Numerical and Experimental Study of Ti6Al4V Components Manufactured Using Powder Bed Fusion Additive Manufacturing
Numerical and Experimental Study of Ti6Al4V Components Manufactured Using Powder Bed Fusion Additive Manufacturing
JONAS ZIELINSKI 0
HANS-WILFRIED MINDT 0
JAN DU¨ CHTING 0
JOHANNES HENRICH SCHLEIFENBAUM 0
MUSTAFA MEGAHED 0
0 1.-RWTH Aachen University - Digital Additive Production , Aachen, Germany. 2.-ESI Group, Essen , Germany. 3.-Fraunhofer Institute for Laser Technology , Aachen, Germany. 4.-
Powder bed fusion additive manufacturing of titanium alloys is an interesting manufacturing route for many applications requiring high material strength combined with geometric complexity. Managing powder bed fusion challenges, including porosity, surface finish, distortions and residual stresses of as-built material, is the key to bringing the advantages of this process to production main stream. This paper discusses the application of experimental and numerical analysis towards optimizing the manufacturing process of a demonstration component. Powder characterization including assessment of the reusability, assessment of material consolidation and process window optimization is pursued prior to applying the identified optima to study the distortion and residual stresses of the demonstrator. Comparisons of numerical predictions with measurements show good correlations along the complete numerical chain.
Additive manufacturing is a versatile
manufacturing route promising shorter lead times for
complex and personalized functional products. The
process flexibility and strength originates from the
use of many control parameters1 and comes at the
cost of high nonlinearity of the system. Qualifying
additive manufacturing processes and asserting
product quality is coupled with extensive
experimental effort. To reduce experimental cost,
physicsbased modeling is expected to offer more insight and
enable virtual optimization while providing the
necessary documentation for expected material
and product qualification.2
This paper focuses on the application of a
modeling platform encompassing powder-scale models as
well as component-scale models towards assessing
the final quality of a demonstrator component.3–7
Ti6Al4V is chosen to demonstrate the modeling
work flow. The numerical results of each step are
compared to experimental measurements
confirming the applicability of numerical methods
towards quick economic qualification of additive
Powder Bed Fusion Machine
For the single-track experiments, an EOS M270
and an ILT laboratory machine have been used. The
manufacturing of the distortion specimens and the
melt pool monitoring was performed on the latter.
One of the important differences between the two
machines is their laser beam diameter (dL).
Single Tracks studies
Melting has been investigated by varying the line
energy EL ¼ PL=vs (where PL is laser power and ms is
scan speed) in such a way, that a transition from a
‘heat conduction’-dominated process towards a
‘keyhole’-dominated8 process is observed (Fig. 1). We
have also investigated the influence of different
laser beam diameters on the melt pool geometry.
Fig. 1. (a) Heat-conduction mode. (b) Keyhole dominant mode.
The influence of the powder layer on energy
coupling was studied by comparing melt pool
characteristics with and without a powder layer.
For all the experiments, the melt pool depth,
height and width as well as the remolten surface
have been evaluated. All single-track experiments
were conducted on Ti6Al4V substrate.
Process Parameter: Scan Speed, Laser Power
Starting from the default parameter set (red circle
in Fig. 2), ten different combinations of laser power
and scanning velocity are used. Figure 2 shows the
line energy as a contour plot for the different
process parameters. With increased line energy,
more keyholing is expected.
The single-track studies were performed with
either no powder or with two different layer
thicknesses: hL30 and 60 lm. The powder layers were
manually applied with a scraper with the
corresponding gap height. To increase the flowability, the
powder was applied in an ethanol suspension
(volume weighted 50% powder, 50% ethanol). The
applied powder layer thickness was measured using
an infinity focus microscope and found to be roughly
5 lm higher than the gap height, resulting in layer
thicknesses of hL 35 lm and 65 lm.
To vary the laser beam diameter (based on 86%
definition), two different SLM machines with beam
diameters of dL EOS ¼ 80 lm and dL ILT ¼ 110 lm
were used. The process parameters from the ILT
laboratory machine were transferred to the EOS
machine by keeping the average laser beam
intensity and scanning velocity constant. The laser power
PL EOS was adapted accordingly:
pd2L ILT ¼ pd2L EOS
The fabricated single-track samples were cut
perpendicular to the scanning direction. The
crosssection was polished and etched with Kroll’s reagent
(duration: 30 s). The melt pool boundaries were
visualized using a light microscope. The depth
(substrate level to deepest melting point), height
(highest remolten material point to substrate level),
width (width of melt pool on substrate level) and
surface (integrated area of molten material in
crosssection) were measured.
Melt Pool Monitoring: High Speed Videography
Extracting the length of a melt pool using
micrographs is time consuming and error-prone. To
obtain the melt pool length, we used high speed
videography. The melt pool width measured by
videography was verified using single-track
measurements. Other information like the size of
powder denudation zone and particle movement in the
process zone could also be observed, but were not
part of this work.
The videos were recorded with a Photron
FastCam SA-5 and additional pulsed diode lasers
(k 800 nm) were used to illuminate the process
zone. The wavelength of the process laser
(k ¼ 1064 nm) was filtered out to avoid
overexposure. The spatial resolution of the video
material was gauged by recording single tracks with
a defined hatch spacing. The achieved resolution
was 9:31 0:06 plmx. The frame rate was 75 kHZ.
The software package ESI-AM was used
throughout this study for both powder-scale and workpiece
analysis. Using a discrete element model to obtain
the powder bed characteristics, Mindt et al. showed
how the balance between powder size distribution
and the slit height can affect the coating quality and
powder recyclability.9,10 The numerically obtained
powder bed was transferred to the melting
models to solve Navier–Stokes equations as
described in Ref. 6. The mechanical analysis was
assumed weakly coupled to the
thermo-metallurgical phenomena.11 The non-linear material behavior
was accounted for by using a quasi-static
Ti6Al4V properties reported in the literature
A bridge geometry was chosen as a demonstrator
component because of the relative ease of
quantifying the bridge curvature (BC).21 Table I
summarizes the process parameters used.
To increase stress and strain, no preheating was
used. The specimens were removed force-free from
the base plate by EDM. Since the pillars are not
held in place any more, the residual stresses inside
the material leads to a distortion. The angle aBCM
between the bridge pillars was then measured with
a 3D scanner (GOM) (Fig. 3).
RESULTS AND DISCUSSION
The powder chemical composition was analyzed,
confirming compliance with Ti6Al4V specifications.
The particle size distribution was analyzed using an
automated particle measuring system (Morphologi
G3). It was found to correspond to the recommended
particle diameters of dp = 20–63 lm. The particle
size distribution is shown in Fig. 4; it serves as
input for powder-coating simulations. A numerical
assessment of powder recyclability was performed
using the models described in Ref. 10. Figure 4
compares the number density of powder fractions in
fresh powder (source) with those of the powder
deposited on the processing table and the powder in
the recycling bin. Powder particles with the
smallest diameters deposit readily on the processing
table. Larger powder sizes deposit proportionally to
the provided volume fraction. By considering the
predicted mass fraction in the recycling bin, it can
be expected that the powder will tend slightly to
larger particle sizes when recycled. An example of
numerically obtained powder beds used for melting
analysis is also presented.
Single Track Studies
Figure 5 shows typical results for melt pool
modeling. The images compare melt pool isometric
and side views for exemplary conduction (a) and
keyhole conditions (b). The contour plot depicts the
temperatures. The peak temperature is
significantly higher than the melting temperature for
both cases and remains slightly lower than the
evaporation temperature due to the latent heat of
evaporation. In the case of the conduction mode,
evaporation mainly occurs on the surface of the melt
pool. The side view shows that less than two layers
will be remolten in the case of the conduction mode,
whereas up to 4–5 layers will remelt in the case of
Measured and predicted melt pool width and
depth values are compared in Fig. 6. The width
increases with line energy until a possible
stable state (maximum width) is reached. The
maximum width is roughly 2–2.5 times the beam
diameter. Increasing the line energy after this point
only increases the melt pool depth. The melt pool
aanndd 1h4ig5hWlinaendenvser¼gy12c0a0seasnd(d6L4¼7 8ms0ml).m, hL ¼ 30 lm; PL ¼ 103 W
width without powder is a little larger ( 10 lm)
than ‘with powder’, possibly due to additional
particle mass sucked into the melt pool, effectively
cooling the melt pool at its boundaries, thus slowing
the outward expansion of the melt. Single-track
melt pool width values have also been used to
confirm the accuracy of high speed image
processing; Fig. 6a shows excellent correlation between
both methods for a 30-lm powder layer thickness
and beam diameter of 110 lm.
The melt pool depth increases linearly with
increasing the line energy. Since the depth is
measured from substrate level to the deepest point
in the melt pool, the depth of the single tracks
processed without powder is larger than the
corresponding ‘powder case’ setup. Figure 6b shows that
the depth difference is roughly the height of the
powder layer hL ¼ 30 lm and 60 lm.
Different beam diameters lead to different slopes
of the depth-line energy curve. A smaller beam
diameter, dL, results in a deeper melt pool (for the
same line energy). The current working theory for
this effect is that, for the small beam diameter, less
energy is required to sustain a smaller (not so wide)
keyhole leading to a more energy-efficient deep
Numerical predictions of both the melt pool width
and depth are within experimental accuracy for all
cases, except of the case with a line energy
EL ¼ 0:42 J=mm, where the computational domain
size was too small so that the boundary conditions
were found to influence the result.
Figure 7 shows the thermal history at two
monitor points on the track center line. One point is
close to the melt pool surface and the other is
directly below, near the melt pool base (conduction
mode). The results for 0.53 J/mm and 1:38 J=mm are
compared. The higher energy density leads to
higher peak temperatures. The front view of the
corresponding melt pool indicates keyholing. The
cooling rate (peak temperature to solidus) for low
and high energy densities are 6.3 9 106 K/s and
2:27 106 K=s respectively; which is comparable to
the validated finding of IN718Plus.5 The difference
in cooling rates is attributed to the difference in
melt pool volume.
The surface of the remolten zone also increases
with line energy (Fig. 8a). For the small beam
diameter, the process seems to be more energy
efficient. The depth-to-width ratio (Fig. 8b) for all
cases increases linearly with line energy, but the
slope for different beam diameters is different. The
reason is that the melt pool width strongly depends
on the beam diameter, while the depth depends
mostly on the beam power.
Figure 9 shows the melt pool length extracted
from high-speed videos compared with the values
obtained from simulations. From the videos, it can
be seen, that the melt pool length fluctuates
strongly, resulting in a large statistical error.
Nevertheless, the melt pool length in the
simulations is only underestimated by up to 75 lm, fitting
very well with the measured data.
The process parameters are varied using an
evolutionary optimization scheme. Two
optimization objectives are defined: maximizing material
density and maximizing deposition rate (defined as
the mass of deposited material per unit time).
Figure 10a shows the optimization results (pareto
front) that compares the performance of
approximately 1800 cases. Each case represents a
combination of process parameters including laser power,
scan speed, table displacement and hatch spacing.
The density (described by a dimensionless factor)
and the deposition rate are inversely proportional to
one another. The leftmost point of the density
curves is chosen for the maximum density
achievable and a point after the increase in deposition rate
is chosen for a quicker deposition option. The
corresponding combination of process parameters
is listed for both points. Process window
optimization was also pursued experimentally and it was
found that a preheat of 200 C is necessary to ensure
a stable, crack-free process. Figure 10b shows the
consolidated material density as a function of line
energy for two different power levels. Both the
numerical and experimental optimization efforts
yield similar process parameter combinations. The
experimental optimum is used to manufacture the
Distortion and Residual Stresses
Figure 11 shows the maximum vertical
displacement for each layer. Given the chosen table
displacement (30 lm) and the predicted powder
packing density, the available gap between build
surface and coater arm is estimated to be
approximately 70 lm. When the bridge arch is built
(after layer 150), the vertical distortion is larger
than 70 lm and the coater arm is expected to
significantly interact with the build. Premature
abortion of the job was avoided by using a soft
coater arm. Six specimens were built to determined
average curvature angle aBC = 178.84 . Three
models were pursued with different levels of fidelity: full
layer with a layer thickness corresponding to 2
printed layers (2 9 lump), full layer with a layer
thickness corresponding to the printed layer
thickness (1 9 lump)and a high-resolution model, where
the deposition strategy is fully resolved.
Table II summarizes the results, indicating a
numerical error of 1.5%. The balance between
accuracy and speed of computation is in line with
studies reported in Ref. 22. The remaining
discrepancy is expected to reflect the accuracy of the
material behavior model used.
CONCLUSION AND SUMMARY
Powder bed fusion process models were validated
using single-track experiments. Different laser
beam diameters and powder layer thicknesses were
analyzed, showing that large spot sizes lead to wider
and shallower melt pools. Model predictions were
accurate for both conduction and keyhole modes.
Numerical optimization of the TiAl6V4 processing
window was successful, reducing the necessary
experimental effort. Material density of up to
99.5% was achieved. The numerical chain was
extended to determine the bridge curvature with
1.5% error. The study confirms that numerical
models can be used throughout the process chain.
Future work will apply the verified work flow to
The authors acknowledge the financial support of
European Union’s Horizon 2020 research and
innovation program under Grant Agreement
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