Mechanism of surface morphology in electron beam melting of Ti6Al4V based on computational flow patterns

Accepted Manuscript Title: Mechanism of surface morphology in electron beam melting of Ti6Al4 V based on computational flow patterns Authors: Wenjun Ge, Sangwoo Han, Yuchang Fang, Jason Cheon, Suck Joo Na PII: DOI: Reference:

S0169-4332(17)31336-3 http://dx.doi.org/doi:10.1016/j.apsusc.2017.05.033 APSUSC 35968

To appear in:

APSUSC

Received date: Revised date: Accepted date:

3-2-2017 28-4-2017 3-5-2017

Please cite this article as: Wenjun Ge, Sangwoo Han, Yuchang Fang, Jason Cheon, Suck Joo Na, Mechanism of surface morphology in electron beam melting of Ti6Al4V based on computational flow patterns, Applied Surface Sciencehttp://dx.doi.org/10.1016/j.apsusc.2017.05.033 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Mechanism of surface morphology in electron beam melting of Ti6Al4V based on computational flow patterns

Wenjun Gea, Sangwoo Hana, Yuchang Fangb, Jason Cheona, Suck Joo Naa,*

a

Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon,34141,

Republic of Korea b

School of Materials Science and Engineering, Harbin Institute of Technology, Heilongjiang,150001, PR China

*

Corresponding author Tel.:+82 42 350 3216

E-mail address: [email protected]

Highlights     

Single melt tracks of Ti6Al4V were fabricated using electron beam melting. Three different surface morphologies were obtained and investigated. A new CFD model was proposed to analyze the melt track formation. A new method for interaction between electron beam and metal particle is proposed. The formation mechanism based on flow pattern of melt track and defects were revealed.

Abstract In this study, a 3D numerical model was proposed that uses the computational fluid dynamics (CFD) method to investigate molten pool formation in electron beam melting under different process parameters. Electron beam ray tracking was used to determine energy deposition in the powder bed model. The melt tracks obtained in this study can be divided into three categories: a balling pattern, distortion pattern and straight pattern. The 3D mesoscale model revealed that it is possible to obtain different molten pool temperature distributions, flow patterns and top surface morphologies using different process parameters. Detailed analysis was performed on the formation mechanism of both the balling defect and distortion pattern. The simulation results of the top surface morphology were also compared with experimental results and showed good agreement.

Keywords: Electron beam melting, Ti6Al4V, surface morphology, simulation

1 Introduction The additive manufacturing (AM) process is used to fabricate component by joining materials, usually layer-by-layer fashion [1]. Over the past ten years, AM methods have attracted much attention as design freedom and short lead times. Recently, most researchers have focused on the research of process parameter optimization and mechanical properties improvement [2-6]. Electron beam melting (EBM) is one of the typical AM methods, similar to selective laser melting (SLM) [7]. Both EBM and SLM have been widely used in complex geometries design and fabrication. EBM is a powder-bed-based additive manufacturing technology using an electron beam as the energy source. The electron beam works under high vacuum conditions, high scan velocity and high beam power. These process features are effective for producing high-performance metal parts with complex shapes, and therefore has appealing potential for the aerospace industry and medical implant industry [8-9]. EBM has been demonstrated to be capable of manufacturing a wide range of materials, including Ti6Al4V, titanium aluminide, In718, cobalt chromium and copper [1013]. In the EBM process, the behaviors of the molten material have a significant influence on the fabrication quality of the final parts, especially for thin-walled structures. Multiple physical phenomena are involved, such as the interaction between the electron beam and materials [14], molten pool flow, heat transfer, etc. The EBM process requires a high-vacuum environment, and the temperature is very high, which makes it difficult to measure the molten pool temperature and the flow characteristics directly. Therefore, numerical modeling has been a powerful tool for shedding light on the molten pool flow, and for further optimizing process parameters to improve product quality [15]. A few CFD models have been developed for EBM [16] and selective laser melting (SLM) [17-20] to investigate the molten pool flow and final solidified morphology. Matthews et al modelled the molten pool flow and denudation phenomena of the laser powder bed fusion process [17]. D. GU et al. demonstrated that Marangoni convection plays a crucial role in the migration of bubbles and densification behavior in SLM-processed Ni-based super alloy and aluminum alloy [18-20]. C. Qiu et al. developed a CFD model based on the C++ open source CFD toolbox to investigate the formation of pores and the development of surface morphology in the SLM process. Surface tension, Marangoni flow, recoil pressure and buoyancy force were taken into account. Splashing was found to be critical to pore formation [20]. Y.S.Lee used Flow3D software to study the effect of powder particle size distribution and packing density on laser additive manufacturing. In their numerical model [21], the volume of fluid (VOF) method was used to track the free surfaces of the molten pool. C. Korner et al. built a 2-D numerical model using the lattice

Boltzmann method (LBM) to investigate the fluid flow in EBM [22]. Recently, recoil pressure was added to their model, while the Marangoni effect was neglected [23]. They investigated the molten pool flow to create a process map to avoid the undesired balling effect. In this study, a three-dimensional CFD model was built to investigate the molten pool flow and top surface morphology during the EBM processing of Ti6Al4V powder. To investigate interaction between electron beam and powder particles, the beam rays were tracked. The molten pool flow pattern was also studied with a CFD model which took into account the Marangoni effect and recoil pressure. A series of experiments using the same parameters as the simulations were then conducted to validate the numerical model.

2 Experiments For the experimental validation, an Arcam AB S400 EBM system was used. The material used in this study was gas atomized Ti6Al4V powder with a particle size range of 45μm-110μm. Powder layer thickness was set to 50μm. In order to study the effect of energy input on the molten pool formation, single tracks were fabricated employing a variety of beam powers and scan speeds, as shown in Table 1. In the experiment, single tracks are built on the substrate. The substrate is built on the base plate using Ti6Al4V powder. The dimensions of the substrate is 50mm×20mm×5mm. Process parameters used in this study is different from the normal metal part process parameters. These parameters are designed for the porous and lightweight structure. According to our previous experiment results, lower scan speed and beam power is helpful to get a stable and smaller width of single track. In order to avoid powder ‘smoke’, each powder layer was preheated to 873K before being melted, as monitored by a thermocouple under the substrate. Liner energy input E is a widely-used index of input energy, and is defined as follows: E=

𝑃 𝑣

(1)

where P is the power of the electron beam and v is the scan speed. In this study, the linear energy was kept to be constant at 600J·m-1. 3 Model The commercial software Flow3D was used to model the transient heat transfer and fluid flow, while tracking the free surface in the electron beam melting process. The three-dimensional CFD model and electron beam single track scan strategy are performed in a computation domain with 1400μm (length), 450μm (width) and 350μm (height). The computation domain compromises a 50μm-thick Ti6Al4V powder particles lying on a 175μm-thick base plate. The powder bed compromises of irregular distributed powder

particles with the size 45μm ~80μm. The mesh size was set according to electron beam absorption, commutation time and accuracy. Alexander Klassen investigated the relationship between electron beam energy, beam incidence and penetration depth when the electron beam deposit on a Titanium powder [24]. For the beam incidence 0° and 75°, the penetration depth of electrons are about 15μm and 8μm separately. However, the energy loss is dramatically within the range of surface 5μm especially for large beam incidence. In this study, the calculation is carried out with a uniform mesh size of 5μm, which are beneficial for obtaining a compromise of energy input, computation time and accuracy. Standard governing equations including the mass conservation equation, the momentum conservation equation and the energy conservation equation were solved numerically. The free surface of the molten pool was tracked using the volume of fluid (VOF) method. On the boundary, the top surface of the powder bed is subject to radiation and imposed heat flux. The energy on the top free surface is balanced between the Gaussian heat flux and the heat dissipation by radiation. The energy balance on the top surface is expressed as the following equations: k

𝜕𝑇 = 𝑄𝑤 − 𝜀𝜎𝑆𝐵 (𝑇 4 − 𝑇04 ) 𝜕𝑛

(2)

where k refers to the effective thermal conduction of the powder system, T is the temperature, σSB is the Stefan-Boltzmann constant of 5.67×10-8W/m2K4 and ε is the emissivity of 0.02. In the numerical model, the mesh size is relatively small and total number of mesh is large. Considering the computation time, the thickness of substrate in the simulation model is smaller than that used for experimentation. The boundary condition set in Flow3d software is continuative boundary at the bottom of the substrate. In the simulation, the molten liquid is assumed to be an incompressible laminar flow with Newtonian viscosity. Various physical phenomena were considered along with standard governing equations in the CFD model. Heat source model: The energy distribution of the electron beam on the focal plane was assumed to be a Gaussian distribution, as follows[25]: 𝑈𝐼

𝑞𝐿 (𝑥, 𝑦, 𝑧) = 2𝜋𝜎2 exp (−

𝑥 2 +𝑦 2 ) 2𝜎 2

(3)

where U and I are the electron beam voltage and current, and σ is the beam radius. In electron beam melting, the interaction between the electron beam and metal particles is the key factor which affects the particle melting and fluid flow. Fig.1 shows the scheme of an electron beam ray interacting with a metal particle.

The Electron beam ray tracking procedures are performed as follows: (1)The electron beam rays move step by step. In each step, the cell where the ray is located will be checked to see whether the rays reach the surface cell or not. (2) If the cell is not a surface cell, repeat step (1). If the ray reaches a surface cell, contact checking is performed. (3)If the ray makes contact with the surface cell, it will be absorbed. The absorptivity is defined by the following equation[26]: 𝜂𝑏 =

1

(4)

(1+𝑐𝑜𝑠𝜃)9⁄√𝑍

where θ is the interaction angle between the electron beam and particle surface, and Z is the molecular number. Reflections are not considered in this model. In other words, after being partially absorbed, the rays will not be tracked again. The details of the ray tracking process before reflection can be found in a previous study [25]. Recoil pressure: Recoil pressure is considered in the simulations using the following model[27]: 𝑇−𝑇

𝑃(𝑇) ≅ 0.54𝑃𝑠𝑎𝑡 (𝑇) = 0.54𝑃0 exp (𝐿v 𝑅̅𝑇𝑇𝑏 )

(5)

𝑏

where Psat is the saturated pressure, P0 is the atmospheric pressure, Lv is the latent heat of vaporization, Tb is the boiling temperature, and R is the universal gas constant.

Surface tension: The surface tension is temperature-dependent in this study, to model the Marangoni flow, and is given as follows: σ(T) = 𝜎0 − 𝐴(𝑇 − 𝑇𝑚 )

(6)

where σ0 is the surface tension of pure metal at the melting point, A is the negative of surface tension gradient for pure metal, Tm is the melting point of the material. Buoyancy force: The buoyancy force is modeled using the Boussinesq approximation, which can be expressed as follows: 𝐹𝑏 = 𝜌𝑔𝛽(𝑇 − 𝑇0 )

(7)

where g is gravity, T0 is the reference temperature, and β is the thermal expansion coefficient. The material properties used in this model are listed in Table 2[28-32].

4 Results and Discussion 4.1 Formation of the top surface morphology Both the experiments and simulations demonstrated that process parameters play crucial role in the formation of the molten pool and surface morphology in EBM process. As shown in Fig.2, different top surface morphologies were formed under different scan speed and beam power. From the experimental results, the molten pool morphology can be divided into three types: a balling pattern (Fig.2a), distortion pattern (Fig.2b) and straight pattern (Fig.2c). The numerical model reproduced these three different morphology of the molten pool. In Fig.2a it can be seen that the melted particles did not form a continuous track, but turned into an isolated island shape. This balling defect occurred at a beam power of 30w and scan speed of 0.05m/s. With a beam power of 60w and scan speed of 0.1m/s, a continuous track was obtained. However, the single track is distorted. Fig.2c shows the morphology at a beam power of 180w and scan speed of 0.3 m/s. It should be noted that the top surface is smoother and relatively straighter compared with the other two results. In brief, as the beam power and scan speed increased proportionally, the temperature within the molten pool increased and the surface quality became smooth. The differences produced by the process parameters occurred with different temperature fields and the resultant molten pool flow, even though the liner energy was kept constant. 4.2 Thermal behavior of molten pool As mentioned in Section 3, the Marangoni flow as well as the buoyancy flow is directly determined by the temperature gradient, and the evaporation and recoil pressure have a stronger influence when the temperature is higher. Therefore, different temperature fields under different fabrication parameters will lead to different flow patterns, forming different surface morphology. Fig.3 shows the temperature field on a longitudinal section for different process parameters. At a beam power of 30w and scan speed of 0.05m/s, the peak temperature of the molten pool is 2267K(Fig.3a). When the beam power and scan speed were increased to 60w and 0.1m/s, the peak temperature of the molten pool reached 2518K(Fig.3b). When the beam power and scan speed were further increased to 180w and 0.3m/s, the peak temperature reached 3449K (Fig.3c). Consequently, the top surface morphology changed as the temperature field shifted. It is easy to understand why the temperature has a tendency to increase when the beam power and scan speed are increased for the same line energy. Liner energy represents the energy input in certain distance. When the liner energy is same, it means that the energy input in the same distance is

same. However, the thermal diffusion time is affected by the interaction time between energy and material. In EBM process, the thermal diffusion time is affected by scan speed. At a higher scan speed, the thermal diffusion time is shorter, which results in a higher temperature within the molten pool.

4.3 Analysis of balling phenomenon by fluid flow pattern The balling defect is one of the key defects in additive manufacturing, and influences the quality of metal parts and the manufacturing process. It can result in larger porosity and worse surface roughness in the additive manufactured products. Many researchers have investigated the balling effect and have proposed methods to avoid the balling effect [33-34]. In this study, the mechanism of the balling defect was studied based on thermal distribution and fluid flow pattern. The longitudinal section at the center of the powder bed was used to study the balling defect formation process. Fig.4 shows the temperature profiles of the molten pool along the longitudinal direction at different time. In the temperature profiles, the red color represents the molten pool. Corresponding fluid flow pattern was shown in Fig.5. At the initial stage t=7ms, particle P2 was heated by the electron beam while the temperature did not reached the melt temperature (Fig.4a). As the electron beam moving forward, particle P2 started melting and the molten volume increased (Fig.4b, Fig.4c and Fig.4d). From the corresponding fluid flow pattern, the melt fluid of particle P2 showed upward flow pattern (Fig.5a, b, c). The upward flow makes the fluid separate with the previous melt metal and not wet on the plate. The particle P2 was not fully melted as the energy input was not enough. Later (t=11ms to t=13ms), the residual region of particle P2 solidified with touch the neighbor particle and particle P1 started melting (Fig.4e, f, g). Instead of wetting on the base plate, the molten part of particle P1 flow backward and upward (Fig.5c, d, e). As a result, the melt track break up and balling defect was formed at t=10.40ms (Fig.4h). It is obvious that in the melt region the fluid flow has an inward flow tendency. The possible reason cause that flow pattern is mainly because of the high surface energy. Surface energy and surface tension is sensitive to the molten pool temperature. Low molten pool temperature result in high surface tension. As mentioned before, the peak temperature at 0.5m/s and 30w is relative low which result in a high surface tension. The nature way to reduce surface energy is to maintain the molten pool as a sphere shape. Therefore, the melted region of the particle shows inward and upward flow pattern.

4.4 Mechanism of distorted morphology formation Particles that are located far from the center of the beam do not receive enough energy to become fully melted. These particles are partially melted or unmelted. Partially melted particles will affect the fluid flow in the molten pool and the final top surface morphology. The temperature contours during the melting process are shown in Fig.6 from 7.0ms to 8.5ms. From 7.0ms to 7.3ms (Fig.6a and Fig.6b), the particle on the right started melting from the top region. At 7.6ms (Fig.6b), the molten liquid of the particle flowed downward and touched with the molten pool. As the electron beam moved forward, the particle cooled down very fast. At 7.9ms, the temperature of the particle is lower than the melt point and it still keep in touch with the molten pool (Fig.6d). From 8.2ms to 8.5ms, the solidification of molten pool started and the center molten pool was deviated to the side located particles (Fig.6e,f).

Fig.7 shows the calculated velocity vector patterns of the molten pool from 7.9ms to 8.1ms. The velocity at the top of the molten pool is higher. That illustrated that the surface tension drive the main flow of the fluid. Another trend is that the fluid on the top surface showed a sideward flow. The possible reason for this is that the temperature of the partially melted particle on the right side is relatively low, and thus the surface tension is high. That led to a large surface tension gradient from the melted metal to the partially melted particle. Driven by the Marangoni effect, the liquid metal flowed from the center of the molten pool to the right side. Finally, the single track exhibits the distortion morphology.

4.5 Formation of straight melting track As the scan speed and beam power were increased to 0.3m/s and 180w, a straight and smooth melting track formed. At this parameter, the peak temperature of the molten pool increased, and the particles along the scan line were fully melted. That contributed to the straight melting track formation. Fig.8 shows a longitudinal section of the molten pool from 1.0ms to 3.0ms. The corresponding streamline patterns are shown in Fig.9. From 1.0ms to 3.0ms, the particles in front of the scan direction were continuously melted and flowed downwards into the melt pool. From the corresponding streamline profile (Fig.9), the velocity is very high at the front of the molten pool and the flow direction is backward and downward, which causes the melted metal to flow into the molten pool. A vortex flow pattern was formed during the process. When the particles were melted, a strong backward and downward flow was produced. Due to the strong backward flow, the molten flow reached the bottom at the solid/liquid boundary and was

directed backward, and was finally lifted up, owing to the buoyancy. This circular pattern causes a vortex, as seen in Fig. 9a, b, c.

When the electron beam marches forward, the flow pattern becomes more complex, as the fluid flow splits into two main streams, as shown in Fig. 9d, e. At the rear of the molten pool, the fluid metal flows forward, which contributes to a flat and continuous molten track. 5 Conclusions In this study, a three-dimensional CFD model of the interaction between electron beam and metal particles was applied and validated by experimental results. Both the simulations and experiments were conducted with the same fabrication parameters, including scan speed and beam power. The model shed light on particle melting and morphology forming processes at the meso-scale. The main conclusions are as follows: (1) By keeping the constant liner energy (beam power/ scan speed), increasing the beam power and scan speed will lead to increase in the temperature of the molten pool. That result in different surface morphologies for the single track. Simulation and validation experiments show that the top surface morphology can be divided into three types, i.e. balling effect, distortion track and straight track morphology. (2) Balling defects are sensitive to molten pool temperature and surface tension. The balling defect formed when the temperature of the molten pool was low and the surface tension is high. In the longitudinal direction, the inward flow pattern gives rise to spherical morphology which breaks up the melt track. (3) As the molten pool temperature increased, the balling defect vanished while distortion morphology occured. The probable reason is a partially melted particle located at the side of the molten pool which remains in contact with the molten pool. Thus, the Marangoni effect will drive the fluid flow from the molten pool center to the partially melted particle located at the side. (4) When the temperature of the molten pool further increased, the effect of the partially melted particles decreased. In that case, a vortex flow pattern is formed and contributes to a straight molten track. Acknowledgments The authors gratefully acknowledge the support of the Brain Korea 21 project, and would like to thank Dr. Brent Stucker and Dr. Li Yang for their help with EBM experiments.

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Electron beam rays

θ

Particle surface

Fig. 1 Schematic of interaction between Electron beam ray and a particle

1mm

100μm

1.5mm (a) Balling morphology, P=30W and v=0.05m/s

1.4mm

200μm

3.0mm (b) Distortion morphology, P=60W and v=0.1m/s

1.4mm

200μm

3.0mm (c) Straight track morphology, P=180W and v=0.3m/s Fig.2 The top surface morphology from simulated and experimental results.

(a) P=30w, v=0.05m/s

(b) P=60w, v=0.1m/s

(c) P=180w, v=0.3m/s Fig.3 Temperature contour plots on the longitude cross section for different parameters: (a) P=30w, v=0.05m/s, (b) P=60w, v=0.1m/s, (c) P=180w, v=0.3m/s

P2

P1 P2

(a) t=7ms

(b) t=8ms

P2

P2

(c) t=9ms

(d) t=10ms

P1

P1

P2

(e) t=11ms

(f) t=12ms

P1

P1

(g) t=13ms Fig.4

Temperature

profile

(h) t=14ms at

different

(d)t=10ms,(e)t=11ms,(f)t=12ms,(g)t=13ms,(h)t=14ms

times

(a)

t=7ms,

(b)t=8ms,

(c)t=9ms,

(a) t=8ms

(c) t=11ms

(b) t=9ms

(c) t=10ms

(d) t=12ms

(e) t=13ms

Fig.5 The streamline and velocity magnitude color map of the longitudinal section at different times (a) t=10.10ms, (b)t=10.20ms, (c)t=10.30ms

(a) t=7.0ms

(d) t=7.9ms

(b) t=7.3ms

(e)t=8.2ms

(c) t=7.6ms

(f)t=8.5ms

Fig.6 Temperature contour plots on the cross section at different times: (a)t=7.0ms, (b) t=7.3ms,(c) t=7.6ms,(d) t=7.9ms,(e) t=8.2ms,(f) t=8.5ms.

(a)t=7.9ms

(b) t=8.0ms

(c)t=8.1ms

Fig.7 The streamline and velocity magnitude color map of the longitudinal section at different times: (a)t=7.9ms, (b)t=8.0ms,(c)t=8.1ms.

(a) t=1.0ms

(b) t=1.5ms

(c) t=2.0ms

(d) t=2.5ms

(e) t=3.0ms Fig.8 Temperature contour plots on the cross section at different times: (a)t=1.0ms, (b) t=1.5ms,(c) t=2.0ms,(d) t=2.5ms,(e) t=3.0ms

(a) t=1.0ms

(b) t=1.5ms

(c) t=2.0ms

(d) t=2.5ms

(e) t=3.0ms Fig.9 The streamline and velocity magnitude color map of the longitudinal section at different times: (a)t=1.0ms, (b) t=1.5ms ,(c) t=2.0ms,(d) t=2.5ms,(e) t=3.0ms.

Table 1 Electron beam melting process parameters Scan speed(m·s-1)

Beam power(w)

1

0.05

30

2

0.1

60

3

0.3

180

Samples

Table 2 Material properties used in simulation Physical properties

Value

Density of liquid metal ρ (kg m-3)

3890

Density of solid metal ρ (kg m )

4430

Thermal conductivity of liquid KL(W m-1 K-1)

32.5

Thermal conductivity of solid KS(W m-1 K-1)

20

Viscosity μ(kg m-1 s-1)

0.005

Surface tension σ (N m-1)

1.68

Surface tension gradient d σ /dT (N m-1 K-1)

-2.6×10-4

Specific heat of solid CS(J kg-1 K-1)

763

Specific heat of liquid CL(J kg-1 K-1)

872

-3

-1

Latent heat of fusion hSL(J kg )

3.6×105

Latent heat of vaporization(kJ kg-1)

9×103

CTE(m/mK)

1.0×10-5

Liquidus temperature TL(K)

1933

Solidus temperature TS(K)

1877

Boiling temperature TV(K)

3533