A priori process parameter adjustment for SLM process optimization
S. Clijsters, T. Craeghs, J.-P. Kruth
Katholieke Universiteit Leuven, Department of Mechanical Engineering, Division PMA, Belgium
ABSTRACT: Selective Laser Melting (SLM) is a layerwise production technique enabling the production of
complex metallic parts. In the SLM process parts are built by selectively melting subsequent layers of powder
by a laser beam. Nowadays a SLM machine is provided with a fixed scan strategy (laser power, scan velocity
and scan pattern) throughout the full build process. However, the part’s geometry has a large influence on the
stability of the process and therefore the quality of some features like for instance thin walls, sharp corners,
down facing layers (layers above powder), is often poor. This problem can be overcome by using knowledge
of the geometry a priori. This paper presents a methodology to detect critical features in the model of the part,
based on the slicing data. In this way these critical features can then be processed with optimized parameters.
As a proof of concept this a priori parameter adaptation methodology is applied on production overhang.
1 INTRODUCTION
Selective laser melting (SLM) is Additive Manufacturing technique enabling the production of complex
metallic parts in a layerwise manner (Kruth, et al.
2007a). Figure 1 shows a schematic overview of a
typical SLM machine. In this process a thin layer of
metal powder is deposited on a base plate by means
of a powder deposition system. After the layer is deposited, a laser will selectively melt the powder layer
according to a predefined scanning pattern. Such
scanning pattern typically consists of a set of subsequent scan vectors. After the layer is scanned, the
build platform moves over a fixed distance equal to
the thickness of one layer (in SLM typically 20 to 40
Figure 1. Schematic overview of a typical SLM set-up
µm) and a new layer is deposited. This cycle is repeated continuously until the last layer is scanned.
The whole melt process takes place in a process
chamber filled with a protective gas, typically nitrogen gas for processing steels and argon for processing reactive materials e.g. titanium.
The SLM process has a huge potential for a large
range of applications. Due to the almost infinite geometrical freedom, there is no need to design or
manufacture dedicated tools for production. Since
material properties of SLM parts are nowadays comparable to the properties of the corresponding bulk
material (Thijs, et al. 2010, Rombouts, et al. 2006),
applications of the process can be found in domains
such as the medical sector, e.g. dentistry (Kruth, et
al. 2004, Vandenbroucke, 2005), in tool making industries for the manufacturing of tools (Abe, et al.
2001, Klocke, et al. 1996, Berger, 2001 and Voet, et
al. 2005), the general manufacturing industry (machine construction, automotive, etc.) while the potential in production of lightweight structures
(Rehme & Emmelmann, 2006) is investigated for
aerospace applications.
Until today, all existing additive manufacturing
technologies (like SLS and SLM) use a fixed set of
process parameters (laser power, scan speed, hatch
spacing, scan strategy) during scanning of a part.
These parameter sets are mainly selected first of all
to obtain the highest possible material density and
second the highest possible production speed. However, using constant process parameters throughout
the build process does not ensure constant quality
throughout the part since in SLM very different geometrical features have to processed, which have a
different influence on the melt pool behavior. An
example of such feature are overhanging structures,
which are zones in a layer that are completely build
on loose powder. When such (critical) features are
scanned with constant process parameter sets, this
will result in a non-constant melt pool size and shape
due to the different heat flow situations during processing. For instance during scanning of an overhang
structure, the melt pool becomes very elongated,
which leads to balling. This is not beneficial for the
quality and therefore it is necessary to keep the melt
pool dimensions under control during the process.
To solve the problem of variations in thermal behavior of the melt pool, two different solution strategies can be considered. The first strategy is to monitor the melt pool dimensions continuously
throughout the build process, using melt pool sensors as a high speed NIR camera and a photodiode
(Kruth et al. 2007b, Craeghs et al. 2010,). With such
a monitoring system the surface of the melt pool can
be monitored accurately at during the process (online) in real-time and at high frequency: the use of
FPGA image processing enables real-time image
processing up to 10kHz (Craeghs & Kruth, 2010). If
the measured melt pool dimensions deviate from the
desired melt pool dimensions, feedback is given to
the process input parameters to control the melt pool
dimensions towards the desired dimensions. However, in the SLM process for processing of each different geometrical feature the desired melt pool dimensions may differ. Therefore the reference values for
the feedback system are function of the processed
feature. When only one single reference value is
used for all different geometries, the efficiency of
feedback control is very low.
The second solution strategy to overcome the
problem is to define the desired changes in process
parameters and scan strategies a priori (off-line), at
the level of job preparation. This can be done by extracting the information out of the geometry data of
the part. By using this method it is possible to
choose the process input parameters such that the
melt pool has the desired shape and size for all different heat flow situations. This paper will explain
how this a priori adaptation has been implemented
and will show a case study to prove its utility and
applicability.
Figure 2. Methodology for a priori scan strategy adaptation
aptation should occur when the heat flow situation is
different from the nominal situation (i.e the situation
for which the standard process parameters lead to
optimal process quality). These locations need to be
extracted from the geometrical (CAD) model. In
such way the scan vectors can be classified in different vector classes. Each class will have a certain vector identity, which reflects its heat flow situation.
The algorithm for scan vector classification will be
discussed in section 2.1.
The second essential aspect in is the choice of
process parameter sets for each vector class. These
process parameters must be optimized for different
heat flow situations and induce an optimal melt pool
for different heat conductivities. Since these parameter sets are material dependent and are currently only
known for nominal/standard heat flow situations, optimizing such a parameter set is a time consuming
process with a lot of trial and error experiments.
Linking these two aspects, vector classification
and optimized parameter sets for different vector
identities will optimize the process for each heat
flow situation. In the following paragraphs the implementation for both vector classification and parameter optimization will be explained into further
detail.
2.1 Scan vector classification
2 METHODOLOGY
To implement a priori parameter adaptation in a
SLM-process, current process strategy should be extended with two aspects as shown in figure 2. An accurate job preparation is the first important aspect in
a priori parameter adaptation. It is crucial to know
where parameters should be adapted. Parameter ad-
As mentioned before, it is crucial to detect heat
transfer changes a priori to adapt the parameters. In
this paragraph the scan vector generation procedure
for SLM has been extended to classify vectors according to their processing behavior, as shown in
Figure 3. The procedure consists of four steps:
(1)
Slicing
Figure 4. Scan vector classification procedure
In this phase, the job preparation converts the parts
3D model into sequential slices with 2D contours.
The ‘to build’ CAD part is converted into a .stl-file
(which is a standard in additive manufacturing). After orienting this .stl-model in the preferred building
direction, the model is sliced. The output of this slicing is a file containing all the layers (slices) with
their contours.
(2)
Offsetting
Once the slices of each layer are available, the contours of the slices are offsetted. Offsetting is needed
to compensate for the finite dimensions of the laser
beam: without offsetting the dimensions of the part
would be biased outwards. The procedure is very
similar to offsetting in milling: A mill has a certain
radius, therefore the tool path in contour milling has
to be offsetted to mill the correct contour. However
in this case the generated melt pool is the mill of the
process. More details on the offsetting algorithm can
be found in Moesen, et al. 2011.
(3)
to eachother. A relatively simple algorithm is used to
detect these zones.
The algorithm used for this identification is extracted from the open source CGAL library (CGAL).
This algorithm of CGAL ascribes separate values to
areas defined by the contours in a layer (e.g. Figure
5: areas in layer i have value 1 and the areas in layer
i+1 have value 2). To detect 3D information out of
these two layers, the layers will be placed on top of
each other and the values of the areas will be added
up, resulting in areas with calculated values (e.g. areas with value 1 and 2 indicate respectively only in
layer i and i+1, value 3 indicates the common areas
of the two layers). By filtering the right values the
down facing, up facing or middle areas can be easily
distinguished: in Figure 5 value 1, value 2 in (layer i)
+ (layer i+1) indicate respectively the up facings of
layers i and the down facings of layer i+1, the middles are represented by a value 3. Once these different areas are distinguished, the classifying of the
scan vectors can be applied during the generation of
the scanning pattern. Vectors of a scanning pattern
can be classified based on the zone in which they are
located.
(4)
Hatching
The next step in SLM job preparation is filling the
2D contours with scanning patterns. Such a scanning
pattern typically consists of a set of subsequent linear scan vectors representing the tool path of the laser
beam. Commonly these scan strategies are generated
UMD-splitting
In the standard state-of-the-art job preparation a lot
of geometrical information gets lost. This information on the part can be very useful for detecting
differences in heat transfer situations. The main different heat flow situations are illustrated in Figure 4.
The vertical direction of the table distinguishes between huge differences in heat flow, while the horizontal direction refers to small heat conductivity variations due to neighbor scan tracks.
The goal of the UMD (Up, Middle and Down facings) splitting is to import the geometrical 3D
knowledge and to recognize three different zones in
a slice: up facing areas (areas on which no layers
will be built), down facing areas (areas build on
loose powder) and middle areas (layer above and beneath). To recognize these different zones and
changes in heat conductivity, the slices are compared
Figure 3. Classification of vectors by their heat conductivity
by the SLM machine and the specific hatching algorithms are in general IP of a certain SLM machine
vendor. Therefore little information is available on
3 EQUIPMENT & MATERIALS
3.1 SLM machine of KULeuven – PMA
Figure 5. UMD-splitting: detection of down facing
this topic in literature. However it is noticed that
these scan strategies all look similar. Sectorial scanning (Yasa, et al. 2010) is commonly used by all the
SLM companies. In this research simple zig-zag
scanning patterns are generated and used.
This four step procedure is implemented in an inhouse tool path generator. The final result of the new
vector generation tool is a scanning pattern with geometrical knowledge included: each subsequent scan
vector of the scanning pattern, when generated, receives an identity which is dependent on its geometrical location. The next step is to define optimal process parameters for all these different identities. In
this work only optimization of down facing surfaces
will be discussed.
2.2 Parameter optimization for vector identities
Once all different vectors zones are classified according to their process behavior, the parameters
(scan speed, laser power) for each identity and zone
must be optimized (second branch in Figure 2). To
greatly reduce the amount of trial and error experiments, a numerical model of SLM has been used
which allows estimating the process parameters for
different geometries. This way the number of experiments to optimize the processing of each vector
class can be reduced significantly in comparison
with fully experimental parameter optimization.
Once an estimation of the process parameters is determined for a specific geometrical situation, further
detailed optimization has to be performed with experiments. These experiments aim to find the definite optimized parameter sets for the specific situation. As an example, parameter optimization for
processing of downfacing surfaces will be discussed
in section 4.2.
In this research experiments have been performed on
a home-made SLM machine of KULeuven-PMA.
This machine is equipped with a 300W IPG fiber laser (wavelength 1064 nm) and a spot size of 80 µm
(99%). The central control unit of the machine is a
National Instruments PXI system, equipped with two
field programmable gate arrays (FPGA) to control
the scanner and to process the data of the melt pool
sensors in real-time. The use of in-house developed
software opens opportunities to implement and experiment with own developed hatch strategies and
parameters sets.
3.2 Scan vector generation software tool
An in-house developed software tool has been written which enables to implement different scanning
strategies and the vector classification methods as
described above.
3.3 Material
All experiments are executed with Ti-6Al-4V powder, since the behavior of this material has been
studied extensively (Thijs, et al. 2010). With the already available knowledge, conclusions on melt pool
behavior can be interpreted better.
3.4 Numerical model of SLM
To limit the amount of trial and error efforts in optimizing the SLM process, simulation models are being developed in many research institutes in the last
5 years. The process is a complex combination of
heat flow, fluid dynamics, optics and mechanics. A
total model of the production process has therefore
not yet been developed and is not expected in the
near future. Depending on the aim of the simulations, different models are developed. Each of the
models tries to predict a certain aspect of the process
(e.g.: thermal stress and deformation (Zaeh &
Branner, 2009)). The most interesting models for
this research are models which try to predict the melt
pool behavior.
By modeling the process at micro level and implementing phase transitions the model should be
able to estimate the melt pool size, shape and/or behavior. The literature shows that among others the
numerical models from Gusarov (Gusarov, et al.
2009) and Verhaeghe (Verhaeghe, et al. 2009) have
been able to predict the melt pool behavior. The
model of Verhaeghe et al. will be used in this
research to predict the process parameters for certain
geometries.
as as mentioned are already detected by the UMDsplitting. Therefore no further classification needs to
be done.
4.2 Parameter optimization
Figure 6. Bridge structure: (a) Reference bridge with
dimensions, (b) dross formation without parameter adaptation
4 OPTIMIZATION OF OVERHANG
STRUCTURES
(1)
To show the applicability and utility of the developed methodology (Fig. 2), processing of overhang
geometries will be discussed. As a reference for
these overhang geometries a more specific square
shaped overhang/bridge structure (Fig. 6a) was investigated. Building such a bridge with standard parameters results in big deformations and dross formations (Fig. 6b); sometimes even resulting in
process abortion. Optimizing the parameters should
result in a stable (constant melt pool dimensions in
process) and controllable melt pool in every separate
heat flow situation of the bridge to minimize the
dross formation.
To implement the a priori methodology the two
aspects, vector classification and parameter optimization should be considered.
4.1 Scan vector classification of overhang
structures
In overhang structures mainly two different heat
flow situations can be distinguished: scanning on
loose powder (Fig 4. Overhang heat flow) and scanning on a powder layer with standard heat flow conditions (solid substrate beneath the powder layer, Fig
4. Standard heat flow). To classify the vectors into
the right heat flow situations it is crucial to detect
which area will be scanned on loose powder (down
facing). This down facing and standard (middle) are-
Figure 7: (a) Simulation of overhang structure,
Since a set of parameters is already available for
scanning under standard heat flow conditions (a laser
power of 42W and a scan speed of 225 mm/s), only
a parameter set for scanning the down facing needs
to be determined. The estimation of this parameter
set is done with a numerical model to minimize the
amount of experiments. The further optimization is
done by trial and error experiments.
Parameter estimation by a numerical model
of SLM
The use of a numerical model reduces the intensive
trial and error to find the correct process parameters.
The goal of these simulations is to find an estimation
of stable parameters to control the melt pool volume
and shape in the down facing heat flow condition of
the overhang. Scanning on a lower heat conductive
powder results in larger extended melt pool. Figure 7
depicts the simulated melt pool size when scanning
an overhang using fixed parameters, optimized for
standard heat flow conditions. During the numerical
experiment, in the first 240 µm the laser scans on
solid foundation. Once reaching the bridge overhang,
the heat flow situation switches and the simulation
shows the transformation of the melt pool. The melt
pool will take a half barrel shape (depth = width/2)
as the conductivity is about equal in all the directions
perpendicular on the scan direction (since a single
line track is simulated). Another notable phenomenon is the increase in melt pool length, since heat
can only transfer backwards throughout the solidified scan track.
A melt pool will be called stable when it is not
deeper as the melt pool of the scan line in standard
conditions, to avoid dross formations, and on top of
that its length should be constant. To create such a
stable melt pool it is obvious that the laser power
and scan speed have to be lowered to avoid a deep
melt pool and to give the melt pool time to solidify.
(b) Melt pool behavior during simulation
Figure 9. Microscope image of a cross section of
the bridge deck
Figure 8: Melt pool behavior scanning a powder layer
with x µm solid beneath to conduct the heat (standard parameters 42W 225mm/s)
A set of stable process parameters for melting on
loose powder could be determined after simulating
an amount of parameter sets. The simulations indicated that very low power (4 W) with low scan speed
(70 mm/s) were preferable to create a stable process
to bridge the gap between the two pillars in this heat
flow situation.
During the melt pool simulation it is noticed that
the melt pool behavior changes before reaching the
border of the overhang (Fig. 7). The heat conductivity drops when reaching the border (Fig. 4 Transition
heat flow). This results in an enlargement of the melt
pool. However this zone of 40 µm is too small to define an own set of parameters. Therefore to avoid a
big melt pool at the start of the overhang which results in dross formation, an offset is induced on the
down facing zone to cover this transition zone. This
offset enlarges the down facing area to induce a
smooth transformation of the standard melt pool to a
down facing melt pool and to avoid the dross formation at the border.
Once an estimation of the parameters is defined, it
is useful to estimate the influenced zone of this
overhang (i.e. the amount of solid layers that is
needed to be in standard process conditions.) Simulations showed that with ± 150 µm solid material
above an overhang (5 layers of 30 µm) the process is
back in standard conditions (Fig. 8). Therefore after
scanning 5 layers with the optimal defined parameters for down facing layers, the build can be continued with standard parameters.
(2)
Experimental optimization
By means of the estimated parameters, experiments
have been performed to study and optimize the parameters more into detail. First the estimated parameters where optimized to avoid deformation, this re-
sulted in optimal parameter set (laser power 5W and
scan speed 85mm/s).
These optimized parameter set was used to build
an overhang structure. After scanning 5 layers at the
optimal parameters with a hatch spacing of 20 µm,
the standard parameters, as indicated in the simulations, were used. Dross formation occurred, since the
first 5 layers at low power and scan speed were not
dense enough to conduct the heat towards the pillars.
Therefore two extra zones of 5 layers are put on top
of the first 5 overhang layers of which the parameters incrementally raise towards the standard parameters. A microscope image of the cross section of
a bridge deck is displayed in Figure 9. This figure
shows the 3 increasing parameter sets before the
standard parameters are reached. Reaching the
standard parameters (Fig. 9: above the top line) the
part is almost full dense again. The density of the 10
first layers however is low. This explains why dross
formation occurred, after switching back to standard
heat conductivity parameters after 5 layers. These 5
layers are not able to conduct the heat to the pillars
since they are not dense.
Sandblasting these completed bridges erodes
these first layers. These sacrificial layers however
are necessary to create mechanical resistance against
deformation induced by thermal stress and to create
a heat flow towards the pillars of the bridge.
4.3 Extrapolation of geometry
The methodology of a priori scan strategy adaptation
seems very promising on the reference bridges.
Therefore this building strategy was applied on different bridge dimensions. To check the possible span
width, a long geometry with a gap of 40 mm was
built (Fig. 10). This was so to date not possible with
state-of-the-art job preparation, without any support
structure.
The next test-geometry built, was a long tunnel
with pillars of 3 mm and a span of 10 mm (Fig. 11).
This tunnel with a length of 40 mm was built successfully, however at the entrances of the tunnel deformation occurred by the lack of mechanical resistance (Fig. 11). To solve this issue it is necessary
to use support structures at the entrances.
5.1 Circular attic window
Figure 10: A bridge with a span of 40 mm is produced
To show the utility of the a priori methodology, it is
worth to take a closer look at the circular attic
window (diameter 6.35 mm), which is shown in Figure 13 (a) using fixed parameters and (b) using the a
priori scan parameter adaptation. It shows that the
circular cavity with a priori parameter adaptation has
a more circular shape than the one with fixed parameter sets. Measuring the circularity with an optical
microscope shows that the circularity with parameter
adaptation (0.163 mm) is much better than with
fixed parameters (0.318 mm). This optimized scan
strategy for overhang structures had a remarkable
improvement for this circular cavity.
5.2 Roof of the house
Figure 11: Tunnel with a span of 10 mm and a length
of 40 mm
5 CASE STUDY
To prove the improvements of this a priori parameter
adaptation a house with carport was built as a case
study (Fig. 12). This house was built twice, once
with standard fixed parameters and once with the a
priori adapted scan strategy. There was a clear difference between those two parts. To show these differences two features of the house will be compared.
The circular attic window and roof of the house are
compared further into detail.
In this case study one challenging overhang feature
was designed, namely the top roof of the house. The
size of this overhang is 40 mm by 22 mm. This feature cannot be built properly without support structures with fixed scan parameters. The scan strategy
had a major influence on the quality of this roof.
The roof of the house built without a priori scan
strategy adaptation was rough and deformed by dross
formation and the thermal stresses. The house with
parameter adaptation was built successfully with no
visible deformation and dross formation was avoided
successfully. The surface was even smoother than
any top layer. In Figure 14 a cross section of the roof
of the case study is illustrated. It shows the overhang
of 40mm by 22 mm which was gapped successfully
with a minor amount of dross formation and deformation.
6 CONCLUSIONS
Figure 12: The case study house built in Ti-6Al-4V
Figure 13: Circle cavities build in a 3 mm thick wall
(a) with fixed parameters, (b) with parameter adaptation
for down facing layers
The geometric boundaries of current SLM processes
are limited by the static character of the parameter
sets chosen in the SLM process. Adjusting parameters to the appropriate heat conductivity would push
the boundaries of the SLM process further and
would introduce new possibilities.
As was shown for overhang structures, support
structures can be minimized and quality can be improved by optimizing the job preparation. To execute this optimization it is necessary to use the
knowledge of the CAD model to predict the heat
conductivity.
Many other features which influence the heat
flow can be investigated. However a lot of simulations and trial and error work should be done to find
the correct parameter sets for different situations.
Figure 14: Cross-section of the case study
7 ACKNOWLEDGEMENTS
The authors acknowledge the financial support from
the IWT SBO-project DiRaMaP and the KULeuven
IOF-project IOF-KP/06.
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