0.03 0.001

modeling of welding arc, based on the double ellipsoidal distribu-tion proposed by Goldak et al. (1984), which is expressed by thefollowing equations. For the front heat source:

Q (x′, y′, z′, t) = 6√

3ff Qw

˛1bc�√

�e−3x′2/˛2

1 e−3y′2/b2e−3z′2/c2

(2)

And for the rear heat source:

Q (x′, y′, z′, t) = 6√

3frQw

˛2bc�√

�e−3x′2/˛2

2 e−3y′2/b2e−3z′2/c2

(3)

where x′, y′ and z′ are the local coordinates of the double ellipsoidmodel aligned with the weld line. ff and fr are parameters for defin-ing the fraction of the heat deposited in the front and the rear ofthe welding arc, respectively, and ff + fr = 2.0. In this work, ff wasconsidered 1.4 and fr was 0.6. It should be noticed that tempera-ture gradient in the front of the arc is much steeper than that ofthe rare. Qw is the welding heat source power. The calculation ofthis parameter has been mentioned in Section 2. The parametersa1, a2, b, and c belong to the features of welding heat source. Theseparameters can be determined through experimental study of theweld pool and may be adjusted to create a desired melted zoneaccording to the welding conditions.

Since in this work, one pass GTAW has been carried out withoutusage of filler metals, the common techniques of adding weld ele-ments, such as element birth and death, element movement andelement interaction, are not applicable. When the welding arc isapplied to the work-pieces, the whole weld line is present andundergoes heating. Thus, application of the mentioned techniquesinduces errors in the simulation, and in this work, the moving heatsource reaches to the weld elements, which are included in themodel throughout the entire simulation time.

During welding, the governing equation for transient heat trans-fer analysis is given by:

�c∂T

∂t(x, y, z, t) = −∇ · −→q (x, y, z, t) + Q (x, y, z, t) (4)

where � is the density of the materials, c is the specific heat capacity,T is the current temperature, −→q is the heat flux vector, Q is theinternal heat generation rate, x, y and z are the coordinates in thereference system, t is the time, and� is the spatial gradient operator.

The non-linear isotropic Fourier heat flux constitutive equationis employed:

−→q = −k∇T (5)

where k is the temperature-dependent thermal conductivity.

St37

ductivity (J/m ◦C) Specific heat (J/kg ◦C) Conductivity (J/m ◦C s)

.6 444 45.9

.1 472 44.8

.1 503 43.4

.9 537 41.4

.0 579 38.9

.8 692 33.6

.9 837 28.7

.2 860 28.6

.7 863 29.5– –

M.J. Attarha, I. Sattari-Far / Journal of Materials Processing Technology 211 (2011) 688–694 691

F(

Fig. 3. Welding direction and temperature fields at the middle of weld line.

Fig. 4. Temperature history during welding for experiment E1, at points with dif-ferent distances from the weld melt line.

ig. 5. Temperature history, comparison between experiment E1 and finite element simulation results at points with different distances from the weld melt line, (a) 3 mm,b) 8 mm, (c) 13 mm, (d) 18 mm, (e) 23 mm.

692 M.J. Attarha, I. Sattari-Far / Journal of Materials Processing Technology 211 (2011) 688–694

Table 4Temperature-dependent combined convection coefficient model (Salonitis et al.,2007).

h [W/m2 K] T − T0 [K]

1.85 569.079 278

taaztwbcc

4

4

itptfdrawtIttwti

4

o

Ffi

Fig. 7. Temperature history during welding for experiment E2, at points with dif-ferent distances from the weld melt line.

18.5 55652.6 2778

To consider the heat losses, both the thermal radiation and heatransfer on the weld surface have been considered. Radiation lossesre dominating for higher temperatures near and in the weld zone,nd convection losses for lower temperatures away from the weldone (Deng and Murakawa, 2006). It is customary to use combinedhermal boundary conditions to avoid the difficulties associatedith radiation modeling. Likewise, a temperature dependent com-

ined convection coefficient has been used to model the coolingondition. Table 4 presents the temperature dependent convectionoefficients.

. Results and discussion

.1. Similar butt weld joint of St37 carbon steel

Fig. 3 presents the temperature contours and welding directionn the finite element simulation model of this joint. In Fig. 4, theemperature distributions throughout the weld pieces at differentoints with certain distances from the weld melt line are illus-rated. The maximum temperature at the point with 3 mm distancerom the melt line is about 550 ◦C. It is important to notice that theecrease of temperature with distance has a nonlinear trend. Theeason is associated with the local heating of the welding torchnd the nonlinear variation of the material’s thermal propertiesith temperature. The experimental and finite element simula-

ion results are compared for distances mentioned above in Fig. 5.t is obvious that the results have generally good conformity, buthe difference between the results is noticeable at the tempera-ure rise. It is probably due to molten spattering during fusionelding and the selection of heat source model. Peak tempera-

ure distribution with distance from the weld melt line is shownn Fig. 6.

.2. AISI type 304 Stainless steel similar butt weld joint

Temperature histories of the points in the left weld piece,btained by experimental measurements, are presented in Fig. 7,

ig. 6. Peak temperatures vs. distance from weld melt line for experiment E1 andnite element simulation results.

Fig. 8. Peak temperatures vs. distance from weld melt line for experiment E2 andfinite element simulation results.

for this joint. The maximum temperature is about 470 ◦C. Com-paring Figs. 4 and 7, it is clear that the slope of cooling in St37is steeper than S304 which is because of the differences in thermalproperties, especially thermal conductivity. The peak temperaturedistributions, extracted from both experimental and FE results, areillustrated in Fig. 8.

4.3. Butt weld joint of dissimilar metals

In this section, the temperature distribution in a dissimilar butt-welded joint comprised of St37 carbon steel and AISI type 304

stainless steel is studied (Figs. 9–11). The experimental temper-ature histories are shown in Figs. 9 and 11 for St37 and S304,respectively. It can be seen that pick temperatures of S304 arehigher than St37. The experimental measurement and finite ele-ment results are compared for both materials in Figs. 10 and 12. The

Fig. 9. Temperature history during welding for experiment E3, at points with dif-ferent distances from the weld melt line in St37 carbon steel weld piece.

M.J. Attarha, I. Sattari-Far / Journal of Materials Processing Technology 211 (2011) 688–694 693

Fig. 10. Temperature history, comparison between experiment E3 and finite ele-ment simulation results at points with different distances from the weld melt linein St37 carbon steel weld piece, (a) 3 mm, (b) 8 mm, (c) 13 mm.

Fig. 11. Temperature history during welding for experiment E3, at points with dif-ferent distances from the weld melt line in AISI type 304 stainless steel weld piece,(F) 3 mm, (G) 8 mm, (H) 13 mm.

Fig. 12. Temperature history, comparison between experiment E3 and finite ele-ment simulation results at points with different distances from the weld melt linein AISI type 304 stainless steel weld piece, (f) 3 mm, (g) 8 mm, (h) 13 mm.

Fig. 13. St37 and S304 peak temperatures vs. distance from weld melt line forexperiment E3 and finite element simulation results.

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94 M.J. Attarha, I. Sattari-Far / Journal of Mate

ooling slopes are displayed for both materials in Fig. 13. As can bebserved, this slope for St37 is slightly lower than S304. It should beoticed that since there is no consumable filler material in the dis-imilar welded work-pieces and considering the symmetry duringelding, the differences between peak temperatures and cooling

ates most correspond to thermal material properties, especiallyhermal conductivity. Another reason behind peak temperature dif-erences may be attributed to the effect of phase transformationn St37 carbon steel. Occurrence of solid state phase transfor-ation in St37 carbon steel absorbs a proportion of heat source

nergy.

. Conclusions

3D finite element simulation of GTAW welding for three jointstwo similar and one dissimilar) comprised of AISI type 304tainless steel and St37 carbon steel thin plates, was studied,nd the temperature distributions and histories were com-ared with experimental measurements. Comparison between thenite element and experimental results revealed that the devel-ped model had good capability for predicting the temperatureycles throughout welding. The following conclusions may beonducted:

1) Peak temperature distribution vs. distance in the weld piecesshows that the temperature decreasing behavior has a nonlin-ear nature. Prediction of the cooling slope with distance can beused in the prediction of the HAZ microstructure.

2) Comparing the peak temperatures in the dissimilar joint of St37carbon steel and type 304 stainless steel, it is revealed that theS304 peak temperature near the weld melt line is higher thanSt37. The difference between thermal conductivity coefficientscan justify this behavior.

rocessing Technology 211 (2011) 688–694

Acknowledgements

The authors wish to express their acknowledgement to Dr. R.Moharrami and Mr. I. Akbarzadeh for their fruitful help and supportduring the course of this project.

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