www.laser-journal.de
Optical In-Process Monitoring of
Direct Metal Laser Sintering (DMLS)
A revolutionary technology meets automated quality inspection
Thomas Grünberger and Robert Domröse
Additive Manufacturing (AM) is a
revolutionary technology enabling
the users to design and manufacture
products in a completely new way. As
the AM technology is proceeding from
rapid prototyping to rapid manufacturing, industries ranging from aerospace and medical (Fig. 1) to energy
and automotive require detailed documentation of the process. The market
demands automated in-process quality
inspection. The goal is to get a better
understanding of the process and assure repetitive high-end part quality.
Plasmo, a worldwide leading engineering partner for quality inspection systems in the field of laser and joining
technologies cooperates with EOS, the
technology and market leader for the
DMLS technology, and with selected
industrial partners to develop a diode
based in-process monitoring system.
Direct Metal Laser Sintering
Direct Metal Laser Sintering (DMLS) is
an AM process [2] by which digital 3D
design data (Fig. 2 a) are used to build
up a component in layers by depositing
metal material. The system starts by applying a thin layer of the powder material to the building platform (Fig. 2 b).
After each layer a laser beam then fuses
the powder at exactly the points defined
by the computer-generated data, using a
laser scanning optic (Fig. 2 c). The platform is then lowered and another layer
of powder is applied (Fig.  2 d). Once
again the material is fused so as to bond
with the layer below at the predefined
points resulting in a complex part.
Thereby not only the part but also the
final material is created in the process
and defines the unique characteristics
of this technology. Every single welding line creates a new micro segment
40 Laser Technik Journal
2/2014 Fig.1 Additive manufacturing of dental crowns and bridges, material EOS Cobalt Chrome
SP2 [1]
of the final part and can therefore be
monitored. Stacking all monitoring information on top of each other, we can
visualize a 3D model of the part quality
(Fig. 2 e) [3].
Part quality, quality assurance
Besides others [4, 5, 6, 7], the final part
quality is heavily influenced by various
factors: the powder material, exposure
parameters such as laser power, scan
speed and exposure-strategies, as well
as the inert gas flow and temperatures in
the process chamber. Potential negative
phenomena in part quality can occur
e.g. porosity, lack of fusion and rough
surfaces. There are several strategies to
enable quality assurance during the process, such as system monitoring, camera
based powder bed monitoring and diode-, pyrometer- or camera-based inprocess monitoring. In the following
section the technology diode-based inprocess monitoring is described.
Diode based in-process monitoring – basic layout
The basic layout of the system shows
Fig. 3. Light emissions emitted from the
process are measured by photo diodes
Fig. 2 Description of DMLS technology, [3]
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Process Monitoring
gorithms need less computational effort
compared to evaluations in frequency
domain enabling a real time implementation. Therefore, the user gets the information during the building process and
can change the settings immediately.
using the plasmo® fastprocessobserver.
Photodiodes can be mounted in lateral
“off-axis” configuration with direct
view on the process. Photodiodes can
also be mounted in “on-axis” or ‘coaxial’
configuration – integrated to the optical
path via a beam splitter, which allows
view through the scanner, along with
the laser beam. The measured diode
signals are transmitted to the plasmo®
in-process monitoring system where
the appropriate visualizations and
evaluations are performed. The quality
inspection results are transferred to the
machine PLC via a machine interface.
A suitable wavelength region for reception of the observed light must be
choosen. This is done by careful selection of photodiode types (e.g. silicon or
germanium diodes have different sensitivity in the visible and near infrared
spectrum) and spectral filter types.
The building process is highly dynamic. Due to the high scanning speed,
a sufficiently high sample rate has to be
used. The hardware is capable of sampling rates up to one sample per second.
Usually, the sample rate is reduced in order to reduce the data amount. Experiments have been done to determine a
sample rate appropriate for the process
dynamics. Nevertheless it turned out
that a high sample rate should be used.
The great advantage of this high sampling rate compared to camera based inprocess monitoring systems is to detect
high dynamic effects like keyhole dynamics. Spatial information can be obtained by using neighboring exposure
lines and layers.
For developing algorithms and proving
the reliability of a quality inspection system it is necessary to have NOK samples
(defect population) and OK samples
(baseline population). In many cases real
defects in production are hardly available, so it is necessary to simulate defects.
Example: Building of a cube of
7 × 7 mm base area. At the beginning
nominal exposure parameters are used,
later the laser power is reduced by
10% of its nominal value. The resulting part quality was inspected by computer tomography (Fig. 4 a) obtaining
a NOK part containing a pore of size
0.18 mm × 0.18 mm × 0.18 mm [9].
In a first step characteristic values
like the mean value of the measurement
are plotted over its corresponding position at the building platform layer per
layer (Fig. 4 b shows this for a layer with
the given pore) in false color representation (dark red equals high mean value,
dark blue equals low mean value). The
defect can easily be seen by a lowered
signal level, which can also be found in
Fig. 4 c in the time series plot.
Algorithms like short term fluctuations in combination with spatial information enable the user to detect this
behavior automatically.
Evaluation principles
Example “Splashy” process
It has been revealed successfully, that
changes in process parameters like e.g.
laser power, focus position and scan
speed have an influence on the resulting
measurements, as well as on part properties.
To evaluate monitoring data automatically, analysis can be done in
time, frequency and time scale domain
(wavelet transformation).
Algorithms in time domain known
from e.g. remote laser welding – like
absolute signal limits, signal dynamics
and short term fluctuations [8] – are applicable to the DMLS process with slight
modifications in parameters caused by
higher process speeds. Time domain al-
Changes in exposure parameters by the
user may yield in an operating point
close to stable process boundaries or
even an unstable process. It is possible
to detect such unstable processes like a
splashy process, so the user gets the information during the building process
and can change the settings immediately saving metal powder and machine
time.
Fig. 5 shows at the left side (green
marked area) an image of an OK process
and the corresponding sensor signal in
time domain compared to an NOK process (Fig. 5 right side red marked area).
The bottom plot shows the calculated
Fourier transform of the measured sig-
Example porosity
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Fig. 3 System layout diode based in-process monitoring
nal over time. The changes in process
can be detected by a more noisy signal
and by upcoming higher frequencies in
frequency domain.
Summary and Outlook
Benefits of diode based in-process monitoring
Diode-based in-process monitoring
systems especially using high sampling
rates offer great opportunities for the
analysis of fast processes like the DMLS
process in time and frequency domain
to guarantee constant high and repetitive part quality. Calculation and visualization of measurement characteristics
lead to a better understanding of the
process and enable a faster development
of new and optimization of existing processes.
Using spatial information from the
building process enables a parameterization for different sizes of defects depending on the need of the customer.
Fig. 4 Example detection of porosity, Source Fotec [9]
Laser Technik Journal
2/2014
41
www.laser-journal.de
The hardware of the system is independent from the processed metal
powders. Only the parameterization
of the OKNOK evaluation needs to be
adapted individually.
The system enables the user to perform process and quality documentation following international standards.
Challenge
Fig. 5 “Splash” process, image of process, sensor signal and
analysis in frequency domain
Next Steps
Companies
plasmo Industrietechnik GmbH
Vienna, Austria
plasmo is a global high-tech provider of automated quality inspection systems for the
manufacturing industry. Established in 2003, it’s
headquartered in Vienna and has a network of
international distributors. The expertise ranges
from control of welding processes, monitoring
of weld seams, geometric shapes and surfaces,
laser power measurement, up to tailored solutions in the field of machine vision and analysis
software. A wide range of services with comprehensive engineering rounds off the portfolio.
A leading position in the market, the company
occupies in the real-time quality control for
assembly processes.
www.plasmo.eu
EOS GmbH - Electro Optical Systems
Founded in 1989 and headquartered in Germany,
EOS is the technology and market leader for
design-driven, integrated e-Manufacturing solutions for Additive Manufacturing (AM), an industrial 3D printing process. EOS offers a modular
solution portfolio including systems, software,
materials and material development as well as
services (maintenance, training, specific application consulting and support). As an industrial
manufacturing process it allows the fast and
flexible production of high-end parts based on
3D CAD data at a repeatable industry level of
quality. As a disruptive technology it paves the
way for a paradigm shift in product design and
manufacturing. It accelerates product development, offers freedom of design, optimizes part
structures, and enables lattice structures as
well as functional integration. As such, it creates significant competitive advantages for its
customers.
www.eos.info
Laser Technik Journal
In a next step, the system will be qualified for additional defects and further
automation. Also, the intelligent combining integration of other monitoring technologies such as camera-based
powder bed monitoring will be done.
The presented results in porosity
detection are developed in the project
PAM – Powder Additive Manufacturing. It is supported by the Austrian
Research Promotion Agency FFG and
carried out by FOTEC Forschungs-
2/2014 References
[1] EOS GmbH – Electro Optical Systems: press
material – images and applications (2012).
[2] A. Gebhardt: Generative Fertigungsverfahren, Hanser Verlag, München (2007).
[3] EOS GmbH – Electro Optical Systems: EOS
system data sheet M 280 (2012).
[4] J. Kruth et al.: Part and material properties
in selective laser melting of metals. Proc.
16th Int’l Symp. Electromachining (ISEM
XVI), Shanghai (2010).
[5] A. B. Spierings, M. Schneider, R. Eggenberger: Comparison of density measurement techniques for additive manufactured
metallic parts, Rapid Prototyping J. (2011)
380–386.
[6] L. E. Murr et al.: Microstructure and mechanical behavior of Ti-6Al-4V produced
by rapid-layer manufacturing for biomedical applications, J. mech. behav. biomed.
mater. (2009) 20–32.
[7] S. Bremen, W. Meiners, A. Diatlov: Selective
Laser Melting – A manufacturing technology for the future? Laser Tech. J. 9 (2012) 2,
33–38
[8] plasmo Industrietechnik GmbH, Operating manual processobserver advanced,
www.plasmo.eu
[9] Fotec Forschungs- und Technologietransfer GmbH, www.fotec.at
[10]University of Applied Sciences Wiener
Neustadt, www.fhwn.ac.at
DOI: 10.1002/latj.201400026
Authors
Thomas Grünberger
Krailing, Germany
42 One challenge due to high sampling
rates of the system is the development
and implementation of a data flow
model, which can handle big data (approximately 10 MByte/s are generated
by the diode based system). Appropriate data compression strategies, which
are already available at plasmo can be
implemented.
und Technologietransfer GmbH [9]
together with the University of Applied
Sciences Wiener Neustadt [10], TU Vienna and plasmo.
Dr. Thomas Grünberger
received his PhD at
TU Vienna, in 1997.
Since then he has
been scientist at
Vienna University of
Technology (Institute
for Electrical Control
Engineering), continued as scientist a t Forschungszentrum
Seibersdorf (Department Quality Assurance
for Production). His actual position is CTO
of plasmo Industrietechnik GmbH which he
cofounded 2003 in Vienna. Dr. Grünberger
is lecturer at FH Wiener Neustadt and
president of Austrian Association for
Thermography.
Robert Domröse
Robert Domröse is
R&D project leader at
EOS. Since joining
EOS in 1999, he held
various positions in
the R&D department,
with focus on development of hardware
and systems. He is a
specialist for lasers, optics, metrology and
laser processing of metals. In his current
projects, he is dealing with process monitoring of metal additive manufacturing processes. Robert Domröse holds a diploma
in physics from University of Regensburg,
Germany.
Dr. Thomas Grünberger, plasmo Industrietechnik GmbH, Dresdner Str. 81-85, 1200 Vienna/Austria,
E-mail: thomas.gruenberger@plasmo.eu
Robert Domröse, EOS GmbH – Electro Optical Systems, Robert-Stirling-Ring 1, D-82152 Krailling
E-mail: robert.domroese@eos.info
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim