Computational Materials Science 68 (2013) 107–116

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Computational Materials Science

journal homepage: www.elsevier .com/locate /commatsci

Microstructure evolution simulation in hot rolled DP600 steel during gas metalarc welding

A. Ramazani a,⇑, Y. Li a, K. Mukherjee a, U. Prahl a, W. Bleck a, A. Abdurakhmanov b, M. Schleser b,U. Reisgen b

a Department of Ferrous Metallurgy, RWTH Aachen University, Germanyb Welding and Joining Institute, RWTH Aachen University, Germany

a r t i c l e i n f o

Article history:Received 6 August 2012Received in revised form 12 September 2012Accepted 13 September 2012Available online 22 November 2012

Keywords:Gas metal arc welding (GMAW)Dual phase (DP) steelTime–temperature evolution modellingMicrostructure evolution modellingPhase field modelling

0927-0256/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.commatsci.2012.09.009

⇑ Corresponding author. Tel.: +49 241 80 95841; faE-mail address: ali.ramazani@iehk.rwth-aachen.de

a b s t r a c t

This work investigates the gas metal arc welding (GMAW) process of high strength hot rolled dual phasesheet steel and discusses the modelling techniques. The thermocycles during welding were measuredexperimentally and simulated with the software tool SimWeld. The evolution of different diffusionaltransformational phases was simulated based on dilatometer experiments. MICRESS� was used to simu-late the change of the microstructure during heating and cooling periods. The significant input parame-ters include interface mobility and nucleation density. A simple bainite transformation model was addedin MICRESS� since it is needed in predicting the microstructure undergoing fast cooling. The simulationresults are compared with experimental findings, and good agreement is achieved.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

The modern auto industry is increasingly reducing car bodyweight by the implementation of a new technology of materialprocessing and light materials. Advanced high-strength steels(AHSSs) reveal good potential for this purpose. Dual-phase (DP)steels exhibit an excellent combination of cold formability andstrength compared to conventional low carbon and high strengthlow alloy steels [1,2]. The DP microstructure consists of a disper-sion of hard martensite particles in a soft and ductile ferrite matrix.A well-balanced ratio between ferrite and martensite fractions hasbeen reported to be one of the determining factors for the mechan-ical properties of these steel grades [3]. Other factors include themorphology of the martensite islands, carbon content in the mar-tensite, and the deformation condition of ferrite (i.e., geometricallynecessary dislocations (GNDs) and ferrite grain size) [4–10].

Welding, which is the most important joining technique in theautomotive industry, plays a prominent role in determining the fi-nal mechanical properties [11–13]. Local heat input of the weldingheat source that induces a large temperature gradient on the work-piece will destroy the microstructure, and hence the mechanicalproperties, of DP steel [14].

Gas metal arc welding (GMAW) is commonly used for auto bodystructures that are plastically deformed or closed parts. It is also

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x: +49 241 80 92253.(A. Ramazani).

employed where the part geometry inhibits the use of resistancespot welding or when additional joint strength and stiffness are re-quired in the design [15]. The joined parts using GMAW typicallyexperience a higher heat input and lower heating and cooling ratescompared to other welding techniques in automotive applications[16]. Therefore, it is important to characterize the microstructureevolution during welding and its influence on the final mechanicalproperties of DP steels.

Phase transformations during welding were simulated by thecommercial phase field software MICRESS� [11]. Initially, thephase field model has been used in the simulation of alloy solidifi-cation because it enables a detailed spatially resolved descriptionof the morphology [17]. More recently, it has been applied in themodelling of solid–solid phase transformations. An increasinginterest is using the phase field method to simulate microstructureevolution in the heat affected zone of welds. Recently, the predic-tion of the microstructure evolution in technical alloys and undertechnical process conditions using phase field model approachhas become possible [12,18]. The phase field method shows twoprincipal characteristics: the diffuseness of the interface and thegenerally addressed non-equilibrium states [19].

Many phase field models are based on the approach presentedby Wheeler et al. [20–24]. Steinbach et al. have proposed an alter-native multiphase field approach with a different definition of thefree energy density and different assumptions about alloy compo-sition in the interface [25]. This approach proposed describes themore general case of phase transitions in a multiphase system,

Fig. 1. Initial microstructure of the dual-phase steel studied.

108 A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116

including peritectic and eutectic systems, and allows the simula-tion of growing microstructures in technically relevant alloys.Thermo-physical data like interfacial energy, diffusion coefficient,and phase diagram data are taken into account [26]. This approachis commercialized in the software MICRESS�.

In recent years, much attention has been devoted to studyingthe weldability of sheets of DP steels [27–33]. However, a thoroughprocess and two-scale model of GMAW is missing, in particular forDP steels. The work presented in this paper consists of developingwelding procedures with the GMAW process, representing andmodelling different thermal cycles and heat distributions, charac-terizing the welded microstructure, and performing CCT tests toquantify the parameters of phase field modelling of phase transfor-mation for this chemistry. GMAW experiments with process mod-elling were used to determine the maximum temperature andheating and cooling rates. Based on these findings from macroanal-ysis of the process, dilatometer tests were performed for setting upthe MICRESS� approach.

First GMA welding experiments have been performed whileanalysing the temperature evolutions locally with thermocouples.Based on the temperature parameters dilatometer experimentshave been carried out to investigate the phase transformations. Fi-nally, these transformation data have been used to build up thenumerical model using MICRESS�.

2. Experimental techniques

2.1. Material

For the welding experimental investigation, the hot rolledsheets of 2.5 mm with 600 MPa (ultimate tensile strength) gradehigh strength DP steel was used. As can be seen from the chemicalcomposition listed in Table 1, this is a low carbon steel, with addi-tions of a relatively high level of manganese, plus some Si and Cr tofacilitate the formation of the DP microstructure.

As filler wire for the welding of DP steel, G3Si1 (SG2) wire of0.9 mm diameter was used. According to the norm EN ISO 14341,this filler material is well suited to the welding of low carbon steel.The chemical composition of the filler material is shown in Table 1.The filler material has a tensile strength of 560 N/mm2 (EN ISO14341), so that the weld seam guarantees approximately 5–10%less tensile strength as the base metal (600 N/mm2, Table 1).

The light optical micrograph (Fig. 1) shows that initial DP600microstructure consists of 90% ferrite and 10% martensite. Theaverage ferrite grain size is 5 lm.

2.2. GMAW experiments

The investigations were conducted on a 6-axis weld robot OTCDaihen Almega AX-V6. The arc source used for the experimentswas an OTC DP400 with a power input of 22 kW for pulse process.For welding experiments, a pulsed arc welding process under inertgas was used. The pulse rate used during the welding in pulsedmode was 84 Hz. The gas used for the investigations consisted of80% argon and 20% CO2 with a flow rate of 15 l/min. The contacttip-to-work distance was 15 mm.

Welding samples were prepared from hot rolled sheets and ma-chined into 200 � 50 � 2.5 mm. The welding parameter set, as pre-

Table 1Chemical composition of DP600 steel and filler material SG2 in weight%.

C Si Mn

Base material 0.06 0.1 0.86

Filler material 0.06–0.014 0.7–1.0 1.3–1.6

sented in Table 2, corresponds to practical welding conditions. Theparameter vd is the feeding speed of the consumable electrode, vs isthe speed of the electrode (or arc), U is the operating voltage, I isthe operating current, P is the power of the welding process andE is the energy input per unit length. Welding parameters wereoptimized on weld seam.

2.3. Thermocouple measurement

The cooling rate has an important influence on the formation ofmicrostructure in the weld seam and HAZ. To get thermal cycles inthe HAZ, four thermocouples Type K (chromel–alumel) were fixedat different distances from the weld spot. The distances were cho-sen to be TE1 = 2 mm, TE2 = 4 mm, TE3 = 6 mm and TE4 = 8 mm onthe bottom side, as shown in Fig. 2.

2.4. Dilatometry tests

Welding CCT (Continuous Cooling Transformation) tests werecarried out for the given steel. For this purpose, small rectangularsamples (7 mm � 4 mm � 2.5 mm) were used. In the presentstudy, the dilatometric experiments were performed on a dilatom-eter type DIL-805A/D fabricated by Baehr Thermoanalyse, GmbH.The samples were heated up to two austenization temperatures(1050 �C and 1350 �C). Two heating rates were used for this pur-pose: 100 �C/s and 400 �C/s. Subsequently, samples were cooleddown to room temperature at several cooling rates: 25 �C/s,50 �C/s, 150 �C/s, 250 �C/s and 350 �C/s.

2.5. Microstructure analysis

The microstructures of welded and dilatometer samples wereexamined by optical microscopy after etching with 3% nital. Thesamples with the highest cooling rates were also used to determinethe austenite grain size at peak temperature. To reveal the prioraustenite grain sizes, a modified nital was used.

Cr Al P S Mo

0.42 0.03 0.026 <0.001 0.005

0.15 0.02 0.025 0.25 0.15

Table 2The welding parameter set.

No. vd (m/min) vS (m/min) P (kW) E (kJ/m)

V1 4.85 0.25 2.1 504V2 7 0.42 3.02 431.4V3 9 0.59 3.91 397.6V4 11 0.69 4.80 417.4V5 11 0.79 4.80 364.6V6 13 0.89 5.74 387V7 13 0.98 5.74 351.4V8 17 1.23 7.56 368.8V9 17 1.36 7.56 333.5

Fig. 2. The location of the thermocouple measurement points.

A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116 109

3. Modelling techniques

3.1. Thermal cycle modelling using SimWeld

The program SimWeld [34] was developed to simulate weldingprocesses (Fig. 3). It offers the calculation of the temperature fieldand the geometrical form of the welding pool in shielded GMAW ofsteels and aluminium alloys.

Fig. 3. GMAW process simulation with SimWeld.

The following equation is assumed for the calculation of theheat source power:

P ¼ �I �U þ 1T

Z T

t¼0D IðtÞDUðtÞdt ð1Þ

with current I and voltage U. In the model, the following distribu-tion of the power density of the arc is offered, which is in fact inter-mediate between normal-circular and cylindrical distributions.

qðrÞ ¼ffiffiffi3p

Q0:886pr2 e

� 3r4e

r4

ð2Þ

The heat transfer process equation is:

vsq ¼@H@x¼ div½kðTÞ grad TðHÞ� þ qvol ð3Þ

where H is the enthalpy; k(T) is the coefficient of thermal conduc-tion depending on the temperature and qvol is the distributed vol-ume heat source of metal drops. Convective processes in thewelding pool are not simulated.

3.2. Modelling the phase transformations using MICRESS�

Phase transformations during welding were simulated by thecommercial phase field software MICRESS�. The evolution ofphases in terms of physical material parameters is formulated bySteinbach, which is computed in MICRESS� through solving thephase field evolution equation [19]. While the thermodynamicdata for the Gibbs energy description have been taken from Ther-moCalc database TCFe6 and the diffusion data from Mob2, theadditional parameters for this approach, like interface mobilityand nucleation conditions, were determined by fitting theMICRESS� phase fraction output and grain size output to the dila-tometric results. The phase field evolution was coupled with thecarbon diffusion in the present study. It was assumed that onlyinterstitial atoms (i.e. carbon atoms) could redistribute by long-range diffusion. The substitutional alloying elements (e.g., Mn,Mo) had diffusion coefficients several orders of magnitude lessthan carbon. Thus, they were considered to be immobile in theirsublattice. This is the paraequilibrium condition used for the calcu-lation of driving force for phase transformation [35]. Driving forcefor phase transformation is given by the local concentration of car-bon and the temperature.

In the heating simulations, martensite was considered as asupersaturated solid solution of carbon in BCC ferrite. The param-eters used for the heating period are listed in Table 3. In the simu-lation, martensite is considered as a supersaturated solid solutionof carbon in ferrite phase. Austenite grain growth is also taken intoconsideration. These parameters are modified according to theexperimental results. The simulated curve is drawn and comparedwith the relative experimental curve. In order to make the simula-tion fit to the experiment, certain parameters are modified.

The parameters used for the two heating rates are almost thesame. It is found that the austenite/austenite interface mobilityhas the most obvious effect on the slope of the simulation curveand the austenite grain size. The c/a and c/a0 mobilities influencethe nucleation of austenite. Only the austenite/austenite interfacemobility for a lower heating rate is set as half that for the higherheating rate. This is because with a lower heating rate the austen-ite grains have more time to grow, leading to a huge grain size be-fore the start of bainite formation. Therefore, the mobility isreduced to obtain smaller grain. The peak temperature has noinfluence on the determination of parameters.

As MICRESS� can only model diffusion controlled phase trans-formation, the present modelling approach considers bainitetransformation only during the cooling cycle of welding. It isassumed that the remaining austenite (after bainite transforma-

Table 3Parameters used for austenite formation.

Parameters used in MICRESS 100 �C/s 400 �C/s

Undercooling for nucleation (K) 0 0Shield distance of nucleation (lm) 4 4c/a0 Interface mobility (m4 J�1 s�1) 1 � 10�4 1 � 10�4

c/a Interface mobility (m4 J�1 s �1) 10.8 � 103 � e(�140,000/8.314T) 10.8 � 103 � e(�140,000/8.314T)

c/c Interface mobility (m4 J�1 s �1) 5.4 � 102 � e(�140,000/8.314T) 10.8 � 102 � e(�140,000/8.314T)

Table 4Parameters used for bainite formation of the heating rate 400 �C/s.

Parameters used in MICRESS Quantified values

Cooling rate (�C/s) 350 250 150 50 25Undercooling for nucleation (K) 150 150 150 150 150Shield distance of nucleation (lm) 6 8 8 8 8Max. nuclei number 75 75 65 40 40Austenite/bainite interface mobility (cm4 J�1 s�1) 6 � 10�5 6 � 10�5 5 � 10�5 1.7 � 10�5 1.1 � 10�5

Table 5Parameters used for bainite formation of the heating rate 100 �C/s.

Parameters used in MICRESS Quantified values

Cooling rate (�C/s) 350 250 150 50 25Undercooling for nucleation (K) 150 150 150 150 150Shield distance of nucleation (lm) 6 6 6 8 8Max. nuclei number 95 95 75 40 35Austenite/bainite interface mobility (cm4 J�1 s�1) 6 � 10�5 5 � 10�5 5 � 10�5 1.6 � 10�5 1 � 10�5

Fig. 4. Macrosections of GMA welded samples V1–V9.

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tion) transforms to martensite. Faceted growth mode is used tomodel bainitic phase transformation in MICRESS�. The austenite-to-bainite driving pressure is calculated considering bainite as fer-rite. The start temperature of bainite nucleation is determinedaccording to the experimental data. For different cooling rates,the parameters have some changes, which can be seen from Tables4 and 5. These two tables list the parameters used for the condi-tions of 400 �C/s and 100 �C/s heating, respectively.

The various values for each of the parameters are selected fordifferent combinations using different conditions. Normally, forfast cooling conditions, the combination of small shield distance,large maximum nuclei number and large mobility are used.

The conditions with high cooling rates have a shorter time forphase transformation. The temperature gradient along with cool-ing time is large. High austenite/bainite interface mobility and alarge amount of nuclei are set, therefore, to fix the simulation tothe experiment. For the situations with a low cooling rate, thesevalues are decreased. The undercooling value is defined from theoutput file of MICRESS�. It is found that the undercooling valuehas a significant influence on the situation with a slow cooling rate.

The parameters for bainitic transformation are quantified frombest fit of the experimental bainitic transformation curves for dif-ferent cooling rates and prior austenite grain size. The carbon dif-fusivity in austenite is much higher than that obtained usingThermoCalc database. The possible reason may be the tip of thebainite being in nanometre dimensions, and may cause a larger va-lue of carbon diffusivity in austenite. In the current MICRESS� code,the phenomenon is reproduced by using a node size of 0.125 mi-crons but a higher effective value of carbon diffusivity.

4. Experimental results and discussion

4.1. Weld process

Fig. 4 shows macrosections for GMA welded samples at weldingconditions listed in Table 2. GMAW exhibits a large weld pool and a

wide welding zone, showing a T-shape fusion zone with reinforce-ment on the top. The amount of heat input and the heat source dis-tributions are the main reasons for the wide heat affected zone;GMAW provides larger heat input with non-uniform (Gaussian-like) heat distribution, inducing a larger melt region and T-shapeweld pool.

Fig. 5 presents a cross section macrograph of the joint V1. Thisfigure shows microstructures in the heat affected zone at differentdistances to the centre line as appointed in Fig. 2. The microstruc-tural examinations near the fusion zone TE1 (2 mm from the centreof the weld seam) revealed a facetted ferrite which is a kind ofupper bainitic microstructure. The maximum measured tempera-ture (Tmax) at that point was 1150 �C. Further away TE2 (4 mmfrom the centre of the weld seam) a partly bainitic structure wasfound. Tmax at this point was measured to be 850 �C. Further away,the fractions of ferrite increase TE3, almost no bainite can be foundand tempered martensite shows up. The transition between theheat affected zone and the base metal TE4 exhibits a tempered

Fig. 5. Microstructures of HAZ in different distance to the weld seam centre in GMA welded specimen V1.

Fig. 6. Experimentally measured thermocycles vs. simulated thermocycles withSimWeld in different distance to the weld seam for weld specimen V1.

Table 6Parameters for the experimental thermal cycles.

Points Maximum temperature (�C) Heating rate(�C/s)

Cooling rate(�C/s)

TE1 1148.8 221.45 �12.03TE2 857.3 221.45 �11.83TE3 670.4 112.87 �11.11TE4 492.3 81.42 �10.64

Fig. 7. Modelling of austenitization for heating rate 400 �C/s.

A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116 111

zone with a similar microstructure as the base metal, but withpartly tempered martensite.

These results demonstrate the microstructural changes withdistance from the weld seam centre.

4.2. Temperature evolution

The cooling rate during the crystallization has a fundamentalinfluence on the formation of structure in the weld seam and

HAZ. To measure thermocycles in the HAZ, thermocouples TypeK (chromel–alumel) were fixed at a different distance from theweld seam, Fig. 2. Fig. 6 shows a comparison of the measured dataand simulation with SimWeld for GMAW.

Table 6 shows the peak temperature, heating rate and coolingrate for the different points in the HAZ.

Fig. 6 and Table 6 show that the peak temperature, heating rate,and cooling rate decrease with increasing distance from the middleline of the fusion zone. The heating of the material far from the fu-sion zone is dependent on the heat conductivity of the materials;therefore, there is a heat hysteresis of the farther points from thefusion zone.

4.3. Microstructure evolution during heating and cooling

The temperature–dilatation curves obtained from dilatometertests were transformed into temperature–phase fraction curvesfor each condition during heating and cooling. These curves were

Fig. 8. Carbon concentration map (left) and phase evolution (right) of the point marked in Fig. 7, the phase in yellow is martensite, orange colour represents ferrite, and thered is austenite.

112 A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116

used in comparison with simulated results and fitting the model-ling parameters.

4.3.1. HeatingThe comparison of experimental and simulated results for the

heating process with the higher heating rate (of 400 �C/s) is shownin Fig. 7. This figure shows the change in austenite fraction duringheating period. The original martensite and ferrite phases trans-form into austenite phase. This figure indicates there is no obviousdeviation between two conditions with different peak tempera-tures. The marked points (a, b and c) are the beginning, middle,and end of the phase transformation, respectively. The carbon con-centration map and phase evolution of these three points are dis-played in Fig. 8. The value represented by the colour range is

displayed at the end of the figure. From white to blue means thedecrease of the carbon concentration value.

Austenite nucleates first in martensite phase. As temperatureincreases, the austenite nuclei grow up to the whole area and car-bon diffuses into ferrite. From Fig. 8, we can see clearly after theend of phase transformation; the carbon concentration is still inho-mogeneous. Carbon continues to diffuse in the following time withthe growing of austenite.

The final austenite grain structure at the peak temperatures ob-tained for each heating rates is shown in Fig. 9. From this figure, itcan be observed that for a fixed heating rate the austenite grainsize increases with an increase in the peak temperature. For exam-ple, comparing Fig. 9a with Fig. 9b, one can observe that for theheating rate of 400 �C/s the austenite grain size obtained for1350 �C is larger than that obtained for 1050 �C. Similarly, compar-

Fig. 9. Final austenite grain structures: (a) 400–1350 �C; (b) 400–1050 �C; (c) 100–1350 �C and (d) 100–1050 �C.

Fig. 10. Simulated start and end points of phase transformation with experimental CCT diagram, for the condition: heating rate 400 �C/s, peak temperature 1350 �C.

A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116 113

ing Fig. 9c with Fig. 9d, one can observe that for the heating rate of100 �C/s the austenite grain size obtained for 1350 �C is larger thanthat obtained for 1050 �C. Comparing Fig. 9a with Fig. 9c, it can be

observed that for the same peak temperature of 1350 �C, a largeraustenite grain size is obtained for a slower heating rate. The reasoncan be attributed to the fact that a slower heating rate provides

Fig. 11. Simulated start and end points of phase transformation with experimental CCT, for the condition: heating rate 100 �C/s, peak temperature 1350 �C.

Fig. 12. Simulated start and end points of phase transformation with experimental CCT, for the condition: heating rate 400 �C/s, peak temperature 1050 �C.

1 For interpretation of colour in Figs. 8, 10, 11 and 15, the reader is referred to theweb version of this article.

114 A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116

more time for the austenitic grain growth. However, this is not ob-served in the lower peak temperature of 1050 �C (see Fig. 9b and d).

4.3.2. CoolingFig. 10 shows the continuous cooling transformation diagram of

the condition: peak temperature 1350 �C and heating rate 400 �C/s,

with the simulated results of the start and end points of the phasetransformation (the blue and red1 points, respectively). The start

Fig. 13. Simulated start and end points of phase transformation with experimental CCT, for the condition: heating rate 100 �C/s, peak temperature 1050 �C.

A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116 115

and end temperatures of the simulation has small deviation from theexperiment. Fig. 11 gives the continuous cooling transformation dia-

Fig. 14. Modelling of phase transformation during cooling for cooling rate 250 �C/swith heating rate 400 �C/s, peak temperature 1350 �C.

Fig. 15. Phase evolution of the points marked in Fig. 14 under the condition

gram of the condition: peak temperature 1350 �C and heating rate100 �C/s, also with the simulated results of the start and end pointsof the phase transformation (again, the blue and red points,respectively).

The continuous cooling transformation diagram for the peaktemperature of 1050 �C and a heating rate of 400 �C/s, with thesimulated results of the start and end points of the phase transfor-mation, is shown below (Fig. 12). With the same procedure, Fig. 13is the CCT diagram with marked start and end points for a heatingrate of 100 �C/s and a peak temperature of 1050 �C. From thesecontinuous cooling transformation diagrams, we can see that fora high peak temperature situation (1350 �C) the final microstruc-tures are almost bainite. For a lower peak temperature condition,there is ferrite with phase fraction from 0% up to 60%, dependingon the cooling rate. From Fig. 12, we can see the final microstruc-ture contains a larger percentage of ferrite phase than that of ahigher peak temperature. Notably, the ferrite content reaches20% and 60% in the samples of two lower cooling rates. Therefore,it cannot be neglected.

The simulation of ferrite and bainite phases within one pro-gramme needs to consider more phase interactions. In the presentstudy, only austenite to bainite transformation is modelled (thecooling process with less than 5% ferrite content). The situation

: heating rate 400 �C/s, peak temperature 1350 �C, cooling rate 250 �C/s.

116 A. Ramazani et al. / Computational Materials Science 68 (2013) 107–116

of both austenite to ferrite and to bainite transformation needs tobe simulated in further work.

An example is shown on how the phase transformations weresimulated during cooling. Figs. 14 and 15 show the simulationresult of the condition: heating rate 400 �C/s, peak temperature1350 �C, cooling rate 250 �C/s. The simulated phase fractionchange of bainite and austenite is shown in Fig. 14. The modelledcurve replicates the experimental curve satisfactorily. Thereforethe modelled result can be used for investigating the phasetransformation during this process. Three points are chosen inthe curve, which are the beginning, middle and end of this trans-formation. The microstructures at these points are shown inFig. 15. The red phase represents austenite, the white phase isbainite.

5. Conclusions

1. The microstructure evolution obtained from phase field simula-tion gives an illustration of phase transformation of DP steelduring the welding process. Martensite and ferrite transforminto austenite during the heating cycle. The austenite graingrows after nucleation and decomposes into bainite phase orbainite and ferrite phases in the cooling cycle, depending onthe heat input. First, austenite nucleates from martensite; later,bainite nucleates from the austenite grain boundary.

2. The austenite/austenite interface mobility has a strong effecton the simulation result during fitting to the experimentalcurve. Small austenite/austenite interface mobility is used inthe modelling of 100 �C/s heating rate to reduce the formedaustenite grain size. The simulated start temperature of aus-tenite formation deviates from the experimental value. A pos-sible reason of this deviation is the linearized phase diagramdata used in MICRESS�. By the use of para-equilibrium phasediagram or data base coupling method, this deviation can beremoved. The simulation also shows that the carbon distribu-tion of the lower peak temperature condition remains inhomo-geneous at the peak temperature. The austenite grain size of100 �C/s heating rate is larger than that of 400 �C/s heatingrate.

3. The start temperature of bainite nucleation is determined bythe phase fraction–temperature curve obtained from the dila-tometer test. The bainite is considered as ferritic bainite phasein the simulation, with the assumption that no carbon precipi-tation takes place in bainite. The austenite/bainite interfacemobility, maximum nuclei number, and shield distance aremodified to fit the experimental and simulation results. Bainitetransformation becomes faster with an increase of the austen-ite/bainite mobility. This mobility used in the present work isof the magnitude of 10–5. A high mobility and large maximumbainite nuclei number is needed for high cooling rate condi-tions, like 350–150 �C/s. The case consisting of both bainiteand ferrite in the microstructure during cooling cycle needs toconsider more phase interaction information. Using a phase dia-gram with the information of ferrite/bainite interaction or database coupling method can be the solution of this kind of simu-lation by the use of MICRESS�.

Acknowledgements

The authors would like to acknowledge the funding and supportfrom AiF (Arbeitsgemeinschaft industrieller Forschungsvereinigun-gen). This research was carried out in the framework of Project AiF15548 N.

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