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Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis
International Journal of Heat and Mass Transfer 138 (2019) 729–737
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International Journal of Heat and Mass Transfer journal homepage: www.elsevier.com/locate/ijhmt
Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis Hunchul Jeong, Kyungbae Park, Sungjin Baek, Jungho Cho ⇑ School of Mechanical Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju 28644, Republic of Korea
a r t i c l e
i n f o
Article history: Received 3 April 2018 Received in revised form 18 April 2019 Accepted 18 April 2019 Available online 25 April 2019 Keywords: Aluminum Arc DCEP DCEN Flow-3d GTAW Thermal efficiency Reverse polarity Straight polarity Variable polarity Volume of fluid
a b s t r a c t One of several solutions to enhance the weldability of aluminum alloy is removing the aluminum oxide layer on the surface by adopting variable polarity arc welding. The authors’ previous experimental research on the variable polarity arc welding of aluminum alloy showed that reverse arc polarity has larger heat input efficiency than a straight polarity. Furthermore, the authors have already developed a numerical analysis model and suggested a methodology of estimating the heat input efficiency through a comparison between three dimensional molten pool simulation and experiment with regard to the variable polarity GTA welding. This research is a follow-up of the previous study, and it reveals the heat input efficiency of reverse polarity arc in aluminum welding for the first time through the suggested method. As a result, the heat input efficiency of reverse polarity is 3.8 times larger than that of straight polarity in aluminum welding. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction The difficulty in assuring the weld quality without any defects using only a conventional welding processes for aluminum alloys is well known. It is attributed to aluminum alloy’s high heat conductivity, thermal expansion coefficient, and hot crack susceptibility, as well as the generation of the porosities in the weldment based on the different hydrogen solubility between the solid and liquid phases [1] and the existence of an aluminum oxide (Al2O3) layer on the surface. Except for eliminating the aluminum oxide layer, there is a limitation in controlling these aspects because they are inherent physical properties or characteristics of the material itself. Consequently, one of the few solutions to increasing the aluminum weldability is to remove the oxide layer on the surface before or during welding. This can be achieved through a mechanical method using a wire brush, pickling, or an electrical method through a variable polarity (VP) arc source whose current has an alternative square wave pattern in the direct current electrode positive (DCEP) or reverse polarity and direct current electrode
⇑ Corresponding author. E-mail address: [email protected] (J. Cho). https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.089 0017-9310/Ó 2019 Elsevier Ltd. All rights reserved.
negative (DCEN) or straight polarity with a consistent period [2,3]. According to conventional theory with regard to the heat input into the base metal during variable polarity arc welding, it has been generally known that DCEN has higher heat input efficiency than DCEP polarity [4]. Recently, however, quite different experimental results have been reported [5–8]. Cho et al. persuasively described these phenomena with newly suggested theories, a combination of quantum tunneling effect and random walk of a cathode spot [8–10]. Information with regard to the heat input efficiency for AC polarity during aluminum welding has already been widely reported, and its values largely range from 0.21 to 0.83 [11]. However, such information is not distinguishable with respect to a single polarity value, and only a combined value of AC welding has been mentioned. Despite the necessity, it is quite difficult to directly measure the heat input efficiency with respect to single polarity in variable polarity welding through the experimental method using a calorimeter. It is rather easy to achieve a solution with help of a CFD-assisted method. Therefore the main purpose of this paper is to indirectly determine the heat input efficiency by comparing the simulation to the experiment. Its outline has already been suggested, the details of which can be found in the authors’ previous paper [12].
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Nomenclature B Bz c Cp F ! F gz h hA I J Jz K Ke Lf n P PA qVP qEP qEN rA T s TE Te Tli TN Tn TP
TS Ts Tso TW Tw T1 V ! V Vn x, y, z bi; bj; b k
magnetic flux density magnetic flux density in z direction thickness of base metal specific heat volume fraction occupied electromagnectic force gravitational acceleration in z direction enthalpy convection coefficient welding current current density current density in z direction thermal conductivity electrical conductivity latent heat of fusion subscript of normal component pressure arc pressure total energy given by variable polarity welding energy given during DCEP duration energy given during DCEN duration effective radius of arc temperature subscript of tangential component temperature in eastern centered cell temperature in eastern interfacial cell liquidus temperature temperature in northern centered cell temperature in northern interfacial cell temperature in current cell
temperature in southern centered cell temperature in southern interfacial cell solidus temperature temperature in western centered cell temperature in western interfacial cell ambient temperature welding voltage velocity vector field velocity in normal direction Cartesian coordinate axis unit vector in x, y, z direction
Greek symbols b volume thermal expansion coefficient c surface tension er emissivity gEP thermal efficiency during DCEP gEN thermal efficiency during DCEN l dynamic viscosity lm magnetic permeability of metal l0 magnetic permeability in vacuum m kinematic viscosity p pi, constant q density r normal stress rs Stefan-Boltzmann constant s shear stress ss shear stress in tangential direction sx shear stress in x direction sy shear stress in y direction
In a numerical analysis model, the fluid is assumed as incompressible, Newtonian, and laminar. The governing equations are the continuity equation, momentum equation, and energy equation. In addition to these three basic equations, the conservative governing equation of the fluid fraction is accompanied to track the free surface of the fluid, which is the well-known volume of fluid (VOF) method. The analysis model is completed through the adoption of several arc welding characteristics such as the arc heat flux, arc pressure, Marangoni flow, buoyancy, and electromagnetic force as boundary conditions or body force terms. The mathematical models applied to this research have already been verified in [13–20]. The previously verified 3-D numerical analysis model can be used to conduct temperature and velocity field analyses and free-surface tracking in each cell by solving the four governing equations above through the commercial package, Flow-3D. Except for the governing equations, all arc characteristics are reflected by user subroutines. 2. Governing equations and boundary conditions
As mentioned before, it is assumed that the fluid for this analysis is regarded as an incompressible, Newtonian, and laminar flow. Based on these assumptions, the continuity equation of the mass conservation is expressed as
!
r V ¼0 The momentum equation is also expressed as [22]
! ! DV 1 1 ! ! ¼ rP þ mr2 V þ g Z ½1 bðT T li Þ þ J B Dt q q
ð1Þ
ð3Þ
where the buoyancy force owing to the thermal expansion fluid in the weld molten pool is considered in the third term of right side in Eq. (3), which is often referred to as a Boussinesq approximation. The last term of Eq. (3) represents electromagnetic force which is described in detail through Eqs. (4) and (5). All the equations are according to schematic diagram of analytic domain seen in Fig. 1. The most important aspect affecting the weld molten pool behavior is the electromagnetic force through a combination of the current density that passes through the molten pool, and its
There are four governing equations used to realize the virtual arc welding process. The first is the VOF method, which estimates the volume fraction in each cell. The VOF technique is based on the variable F, a scalar denoting the volume fraction of fluid occupying each cell, which follows the conservation law. By definition, the value of F is defined as zero in a void cell, and 1 in a fully occupied cell; otherwise, a value between zero and 1 is applied, which is recognized as free surface elements. The conservation of F is expressed as [21]
! DF @F ¼ þr VF ¼0 Dt @t
ð2Þ
Fig. 1. Schematic diagram of analytic domain and welding direction.
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737
resultant self-induced magnetic field, which plays a role as a body force in the weld molten pool. Basically, the Lorentz force per unit of volume is represented as a cross product of the current density ! ! vector J and magnetic field vector B .
! ! ! F ¼ J B
ð4Þ
When electrons are transmitted from cathode to anode, they are always emitted or absorbed in a normal direction, i.e., the current density direction is normal to the anode or cathode surface. However, the weld pool surface always changes, and it therefore an extremely complex problem occurs if the electromagnectic force is considered in the normal direction along the free surface at every single moment. Therefore, previous studies on the electromagnetic force during arc welding have created a simple model under the assumption that a flat top surface is not a free surface. Detailed descriptions for such assumption are as follows. (1) The arc current absorbed to the weld pool surface flows in only the normal direction (z-axis) on the x-y plane regardless of the wave pattern of the pool. (2) The electrical conductivity and magnetic permeability are considered as constants.
@K e @ lm ¼ ¼0 @T @T (3) The current flux density according to the Gaussian distribution is as follows:
! I r2 b k Jz¼ exp 2 2 2p r A 2rA
Based on these several assumptions, its resultant threedimensional electromagnetic force model is simply derived in a Cartesian coordinate system as [23]
" # ! I 2 lm r2 r2 z2 x b 1 exp 1 i F ¼ 2 2 exp c r 4p rr A 2rA 2 2r A 2 " # I2 l r2 r2 z2 y b 1 exp 1 j þ 2 m 2 exp c r 4p rr A 2rA 2 2r A 2 " # 2 I 2 lm r2 z b k þ 1 1 exp c 4p2 r 2 c 2rA 2
ð5Þ It is important to apply the energy conservation equation to accurately estimate the temperature distribution during welding. The related energy equation becomes the following.
! dh 1 þ r V h ¼ r ðK rT Þ dt q
ð6Þ
h ¼ C p T þ f ðT Þ L f
ð7Þ
f ðT Þ ¼
8 > < > :
0
if T T so
T T so =T li T so ; if T so < T < T li 1
if T T li
where the latent heat of fusion, Lf , is divided into three cases with regard to f ðT Þ , the fraction of liquid phase between solidus and liquidus temperature, namely, a solid state, mushy zone, and liquid state in a weld pool. The top surface upon which the heat source is directly imposed is the region where the extreme heat flux is accompanied by thermal convection and radiation emitted by the temperature itself. In addition, it is assumed that all gradients of the physical properties in areas other than the top surface will be simply zero because the size of the actual base metal used in the experiment is too large to include it in the simulation domain owing to an unnecessary loss of time, and hence comparatively unimportant boundaries in terms of the heat transfer, except for the top surface, were dealt with using continuative boundary conditions. The numerical expression of this becomes the following.
@U ¼0 @n
ð8Þ
ð9Þ
where U indicates any kind of physical properties, and n denotes the normal direction on each plane. Basically, the heat loss by evaporation on the top surface of the weldment should be considered. However, it was assumed to be negligible because the arc welding has little influence on the evaporation compared to a high-power density welding process, such as laser beam, plasma, and electron beam welding. Therefore, the boundary condition of the top surface for a heat transfer considers the arc heat flux, heat convection, and radiational heat loss. This is expressed as
K
(4) The magnetic flux density in the radial and height directions in a cylindrical coordinate system is assumed to be negligible. (5) The current density that spreads in the radial direction in the weld pool will be equal on average throughout the entire thickness of the base metal.
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@T ¼ qVP hA ðT T 1 Þ rs er T 4 T 41 @n
ð10Þ
In Eq. (10) above, the second and third terms on the right-hand side indicate the convectional and radiational heat loss, respectively. The first term, qVP , on the right-hand side denotes the arc heat flux whose fluctuating patterns in each DCEP and DCEN polarities are periodically repeated, which is classified into qEP , heat flux during DCEP duration and qEN , heat flux during DCEN duration. For this reason, the thermal efficiency was applied differently to each polarity through a mathematical model such as the following.
2 VI x þ y2 qVP ¼ qEN þ qEP ¼ gEN þ gEP exp 2r 2A 2pr 2A
ð11Þ
where gEN denotes the arc thermal efficiency of the DCEN polarity, and gEP is for DCEP. It means that the heat is supplied with non-zero gEN during DCEN period, while gEP is zero at this time. It acts vice versa for DCEP period. In this research, a strategy to determine the thermal efficiency in each variable polarity is established by adapting the method to compare the FZ areas of the simulation with an experiment, which is another alternative for estimating the amount of heat in the base metal. As indicated in the Introduction, an experimental paper showed that the longer the DCEP duration that is applied, the larger FZ area. Therefore, if we can match the proportional constant between the DCEP duty ratio and the FZ area, we can determine the efficiencies in each polarity. In Eq. (11), V, I, and rA denote the voltage, current, and effective arc radius, respectively. During the arc welding process, the molten pool surface has two kinds of pressure boundary conditions. One is pressure by surface tension, referred to as the Laplace pressure, and the other is arc pressure through an arc plasma flow [24]. Mathematical expressions of this pressure boundary conditions on the top surface can be expressed as follows:
P þ 2l
@V n c ¼ PA þ @n RC
ð12Þ
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PA ðx; yÞ ¼
l0 I2 x2 þ y2 exp 2 2 4p rA 2r 2A
ð13Þ
Eq. (12) describes the pressure boundary condition at the top surface, and n denotes the normal directional component. The second term on the left-hand side in Eq. (12) means the normal stress on the weld pool based on Newton’s viscosity law. In the same equation, the second term on the right-hand side describes the pressure through surface tension c, and thus Rc denotes the local surface curvature. The first term on the right-hand side is the arc pressure, and is described through Eq. (13). One of the most interesting features in an arc weld pool simulation is the fact that the metal surface is locally melted, and the resultant temperature gradient through a Gaussian heat distribution therefore has quite a significant role. Even a slight difference in temperature gradient on the weld pool surface can cause a strong shear force, which is referred to as a Marangoni flow, which has been newly reflected in the user-subroutine through coding. Eq. (14) describes the boundary condition through a Marangoni flow.
ss ¼
dc rT dT
ð14Þ
where subscript s, ss , and dc=dT denote the tangential component, tangential stress on the top surface of the weld pool, and the surface tension gradient, given as the thermo-physical property, respectively. In practice, the top surface of a weld pool has a fluctuating wave pattern, which means there is a large fractional z-directional temperature gradient component in the direction perpendicular to the x-y plane, as shown in Fig. 1. Hence, its effect in the zdirection is negligible because the heat flux distribution in the welding process is generally in accordance with the twodimensional Gaussian distribution on top weld pool surface, which leads to an extreme temperature gradient within the effective radius in the x-y plane regardless of the z-direction. For this reason, it rarely influences the top surface in the vertical direction. Its related mathematical model can be expressed in the Cartesian coordinate system as
dc @T dT @x
ð15Þ
sy ¼
dc @T dT @y
ð16Þ
Fig. 2 shows the mesh grid system used to explain the processes for transforming Eqs. (15) and (16) into the discrete Eqs. (17) and (18) using the FVM. Here, each character, T, denotes each quadrant temperature away from current cell, T P . Moreover, E-e, W-e, S-s, and N-n indicate the eastern, western, southern, and northern locations, respectively. The upper and lower case characters, T, represent the temperature of the center and the interface of the cell, respectively. If accompanied by a mathematical technique integrating Eqs. (15) and (16) with dx from w to e, and dy from s to n, the results for the transformation into the discretization equations can be derived as n
Z
s
e
sx dxdy ¼
w
F x ¼ sx DxDy ¼ Te ¼
Z
n
Z
s
e
sy dxdy ¼
w
F y ¼ sy DxDy ¼ Tn ¼
dc dT
Z
n
s
Z w
e
@T dxdy @y
ð18Þ
dc ðT n T s ÞDx dT
TP þ TN TP þ TS ; Ts ¼ 2 2
where each interfacial temperature, T e ; T w ; T s ; andT n , is derived through the central differential scheme, which is determined based on the average of the adjacent cells in the x or y direction. 3. Procedures of the experiment and simulation
sx ¼
Z
Fig. 2. The adjacent temperature distributions near current cell for discretization in Cartesian coordinate system.
dc dT
Z
n
s
Z
e
w
@T dxdy @x
dc ðT e T w ÞDy dT
TP þ TE TP þ TW ; Tw ¼ 2 2
ð17Þ
The base material used in both the experiment and simulation is a commercial wrought aluminum plate, A6061-T6, which is the most commonly used throughout all industries. Its related thermophysical properties are listed in Table 1. The welding type used for the experiment is an inverter GTA welding machine, and the joint type is a bead-on-plate (BOP) mode. The welding parameters for the experiment and simulation are summarized in Table 2. A three-dimensional analytic domain was set up with cubic cells 0.5 mm in width, depth, and height. The top surface is open to the air, i.e., a free surface, and it faces the arc heat flux, with convectional and radiational heat loss. The other surfaces are set as having a ‘‘continuative boundary condition,” which means a gradient of any kind in the thermophysical properties at each boundary is zero, as described in Eq. (9). It is very difficult to determine both thermal efficiencies in the DCEP and DCEN simultaneously using only an analytical method. It will therefore be necessary to determine which one should be fixed; we decided to fix DCEN at 0.25 and use variables with five levels of relative ratios in DCEP, as shown in Table 2. Recall that the main purpose of this study is simply to determine the relative thermal efficiency ratio instead of the absolute value. The reason for setting the thermal efficiency during the DCEN at nearly the minimum value in this simulation is based on recent studies indicating that the longer the DCEN duty ratio is, the smaller heat input into the base material. Additionally, it is due to a limitation in the
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737 Table 1 Thermophysical properties of the A6061-T6. Property
Value
Symbol
Density Thermal conductivity
2705 kgm
q
195 Jm1 s1 K1
K
Specific heat
S: 847 Jkg1 K1
Cp
L: 870 Jkg1 K1 873 K 915 K
T so T li
3.5104 Nm1 K1 0.0025 m
rA
Solidus temp. Liquidus temp. Surface tension gradient Arc radius Magnetic permeability Ambient temperature Latent heat fusion Emissivity Surface tension Convection coefficient Dynamic viscosity
3
1.26106 Hm1 298 K 336 kJkg1 0.2 0.89 Nm1 80 Jm2 s1 K1 0.00115 kg s1 m1
dc dT
lm
T1 Lf
er c
hA
l
L: Liquid/S: Solid.
Table 2 Welding parameters for experiment and simulation. Parameter
Value
Material size Current Welding speed Arc length Gas type Gas flow rate Electrode Switching freq. Thermal Efficiency in DCEN Thermal efficiency in DCEP (5 levels) DCEP duty ratio (4 levels)
300 100 10 mm 280 A 0.0015 ms1 0.0032 m 99% Ar 23.0 Lmin1 2%Th + W £4.0 mm 100 Hz 0.25 0.55/0.65/0.75/0.85/0.95 15%/25%/35%/45%
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fixed at 100 Hz based on a previous study that revealed that a switching frequency of several hundreds of Hz does not affect the weld quality. A criterion of the assessment of the heat input capacity into the base material by each DCEP duty ratio was arranged through computations in the FZ area. In the experimental study, the welding duration lasted for 80 s with a specific travel speed of 1.5 mm/s, and the cutoff position for computing its transverse crosssectional FZ area was then determined as 100 mm away from the welding start point under sufficient consideration of the quasisteady state. A schematic diagram of this is depicted in Fig. 3. Unlike in the experiment, the welding duration time and size of the base metal during the simulation were reasonably reduced to save time and enhance the convergency without any difference shown in the analytical results. Accordingly, the total analytical domain was set up using 82 mm 32 mm 14 mm, with a top vacancy of 4 mm as a void space for a free surface expression, namely, an F value of zero. Strictly speaking, the dynamic welding process occurs under a transient state, and not a stationary one. Thus, it should be carefully considered when selecting a plane to estimate the FZ areas. During this simulation, the chosen plane was placed sufficiently away from the arc start point, guaranteeing that the weld pool would be fully developed as a quasi-steady state. Under these considerations, the analytical welding duration lasted only for 50 s under the same welding conditions used in the experiment, and the cut-off plane for investigating the transverse cross-sectional FZ areas was decided as being 60 mm from the welding start point, where its quasi-steady state was then completely confirmed. Fig. 4 shows an example of the method used to estimate the transverse cross-sectional FZ areas using a computation in which the number of pixels is multiplied by the pixel resolution.
4. Results and discussion thermal electrons’ absorption into the base metal emitted from the electrode by aluminum oxide on the surface of the base metal, i.e., the DCEN shows a poor performance in terms of the penetration of thermions into the base material. An important variable in addition to the arc current, voltage, and welding speed in the welding process is the DCEP duty ratio, which is the portion of DCEP duration to a single pulse period. Its setting values range from 15% to 45% with 10% unit intervals. The switching frequency, i.e., the reverse of the pulse period, is
4.1. Experimental results The load currents and load voltages based on each DCEP duty ratio were measured using an oscilloscope and a voltmeter, respectively, during the welding process. Fig. 5 shows the current waveform patterns, all of which were maintained at nearly 280 A regardless of the DCEP duty ratio. One of the highly interesting aspects discovered during the experiment is the considerable variation in load voltage by the
Fig. 3. Schematic diagram representing welding procedure in experiment.
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Fig. 4. Procedure for computation of FZ area in weldment.
Fig. 5. Currents measured with oscilloscope during welding by duty ratio of the DCEP (a) DCEP duty ratio 15% (b) DECP duty ratio 25% (c) DCEP duty ratio 35% (d) DCEP duty ratio 45%.
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737
DCEP duty ratio, and its certain regularity with a consistent pattern, i.e., the load voltage tends to decrease by increasing the DCEP duty ratio, as shown in Fig. 6. A special reason for this phenomenon could be cautiously explained. As mentioned before, the power source used in this experiment is an inverter GTA welding machine whose current is kept constant, and its load voltage is automatically adjusted until the load current reaches the current value, which is already determined. According to a reference paper with regard to the investigation of phenomena that occur in a weld pool using a high-speed camera in the variable polarity arc welding of aluminum alloy [25], it was found that the amount of removed oxide layer on the aluminum surface is remarkably reduced as the DCEP duty ratio decreases. A notable voltage drop might occur near the oxide layer on the top surface of the base material because it acts as a resistance when the current passes through the top surface. At that time, the aluminum oxide layer on the aluminum plate interrupts the current smoothly carrying into the aluminum base material. That is, a higher voltage is required to pass through because much more of the oxide layer is left on the aluminum top surface without a sufficient elimination in the case of a lower DCEP duty ratio. Fig. 7-(a) shows transverse cross-sectional images of a quasisteady state region, and a graph representing the comparisons through a simple power computation regardless of the thermal efficiency at each polarity, i.e., simply multiplying the welding
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voltage and current, is shown in Fig. 7-(b). Here, the computational values of arc power and FZ areas acquired through an experimental study are applied to units on the left and right vertical axes, respectively. As shown in Fig. 7-(a), the FZ areas remain steady up to 35% DCEP duty ratio, but increase dramatically at 45%. On the other hand, a curve representing the welding power, while not considering each thermal efficiency, is inversely proportional to the FZ area curve. In summary, FZ areas increase despite the arc power decrease. This directly infers that a higher heat input enters the base material during the DCEP period than during the DCEN period. Accordingly, it is very reasonable to take higher analytical variables in the DCEP period compared to the unchangeable 0.25 in the DCEN period, with regard to the thermal efficiency shown in Table 2. 4.2. Comparison between experimental and simulation results An indirect method was adopted to predict the relative thermal efficiency ratio, which compares the FZ areas between the experimental and simulation results as a part in estimating how much heat input would come into the base material. However, it takes a long time to apply all kinds of options (a total of 20 cases, namely, the DCEP ratio with four levels, and the thermal efficiency in DCEP with five levels) listed in Table 2. Hence, contrasting a graph pattern of an experiment-dependent FZ area with those through the simple calculations below was implemented as a preference, and was then sorted into potential candidates based on the consistency.
ðgEN f EN þ gEP f EP Þ jVIj
ð19Þ
f EN þ f EP ¼ 1
Fig. 6. Load voltage by each DCEP duty ratio.
where f EN ; f EP ; I; and V indicate the duty ratio in DCEN, duty ratio in DCEP, welding current, and welding voltage, respectively. In addition, the welding power was taken as an absolute value because heat calories only have a positive sign, and it was confirmed that the absolute value of the load current and voltage are kept at a constant regardless of the duty ratio during the welding process. Fig. 8 shows the outcomes. Graphs from cases 1 through 3 usually tend to decrease by increasing the DCEP duty ratio: the graph of case 4
Fig. 7. The comparison of graph patterns between FZ area and welding power regardless consideration of the thermal efficiency (a) transverse cross sectional images (b) a resultant graph by DCEP duty ratio.
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Fig. 8. The graph patterns by each case with regard to thermal efficiency in DCEP.
shows a constant pattern, and finally, the graph in case 5 is quite similar to the FZ area acquired through the experiment, as shown in Fig. 7-(b). For this reason, case 5 is a strong candidate to determine the relative thermal efficiency ratio. Among the different cases, a comparison of the FZ areas between the experiment and simulation was performed for only case 5 to determine whether the results match each other while reducing the loss of time during the simulation. Their resultant outputs are summarized with the cross-sectional images and graphs in Fig. 9. Here, distinguished three zones of the weldment denote the FZ, HAZ, and base materials from the top center to outer area, respectively, and the subscript values in Fig. 9-(a) indicate the FZ area. Although it is difficult to clarify the definition of the HAZ in aluminum welding when not considering the cooling rate, it might be regarded that the region whose temperature reaches 653 K is
HAZ; this is the temperature at which the transformation from a b00 -phase to a b0 -phase occurs in the Al-Mg-Si alloy (6XXX aluminum alloy series) during the process of over-aging [26]. As shown in Fig. 9, the FZ area remained fairly constant over the range of 15% to 35%, and showed a sharp increase at 45% in both the simulation and experimental results. It is obvious that a much higher thermal efficiency will be given in DCEP than in DCEN by acquiring entirely similar values for the FZ areas shown in Fig. 9(b). Based on these results, the thermal efficiency in DCEP and DCEN will be assumed as 0.95 and 0.25, respectively, with a ratio of about 3.8 times. Hence, this implies that the physical phenomena occurring at each polarity are obviously dissimilar to each other. It was already confirmed that the load voltage during welding tends to be reduced when increasing the DCEP duty ratio, as shown in Fig. 6,
Fig. 9. The comparisons of FZ areas between the experiment and simulation in case-5 (a) transverse cross sectional images (b) a resultant graph pattern.
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737
and it seems that this is deeply correlated with the remaining aluminum oxide layer on the surface. This can probably be explained through the combination of the quantum tunneling effect and a random walk of the cathode spot, which occurred during the DCEP period in a recent study by Cho [8–10], i.e., these two mechanisms not only have a much more concentrated heat flux but also simultaneously more heat input to be penetrated into the base material by easily eliminating the oxide layer near the surface during the DCEP period. Consequently, the heat input amount during a variable polarity GTA aluminum welding process depends on oxidization characteristics of the material, DCEP duty ratio, and their interactions. 5. Conclusions With regard to aluminum variable polarity GTA welding, it was recently reported that the FZ area tends to increase through an increase in the DCEP duty ratio. It is completely opposite from what we commonly know based on conventional arc theory through which the thermal effect is more exceptional for DCEN than for DCEP because thermal electrons having a high temperature are continuously emitted from a tungsten electrode, and then penetrate into the base material during the DCEN period, which is considered to be natural. Therefore, such recent researches have generated extreme confusion. To resolve this contradiction, two types of new mechanisms were employed, and such opposite results were clearly explained in this study. Hence, higher thermal efficiencies in DCEP than in DCEN were set up as variables based on this fundamental physical characteristic, and its relative thermal efficiency ratio was finally estimated through a comparison of the FZ area between the numerical analysis model and an experiment, the resultant value of which is 3.8. Conflict of interest The authors declared that there is no conflict of interest. Acknowledgement This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning, Korea (Grant No. 2016R1D1A1B03935036) and Technology Innovation Industrial Program funded by the Ministry of Trade, Industry & Energy, Korea (Grant No. 10052793 and Grant No. 10077517).
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References [1] L.W. Eastwood, Gases in Non-Ferrous Metals and Alloys, American Society for Metals, Cleveland, OH, 1953. [2] J. Cho, Variable polarity plasma arc (VPPA) welding Part 1. Introduction and theoretical background, J. Korean Weld. Join. Soc. 30 (2012) 199–201. [3] J. Cho, Variable polarity plasma arc (VPPA) welding Part 2. Applications and welding physics, J. Korean Weld. Join. Soc. 30 (2012) 291–293. [4] H. Patte, R. Meister, R. Monroe, Cathodic cleaning and plasma arc welding of aluminum, Weld. J. 47 (5) (1968) 226s–1223s. [5] R. Sarrafi, R. Kovacevic, Cathodic cleaning of oxides from aluminum surface by variable-polarity arc, Weld. J. 89 (2010) 1s–10s. [6] F. Li, Z. Yu, X. Xiao, X. Hua, Y. Wu, Research of cathode cleaning and weld formation of AC TIG welding of aluminum alloy, The 2nd East Asia Symposium on Technology, Sep 26–27, Nara, Japan, 2012. [7] M.A.R. Yarmuch, B.M. Patchett, Variable AC polarity GTAW fusion behavior in 5083 aluminum, Weld. J. 86 (2007) 196s–200s. [8] J. Cho, Weldability increase of aluminum by variable polarity arc, J. Korean Weld. Join. Soc. 32–1 (2014) 108–111. [9] J. Cho, J. Lee, S. Bae, Heat input analysis of variable polarity arc welding of aluminum, Int. J. Adv. Manuf. Technol. 81 (2015) 1273–1280. [10] J. Cho, J. Lee, S. Bae, Y. Lee, K. Park, Y. Kim, J. Lee, Theoretical background discussion on variable polarity arc welding of aluminum, J. Korean Weld. Join. Soc. 2 (2015) 14–17. [11] N. Stenbacka, I. Choquet, K. Hurtig, Review of arc efficiency values for gas tungsten arc welding, in: IIW Commision IV-XII-SG212 Intermetdiate Meeting BAM, Berlin, Germany, 2012. [12] H. Jeong, K. Park, J. Cho, Numerical analysis of variable polarity arc weld pool, J. Mech. Sci. Technol. 30–9 (2016) 4307–4313. [13] J. Cho, An Analysis of Three-Dimensional Molten Pool in Laser-GMA Hybride Welding Ph.D. Thesis, KAIST, Korea, 2007. [14] J. Cho, D.F. Farson, J.O. Milewski, K.J. Hollis, Weld pool flows during initial stages of keyhole formation in laser welding, J. Phys. D: Appl. Phys. 42 (2009). [15] J. Cho, S.J. Na, Implementation of real-time multiple reflection and Fresnel absorption of laser beam in keyhole, J. Phys. D Appl. Phys. 39 (2006) 5372– 5378. [16] J. Cho, S.J. Na, Theoretical analysis of keyhole dynamics in polarized laser drilling, J. Phys. D Appl. Phys. 40 (2007) 7638–7647. [17] J. Cho, S.J. Na, Three-dimensional analysis of molten pool in GMA-laser hybrid welding, Weld. J. 88 (2009) 35s–43s. [18] J. Cho, D.F. Farson, K.J. Hollis, J.O. Milewski, Numerical analysis of weld pool oscillation in laser welding, J. Mech. Sci. Technol. 29 (4) (2015) 1715–1722. [19] Y.C. Lim, X. Yu, J. Cho, J. Sosa, D.F. Farson, S.S. Babu, S. McCracken, B. Flesner, Effect of magnetic stirring on grain structure refinement Part 1 - Autogenous nickel alloy welds, Sci. Technol. Weld. Joining 15 (2010) 583–589. [20] Y.C. Lim, X. Yu, J. Cho, J. Sosa, D.F. Farson, S.S. Babu, S. McCracken, B. Flesner, Effect of magnetic stirring on grain structure refinement Part 2 - Nickel alloy weld overlays, Sci. Technol. Weld. Joining 15 (2010) 400–406. [21] C.W. Hirt, B.D. Nichols, Volume of fluid method for the dynamics of free boundaries, J. Comput. Phys. 39 (1981) 201s–225s. [22] R.W. Fox, A.T. McDonarld, Introduction to Fluid Mechanics, fourth ed., Wiley, New York, 1992. [23] A. Kumar, T. Debroy, Calculation of three-dimensional electromagnetic force field during arc welding, J. Appl. Phys. 94 (2003) 1267–1277. [24] Z. Cao, Z. Yang, X.L. Chen, Three-dimensional simulation of transient GMA weld pool with free surface, Weld. J. 83 (2004) 169s–176s. [25] J. Lee, Heat Input Mechanism Elucidation through Observation of Cathode Spot Phenomena in Variable Polarity Aluminum Arc Welding Thesis for degree of master, Chungbuk National University, Korea, 2016. [26] O.R. Myhr, Ø. Grong, H.G. Fjaer, C.D. Marioara, Modeling of the microstructure and strength evolution in Al-Mg-Si alloys during multistage thermal processing, Acta Mater. 52–17 (2004) 4997–5008.
Contents lists available at ScienceDirect
International Journal of Heat and Mass Transfer journal homepage: www.elsevier.com/locate/ijhmt
Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis Hunchul Jeong, Kyungbae Park, Sungjin Baek, Jungho Cho ⇑ School of Mechanical Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju 28644, Republic of Korea
a r t i c l e
i n f o
Article history: Received 3 April 2018 Received in revised form 18 April 2019 Accepted 18 April 2019 Available online 25 April 2019 Keywords: Aluminum Arc DCEP DCEN Flow-3d GTAW Thermal efficiency Reverse polarity Straight polarity Variable polarity Volume of fluid
a b s t r a c t One of several solutions to enhance the weldability of aluminum alloy is removing the aluminum oxide layer on the surface by adopting variable polarity arc welding. The authors’ previous experimental research on the variable polarity arc welding of aluminum alloy showed that reverse arc polarity has larger heat input efficiency than a straight polarity. Furthermore, the authors have already developed a numerical analysis model and suggested a methodology of estimating the heat input efficiency through a comparison between three dimensional molten pool simulation and experiment with regard to the variable polarity GTA welding. This research is a follow-up of the previous study, and it reveals the heat input efficiency of reverse polarity arc in aluminum welding for the first time through the suggested method. As a result, the heat input efficiency of reverse polarity is 3.8 times larger than that of straight polarity in aluminum welding. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction The difficulty in assuring the weld quality without any defects using only a conventional welding processes for aluminum alloys is well known. It is attributed to aluminum alloy’s high heat conductivity, thermal expansion coefficient, and hot crack susceptibility, as well as the generation of the porosities in the weldment based on the different hydrogen solubility between the solid and liquid phases [1] and the existence of an aluminum oxide (Al2O3) layer on the surface. Except for eliminating the aluminum oxide layer, there is a limitation in controlling these aspects because they are inherent physical properties or characteristics of the material itself. Consequently, one of the few solutions to increasing the aluminum weldability is to remove the oxide layer on the surface before or during welding. This can be achieved through a mechanical method using a wire brush, pickling, or an electrical method through a variable polarity (VP) arc source whose current has an alternative square wave pattern in the direct current electrode positive (DCEP) or reverse polarity and direct current electrode
⇑ Corresponding author. E-mail address: [email protected] (J. Cho). https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.089 0017-9310/Ó 2019 Elsevier Ltd. All rights reserved.
negative (DCEN) or straight polarity with a consistent period [2,3]. According to conventional theory with regard to the heat input into the base metal during variable polarity arc welding, it has been generally known that DCEN has higher heat input efficiency than DCEP polarity [4]. Recently, however, quite different experimental results have been reported [5–8]. Cho et al. persuasively described these phenomena with newly suggested theories, a combination of quantum tunneling effect and random walk of a cathode spot [8–10]. Information with regard to the heat input efficiency for AC polarity during aluminum welding has already been widely reported, and its values largely range from 0.21 to 0.83 [11]. However, such information is not distinguishable with respect to a single polarity value, and only a combined value of AC welding has been mentioned. Despite the necessity, it is quite difficult to directly measure the heat input efficiency with respect to single polarity in variable polarity welding through the experimental method using a calorimeter. It is rather easy to achieve a solution with help of a CFD-assisted method. Therefore the main purpose of this paper is to indirectly determine the heat input efficiency by comparing the simulation to the experiment. Its outline has already been suggested, the details of which can be found in the authors’ previous paper [12].
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Nomenclature B Bz c Cp F ! F gz h hA I J Jz K Ke Lf n P PA qVP qEP qEN rA T s TE Te Tli TN Tn TP
TS Ts Tso TW Tw T1 V ! V Vn x, y, z bi; bj; b k
magnetic flux density magnetic flux density in z direction thickness of base metal specific heat volume fraction occupied electromagnectic force gravitational acceleration in z direction enthalpy convection coefficient welding current current density current density in z direction thermal conductivity electrical conductivity latent heat of fusion subscript of normal component pressure arc pressure total energy given by variable polarity welding energy given during DCEP duration energy given during DCEN duration effective radius of arc temperature subscript of tangential component temperature in eastern centered cell temperature in eastern interfacial cell liquidus temperature temperature in northern centered cell temperature in northern interfacial cell temperature in current cell
temperature in southern centered cell temperature in southern interfacial cell solidus temperature temperature in western centered cell temperature in western interfacial cell ambient temperature welding voltage velocity vector field velocity in normal direction Cartesian coordinate axis unit vector in x, y, z direction
Greek symbols b volume thermal expansion coefficient c surface tension er emissivity gEP thermal efficiency during DCEP gEN thermal efficiency during DCEN l dynamic viscosity lm magnetic permeability of metal l0 magnetic permeability in vacuum m kinematic viscosity p pi, constant q density r normal stress rs Stefan-Boltzmann constant s shear stress ss shear stress in tangential direction sx shear stress in x direction sy shear stress in y direction
In a numerical analysis model, the fluid is assumed as incompressible, Newtonian, and laminar. The governing equations are the continuity equation, momentum equation, and energy equation. In addition to these three basic equations, the conservative governing equation of the fluid fraction is accompanied to track the free surface of the fluid, which is the well-known volume of fluid (VOF) method. The analysis model is completed through the adoption of several arc welding characteristics such as the arc heat flux, arc pressure, Marangoni flow, buoyancy, and electromagnetic force as boundary conditions or body force terms. The mathematical models applied to this research have already been verified in [13–20]. The previously verified 3-D numerical analysis model can be used to conduct temperature and velocity field analyses and free-surface tracking in each cell by solving the four governing equations above through the commercial package, Flow-3D. Except for the governing equations, all arc characteristics are reflected by user subroutines. 2. Governing equations and boundary conditions
As mentioned before, it is assumed that the fluid for this analysis is regarded as an incompressible, Newtonian, and laminar flow. Based on these assumptions, the continuity equation of the mass conservation is expressed as
!
r V ¼0 The momentum equation is also expressed as [22]
! ! DV 1 1 ! ! ¼ rP þ mr2 V þ g Z ½1 bðT T li Þ þ J B Dt q q
ð1Þ
ð3Þ
where the buoyancy force owing to the thermal expansion fluid in the weld molten pool is considered in the third term of right side in Eq. (3), which is often referred to as a Boussinesq approximation. The last term of Eq. (3) represents electromagnetic force which is described in detail through Eqs. (4) and (5). All the equations are according to schematic diagram of analytic domain seen in Fig. 1. The most important aspect affecting the weld molten pool behavior is the electromagnetic force through a combination of the current density that passes through the molten pool, and its
There are four governing equations used to realize the virtual arc welding process. The first is the VOF method, which estimates the volume fraction in each cell. The VOF technique is based on the variable F, a scalar denoting the volume fraction of fluid occupying each cell, which follows the conservation law. By definition, the value of F is defined as zero in a void cell, and 1 in a fully occupied cell; otherwise, a value between zero and 1 is applied, which is recognized as free surface elements. The conservation of F is expressed as [21]
! DF @F ¼ þr VF ¼0 Dt @t
ð2Þ
Fig. 1. Schematic diagram of analytic domain and welding direction.
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737
resultant self-induced magnetic field, which plays a role as a body force in the weld molten pool. Basically, the Lorentz force per unit of volume is represented as a cross product of the current density ! ! vector J and magnetic field vector B .
! ! ! F ¼ J B
ð4Þ
When electrons are transmitted from cathode to anode, they are always emitted or absorbed in a normal direction, i.e., the current density direction is normal to the anode or cathode surface. However, the weld pool surface always changes, and it therefore an extremely complex problem occurs if the electromagnectic force is considered in the normal direction along the free surface at every single moment. Therefore, previous studies on the electromagnetic force during arc welding have created a simple model under the assumption that a flat top surface is not a free surface. Detailed descriptions for such assumption are as follows. (1) The arc current absorbed to the weld pool surface flows in only the normal direction (z-axis) on the x-y plane regardless of the wave pattern of the pool. (2) The electrical conductivity and magnetic permeability are considered as constants.
@K e @ lm ¼ ¼0 @T @T (3) The current flux density according to the Gaussian distribution is as follows:
! I r2 b k Jz¼ exp 2 2 2p r A 2rA
Based on these several assumptions, its resultant threedimensional electromagnetic force model is simply derived in a Cartesian coordinate system as [23]
" # ! I 2 lm r2 r2 z2 x b 1 exp 1 i F ¼ 2 2 exp c r 4p rr A 2rA 2 2r A 2 " # I2 l r2 r2 z2 y b 1 exp 1 j þ 2 m 2 exp c r 4p rr A 2rA 2 2r A 2 " # 2 I 2 lm r2 z b k þ 1 1 exp c 4p2 r 2 c 2rA 2
ð5Þ It is important to apply the energy conservation equation to accurately estimate the temperature distribution during welding. The related energy equation becomes the following.
! dh 1 þ r V h ¼ r ðK rT Þ dt q
ð6Þ
h ¼ C p T þ f ðT Þ L f
ð7Þ
f ðT Þ ¼
8 > < > :
0
if T T so
T T so =T li T so ; if T so < T < T li 1
if T T li
where the latent heat of fusion, Lf , is divided into three cases with regard to f ðT Þ , the fraction of liquid phase between solidus and liquidus temperature, namely, a solid state, mushy zone, and liquid state in a weld pool. The top surface upon which the heat source is directly imposed is the region where the extreme heat flux is accompanied by thermal convection and radiation emitted by the temperature itself. In addition, it is assumed that all gradients of the physical properties in areas other than the top surface will be simply zero because the size of the actual base metal used in the experiment is too large to include it in the simulation domain owing to an unnecessary loss of time, and hence comparatively unimportant boundaries in terms of the heat transfer, except for the top surface, were dealt with using continuative boundary conditions. The numerical expression of this becomes the following.
@U ¼0 @n
ð8Þ
ð9Þ
where U indicates any kind of physical properties, and n denotes the normal direction on each plane. Basically, the heat loss by evaporation on the top surface of the weldment should be considered. However, it was assumed to be negligible because the arc welding has little influence on the evaporation compared to a high-power density welding process, such as laser beam, plasma, and electron beam welding. Therefore, the boundary condition of the top surface for a heat transfer considers the arc heat flux, heat convection, and radiational heat loss. This is expressed as
K
(4) The magnetic flux density in the radial and height directions in a cylindrical coordinate system is assumed to be negligible. (5) The current density that spreads in the radial direction in the weld pool will be equal on average throughout the entire thickness of the base metal.
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@T ¼ qVP hA ðT T 1 Þ rs er T 4 T 41 @n
ð10Þ
In Eq. (10) above, the second and third terms on the right-hand side indicate the convectional and radiational heat loss, respectively. The first term, qVP , on the right-hand side denotes the arc heat flux whose fluctuating patterns in each DCEP and DCEN polarities are periodically repeated, which is classified into qEP , heat flux during DCEP duration and qEN , heat flux during DCEN duration. For this reason, the thermal efficiency was applied differently to each polarity through a mathematical model such as the following.
2 VI x þ y2 qVP ¼ qEN þ qEP ¼ gEN þ gEP exp 2r 2A 2pr 2A
ð11Þ
where gEN denotes the arc thermal efficiency of the DCEN polarity, and gEP is for DCEP. It means that the heat is supplied with non-zero gEN during DCEN period, while gEP is zero at this time. It acts vice versa for DCEP period. In this research, a strategy to determine the thermal efficiency in each variable polarity is established by adapting the method to compare the FZ areas of the simulation with an experiment, which is another alternative for estimating the amount of heat in the base metal. As indicated in the Introduction, an experimental paper showed that the longer the DCEP duration that is applied, the larger FZ area. Therefore, if we can match the proportional constant between the DCEP duty ratio and the FZ area, we can determine the efficiencies in each polarity. In Eq. (11), V, I, and rA denote the voltage, current, and effective arc radius, respectively. During the arc welding process, the molten pool surface has two kinds of pressure boundary conditions. One is pressure by surface tension, referred to as the Laplace pressure, and the other is arc pressure through an arc plasma flow [24]. Mathematical expressions of this pressure boundary conditions on the top surface can be expressed as follows:
P þ 2l
@V n c ¼ PA þ @n RC
ð12Þ
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PA ðx; yÞ ¼
l0 I2 x2 þ y2 exp 2 2 4p rA 2r 2A
ð13Þ
Eq. (12) describes the pressure boundary condition at the top surface, and n denotes the normal directional component. The second term on the left-hand side in Eq. (12) means the normal stress on the weld pool based on Newton’s viscosity law. In the same equation, the second term on the right-hand side describes the pressure through surface tension c, and thus Rc denotes the local surface curvature. The first term on the right-hand side is the arc pressure, and is described through Eq. (13). One of the most interesting features in an arc weld pool simulation is the fact that the metal surface is locally melted, and the resultant temperature gradient through a Gaussian heat distribution therefore has quite a significant role. Even a slight difference in temperature gradient on the weld pool surface can cause a strong shear force, which is referred to as a Marangoni flow, which has been newly reflected in the user-subroutine through coding. Eq. (14) describes the boundary condition through a Marangoni flow.
ss ¼
dc rT dT
ð14Þ
where subscript s, ss , and dc=dT denote the tangential component, tangential stress on the top surface of the weld pool, and the surface tension gradient, given as the thermo-physical property, respectively. In practice, the top surface of a weld pool has a fluctuating wave pattern, which means there is a large fractional z-directional temperature gradient component in the direction perpendicular to the x-y plane, as shown in Fig. 1. Hence, its effect in the zdirection is negligible because the heat flux distribution in the welding process is generally in accordance with the twodimensional Gaussian distribution on top weld pool surface, which leads to an extreme temperature gradient within the effective radius in the x-y plane regardless of the z-direction. For this reason, it rarely influences the top surface in the vertical direction. Its related mathematical model can be expressed in the Cartesian coordinate system as
dc @T dT @x
ð15Þ
sy ¼
dc @T dT @y
ð16Þ
Fig. 2 shows the mesh grid system used to explain the processes for transforming Eqs. (15) and (16) into the discrete Eqs. (17) and (18) using the FVM. Here, each character, T, denotes each quadrant temperature away from current cell, T P . Moreover, E-e, W-e, S-s, and N-n indicate the eastern, western, southern, and northern locations, respectively. The upper and lower case characters, T, represent the temperature of the center and the interface of the cell, respectively. If accompanied by a mathematical technique integrating Eqs. (15) and (16) with dx from w to e, and dy from s to n, the results for the transformation into the discretization equations can be derived as n
Z
s
e
sx dxdy ¼
w
F x ¼ sx DxDy ¼ Te ¼
Z
n
Z
s
e
sy dxdy ¼
w
F y ¼ sy DxDy ¼ Tn ¼
dc dT
Z
n
s
Z w
e
@T dxdy @y
ð18Þ
dc ðT n T s ÞDx dT
TP þ TN TP þ TS ; Ts ¼ 2 2
where each interfacial temperature, T e ; T w ; T s ; andT n , is derived through the central differential scheme, which is determined based on the average of the adjacent cells in the x or y direction. 3. Procedures of the experiment and simulation
sx ¼
Z
Fig. 2. The adjacent temperature distributions near current cell for discretization in Cartesian coordinate system.
dc dT
Z
n
s
Z
e
w
@T dxdy @x
dc ðT e T w ÞDy dT
TP þ TE TP þ TW ; Tw ¼ 2 2
ð17Þ
The base material used in both the experiment and simulation is a commercial wrought aluminum plate, A6061-T6, which is the most commonly used throughout all industries. Its related thermophysical properties are listed in Table 1. The welding type used for the experiment is an inverter GTA welding machine, and the joint type is a bead-on-plate (BOP) mode. The welding parameters for the experiment and simulation are summarized in Table 2. A three-dimensional analytic domain was set up with cubic cells 0.5 mm in width, depth, and height. The top surface is open to the air, i.e., a free surface, and it faces the arc heat flux, with convectional and radiational heat loss. The other surfaces are set as having a ‘‘continuative boundary condition,” which means a gradient of any kind in the thermophysical properties at each boundary is zero, as described in Eq. (9). It is very difficult to determine both thermal efficiencies in the DCEP and DCEN simultaneously using only an analytical method. It will therefore be necessary to determine which one should be fixed; we decided to fix DCEN at 0.25 and use variables with five levels of relative ratios in DCEP, as shown in Table 2. Recall that the main purpose of this study is simply to determine the relative thermal efficiency ratio instead of the absolute value. The reason for setting the thermal efficiency during the DCEN at nearly the minimum value in this simulation is based on recent studies indicating that the longer the DCEN duty ratio is, the smaller heat input into the base material. Additionally, it is due to a limitation in the
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737 Table 1 Thermophysical properties of the A6061-T6. Property
Value
Symbol
Density Thermal conductivity
2705 kgm
q
195 Jm1 s1 K1
K
Specific heat
S: 847 Jkg1 K1
Cp
L: 870 Jkg1 K1 873 K 915 K
T so T li
3.5104 Nm1 K1 0.0025 m
rA
Solidus temp. Liquidus temp. Surface tension gradient Arc radius Magnetic permeability Ambient temperature Latent heat fusion Emissivity Surface tension Convection coefficient Dynamic viscosity
3
1.26106 Hm1 298 K 336 kJkg1 0.2 0.89 Nm1 80 Jm2 s1 K1 0.00115 kg s1 m1
dc dT
lm
T1 Lf
er c
hA
l
L: Liquid/S: Solid.
Table 2 Welding parameters for experiment and simulation. Parameter
Value
Material size Current Welding speed Arc length Gas type Gas flow rate Electrode Switching freq. Thermal Efficiency in DCEN Thermal efficiency in DCEP (5 levels) DCEP duty ratio (4 levels)
300 100 10 mm 280 A 0.0015 ms1 0.0032 m 99% Ar 23.0 Lmin1 2%Th + W £4.0 mm 100 Hz 0.25 0.55/0.65/0.75/0.85/0.95 15%/25%/35%/45%
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fixed at 100 Hz based on a previous study that revealed that a switching frequency of several hundreds of Hz does not affect the weld quality. A criterion of the assessment of the heat input capacity into the base material by each DCEP duty ratio was arranged through computations in the FZ area. In the experimental study, the welding duration lasted for 80 s with a specific travel speed of 1.5 mm/s, and the cutoff position for computing its transverse crosssectional FZ area was then determined as 100 mm away from the welding start point under sufficient consideration of the quasisteady state. A schematic diagram of this is depicted in Fig. 3. Unlike in the experiment, the welding duration time and size of the base metal during the simulation were reasonably reduced to save time and enhance the convergency without any difference shown in the analytical results. Accordingly, the total analytical domain was set up using 82 mm 32 mm 14 mm, with a top vacancy of 4 mm as a void space for a free surface expression, namely, an F value of zero. Strictly speaking, the dynamic welding process occurs under a transient state, and not a stationary one. Thus, it should be carefully considered when selecting a plane to estimate the FZ areas. During this simulation, the chosen plane was placed sufficiently away from the arc start point, guaranteeing that the weld pool would be fully developed as a quasi-steady state. Under these considerations, the analytical welding duration lasted only for 50 s under the same welding conditions used in the experiment, and the cut-off plane for investigating the transverse cross-sectional FZ areas was decided as being 60 mm from the welding start point, where its quasi-steady state was then completely confirmed. Fig. 4 shows an example of the method used to estimate the transverse cross-sectional FZ areas using a computation in which the number of pixels is multiplied by the pixel resolution.
4. Results and discussion thermal electrons’ absorption into the base metal emitted from the electrode by aluminum oxide on the surface of the base metal, i.e., the DCEN shows a poor performance in terms of the penetration of thermions into the base material. An important variable in addition to the arc current, voltage, and welding speed in the welding process is the DCEP duty ratio, which is the portion of DCEP duration to a single pulse period. Its setting values range from 15% to 45% with 10% unit intervals. The switching frequency, i.e., the reverse of the pulse period, is
4.1. Experimental results The load currents and load voltages based on each DCEP duty ratio were measured using an oscilloscope and a voltmeter, respectively, during the welding process. Fig. 5 shows the current waveform patterns, all of which were maintained at nearly 280 A regardless of the DCEP duty ratio. One of the highly interesting aspects discovered during the experiment is the considerable variation in load voltage by the
Fig. 3. Schematic diagram representing welding procedure in experiment.
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Fig. 4. Procedure for computation of FZ area in weldment.
Fig. 5. Currents measured with oscilloscope during welding by duty ratio of the DCEP (a) DCEP duty ratio 15% (b) DECP duty ratio 25% (c) DCEP duty ratio 35% (d) DCEP duty ratio 45%.
H. Jeong et al. / International Journal of Heat and Mass Transfer 138 (2019) 729–737
DCEP duty ratio, and its certain regularity with a consistent pattern, i.e., the load voltage tends to decrease by increasing the DCEP duty ratio, as shown in Fig. 6. A special reason for this phenomenon could be cautiously explained. As mentioned before, the power source used in this experiment is an inverter GTA welding machine whose current is kept constant, and its load voltage is automatically adjusted until the load current reaches the current value, which is already determined. According to a reference paper with regard to the investigation of phenomena that occur in a weld pool using a high-speed camera in the variable polarity arc welding of aluminum alloy [25], it was found that the amount of removed oxide layer on the aluminum surface is remarkably reduced as the DCEP duty ratio decreases. A notable voltage drop might occur near the oxide layer on the top surface of the base material because it acts as a resistance when the current passes through the top surface. At that time, the aluminum oxide layer on the aluminum plate interrupts the current smoothly carrying into the aluminum base material. That is, a higher voltage is required to pass through because much more of the oxide layer is left on the aluminum top surface without a sufficient elimination in the case of a lower DCEP duty ratio. Fig. 7-(a) shows transverse cross-sectional images of a quasisteady state region, and a graph representing the comparisons through a simple power computation regardless of the thermal efficiency at each polarity, i.e., simply multiplying the welding
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voltage and current, is shown in Fig. 7-(b). Here, the computational values of arc power and FZ areas acquired through an experimental study are applied to units on the left and right vertical axes, respectively. As shown in Fig. 7-(a), the FZ areas remain steady up to 35% DCEP duty ratio, but increase dramatically at 45%. On the other hand, a curve representing the welding power, while not considering each thermal efficiency, is inversely proportional to the FZ area curve. In summary, FZ areas increase despite the arc power decrease. This directly infers that a higher heat input enters the base material during the DCEP period than during the DCEN period. Accordingly, it is very reasonable to take higher analytical variables in the DCEP period compared to the unchangeable 0.25 in the DCEN period, with regard to the thermal efficiency shown in Table 2. 4.2. Comparison between experimental and simulation results An indirect method was adopted to predict the relative thermal efficiency ratio, which compares the FZ areas between the experimental and simulation results as a part in estimating how much heat input would come into the base material. However, it takes a long time to apply all kinds of options (a total of 20 cases, namely, the DCEP ratio with four levels, and the thermal efficiency in DCEP with five levels) listed in Table 2. Hence, contrasting a graph pattern of an experiment-dependent FZ area with those through the simple calculations below was implemented as a preference, and was then sorted into potential candidates based on the consistency.
ðgEN f EN þ gEP f EP Þ jVIj
ð19Þ
f EN þ f EP ¼ 1
Fig. 6. Load voltage by each DCEP duty ratio.
where f EN ; f EP ; I; and V indicate the duty ratio in DCEN, duty ratio in DCEP, welding current, and welding voltage, respectively. In addition, the welding power was taken as an absolute value because heat calories only have a positive sign, and it was confirmed that the absolute value of the load current and voltage are kept at a constant regardless of the duty ratio during the welding process. Fig. 8 shows the outcomes. Graphs from cases 1 through 3 usually tend to decrease by increasing the DCEP duty ratio: the graph of case 4
Fig. 7. The comparison of graph patterns between FZ area and welding power regardless consideration of the thermal efficiency (a) transverse cross sectional images (b) a resultant graph by DCEP duty ratio.
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Fig. 8. The graph patterns by each case with regard to thermal efficiency in DCEP.
shows a constant pattern, and finally, the graph in case 5 is quite similar to the FZ area acquired through the experiment, as shown in Fig. 7-(b). For this reason, case 5 is a strong candidate to determine the relative thermal efficiency ratio. Among the different cases, a comparison of the FZ areas between the experiment and simulation was performed for only case 5 to determine whether the results match each other while reducing the loss of time during the simulation. Their resultant outputs are summarized with the cross-sectional images and graphs in Fig. 9. Here, distinguished three zones of the weldment denote the FZ, HAZ, and base materials from the top center to outer area, respectively, and the subscript values in Fig. 9-(a) indicate the FZ area. Although it is difficult to clarify the definition of the HAZ in aluminum welding when not considering the cooling rate, it might be regarded that the region whose temperature reaches 653 K is
HAZ; this is the temperature at which the transformation from a b00 -phase to a b0 -phase occurs in the Al-Mg-Si alloy (6XXX aluminum alloy series) during the process of over-aging [26]. As shown in Fig. 9, the FZ area remained fairly constant over the range of 15% to 35%, and showed a sharp increase at 45% in both the simulation and experimental results. It is obvious that a much higher thermal efficiency will be given in DCEP than in DCEN by acquiring entirely similar values for the FZ areas shown in Fig. 9(b). Based on these results, the thermal efficiency in DCEP and DCEN will be assumed as 0.95 and 0.25, respectively, with a ratio of about 3.8 times. Hence, this implies that the physical phenomena occurring at each polarity are obviously dissimilar to each other. It was already confirmed that the load voltage during welding tends to be reduced when increasing the DCEP duty ratio, as shown in Fig. 6,
Fig. 9. The comparisons of FZ areas between the experiment and simulation in case-5 (a) transverse cross sectional images (b) a resultant graph pattern.
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and it seems that this is deeply correlated with the remaining aluminum oxide layer on the surface. This can probably be explained through the combination of the quantum tunneling effect and a random walk of the cathode spot, which occurred during the DCEP period in a recent study by Cho [8–10], i.e., these two mechanisms not only have a much more concentrated heat flux but also simultaneously more heat input to be penetrated into the base material by easily eliminating the oxide layer near the surface during the DCEP period. Consequently, the heat input amount during a variable polarity GTA aluminum welding process depends on oxidization characteristics of the material, DCEP duty ratio, and their interactions. 5. Conclusions With regard to aluminum variable polarity GTA welding, it was recently reported that the FZ area tends to increase through an increase in the DCEP duty ratio. It is completely opposite from what we commonly know based on conventional arc theory through which the thermal effect is more exceptional for DCEN than for DCEP because thermal electrons having a high temperature are continuously emitted from a tungsten electrode, and then penetrate into the base material during the DCEN period, which is considered to be natural. Therefore, such recent researches have generated extreme confusion. To resolve this contradiction, two types of new mechanisms were employed, and such opposite results were clearly explained in this study. Hence, higher thermal efficiencies in DCEP than in DCEN were set up as variables based on this fundamental physical characteristic, and its relative thermal efficiency ratio was finally estimated through a comparison of the FZ area between the numerical analysis model and an experiment, the resultant value of which is 3.8. Conflict of interest The authors declared that there is no conflict of interest. Acknowledgement This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning, Korea (Grant No. 2016R1D1A1B03935036) and Technology Innovation Industrial Program funded by the Ministry of Trade, Industry & Energy, Korea (Grant No. 10052793 and Grant No. 10077517).
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References [1] L.W. Eastwood, Gases in Non-Ferrous Metals and Alloys, American Society for Metals, Cleveland, OH, 1953. [2] J. Cho, Variable polarity plasma arc (VPPA) welding Part 1. Introduction and theoretical background, J. Korean Weld. Join. Soc. 30 (2012) 199–201. [3] J. Cho, Variable polarity plasma arc (VPPA) welding Part 2. Applications and welding physics, J. Korean Weld. Join. Soc. 30 (2012) 291–293. [4] H. Patte, R. Meister, R. Monroe, Cathodic cleaning and plasma arc welding of aluminum, Weld. J. 47 (5) (1968) 226s–1223s. [5] R. Sarrafi, R. Kovacevic, Cathodic cleaning of oxides from aluminum surface by variable-polarity arc, Weld. J. 89 (2010) 1s–10s. [6] F. Li, Z. Yu, X. Xiao, X. Hua, Y. Wu, Research of cathode cleaning and weld formation of AC TIG welding of aluminum alloy, The 2nd East Asia Symposium on Technology, Sep 26–27, Nara, Japan, 2012. [7] M.A.R. Yarmuch, B.M. Patchett, Variable AC polarity GTAW fusion behavior in 5083 aluminum, Weld. J. 86 (2007) 196s–200s. [8] J. Cho, Weldability increase of aluminum by variable polarity arc, J. Korean Weld. Join. Soc. 32–1 (2014) 108–111. [9] J. Cho, J. Lee, S. Bae, Heat input analysis of variable polarity arc welding of aluminum, Int. J. Adv. Manuf. Technol. 81 (2015) 1273–1280. [10] J. Cho, J. Lee, S. Bae, Y. Lee, K. Park, Y. Kim, J. Lee, Theoretical background discussion on variable polarity arc welding of aluminum, J. Korean Weld. Join. Soc. 2 (2015) 14–17. [11] N. Stenbacka, I. Choquet, K. Hurtig, Review of arc efficiency values for gas tungsten arc welding, in: IIW Commision IV-XII-SG212 Intermetdiate Meeting BAM, Berlin, Germany, 2012. [12] H. Jeong, K. Park, J. Cho, Numerical analysis of variable polarity arc weld pool, J. Mech. Sci. Technol. 30–9 (2016) 4307–4313. [13] J. Cho, An Analysis of Three-Dimensional Molten Pool in Laser-GMA Hybride Welding Ph.D. Thesis, KAIST, Korea, 2007. [14] J. Cho, D.F. Farson, J.O. Milewski, K.J. Hollis, Weld pool flows during initial stages of keyhole formation in laser welding, J. Phys. D: Appl. Phys. 42 (2009). [15] J. Cho, S.J. Na, Implementation of real-time multiple reflection and Fresnel absorption of laser beam in keyhole, J. Phys. D Appl. Phys. 39 (2006) 5372– 5378. [16] J. Cho, S.J. Na, Theoretical analysis of keyhole dynamics in polarized laser drilling, J. Phys. D Appl. Phys. 40 (2007) 7638–7647. [17] J. Cho, S.J. Na, Three-dimensional analysis of molten pool in GMA-laser hybrid welding, Weld. J. 88 (2009) 35s–43s. [18] J. Cho, D.F. Farson, K.J. Hollis, J.O. Milewski, Numerical analysis of weld pool oscillation in laser welding, J. Mech. Sci. Technol. 29 (4) (2015) 1715–1722. [19] Y.C. Lim, X. Yu, J. Cho, J. Sosa, D.F. Farson, S.S. Babu, S. McCracken, B. Flesner, Effect of magnetic stirring on grain structure refinement Part 1 - Autogenous nickel alloy welds, Sci. Technol. Weld. Joining 15 (2010) 583–589. [20] Y.C. Lim, X. Yu, J. Cho, J. Sosa, D.F. Farson, S.S. Babu, S. McCracken, B. Flesner, Effect of magnetic stirring on grain structure refinement Part 2 - Nickel alloy weld overlays, Sci. Technol. Weld. Joining 15 (2010) 400–406. [21] C.W. Hirt, B.D. Nichols, Volume of fluid method for the dynamics of free boundaries, J. Comput. Phys. 39 (1981) 201s–225s. [22] R.W. Fox, A.T. McDonarld, Introduction to Fluid Mechanics, fourth ed., Wiley, New York, 1992. [23] A. Kumar, T. Debroy, Calculation of three-dimensional electromagnetic force field during arc welding, J. Appl. Phys. 94 (2003) 1267–1277. [24] Z. Cao, Z. Yang, X.L. Chen, Three-dimensional simulation of transient GMA weld pool with free surface, Weld. J. 83 (2004) 169s–176s. [25] J. Lee, Heat Input Mechanism Elucidation through Observation of Cathode Spot Phenomena in Variable Polarity Aluminum Arc Welding Thesis for degree of master, Chungbuk National University, Korea, 2016. [26] O.R. Myhr, Ø. Grong, H.G. Fjaer, C.D. Marioara, Modeling of the microstructure and strength evolution in Al-Mg-Si alloys during multistage thermal processing, Acta Mater. 52–17 (2004) 4997–5008.