The document discusses in-situ process monitoring techniques for selective laser melting (SLM). It describes how process signatures like melt pool size and electromagnetic signals provide information about energy input and process instabilities. Key techniques mentioned include monitoring melt pool size using cameras and photodiodes, measuring electromagnetic signatures, and imaging the powder bed. Developing correlations between sensor data, process variables, and part quality is important for process control and reducing post-production inspection needs.

1. Notes on in-situ process
monitoring of selective laser
melting
By: Dr. Khuram Shahzad
Contact: engineerkhuram@gmail.com
1

2. Use of In-Situ Sensing for the Assessment of
Part Quality
• Voice of Process: Process signature provides the
voice of process which ultimately shall be linked
to part quality metric
• SLM process signature: dynamic characteristics of
heating, melting and solidification of the powder
feed material. Most obvious process signatures
are:
• Physical size of melt pool: physical sized of melt pool
provides the information about the energy input and
instabilities in the process. Like too high input of
laser energy leads to the formation of large melt pool
and satellite formation. Whereas too low laser
energy causes balling in melt pool.
• Electromagnetic signature
• Radiation emission from the melt pool in accordance with
temperature
• Formation of plasma pume above the melt pool,
containing ionized plasma from build environment as well
metal vapors.
2Thomas G. Spears and Scott A. Gold, In process sensing in Selective laser melting (SLM) additive manufacturing, Spears and Gold Intergrating Materials and Manufactureing Innovation (2016) 5:2, DOI 10,1186/s40192-016-0045-4

3. Use of In-Situ Sensing for the Assessment of
Part Quality
• Key process variables and key process signatures have a potential to become
a part of large quality and process control plan.
• The ultimate goal of process monitoring is to ensure the quality of the fixed
parts reducing the need for costly, time consuming post processing
inspection and to develop real time process control
• To achieve this goal will require which many process variables and process
signals and combinations thereof provides the most valuable information
while the same time being assessable to measurement and analysis. This
evaluation will require how SLM process variables and process signatures are
related to another and ultimately how both relate to part quality metric.
These correlations unfortunately not well understood well at this time.
• The key strategic focus of SLM in process sensing development work is on
identifying and understanding these quantitative correlations. Ideally, the
theoretical models that provide insight in to the process physics would
provide this information. From a practical standpoint, empirical correlations
will provide significant value and enable in-process sensing to be used for
quality monitoring and process control. Wheather, theoretical or empirical,
these kinds of quantitative relations are critical to making process
intelligence gained from a monitoring systme actionable,
3Thomas G. Spears and Scott A. Gold, In process sensing in Selective laser melting (SLM) additive manufacturing, Spears and Gold Intergrating Materials and Manufactureing Innovation (2016) 5:2, DOI 10,1186/s40192-016-0045-4

4. Use of In-Situ Sensing for the Assessment of
Part Quality
• In process modulaties can be broadly categorized in to
• Monitoring of process inputs are predefined variables
• Machine measurements
• Powder property measurement
• Voice of the process
• Acoustic signature of the weld pool
• Electromagnetic signature  mostly used
• Lagrangian (i.e., moving with the melt pool)
• Eulerian (i.e., fixed position)
• Powder bed imaging  mostly used
4Thomas G. Spears and Scott A. Gold, In process sensing in Selective laser melting (SLM) additive manufacturing, Spears and Gold Intergrating Materials and Manufactureing Innovation (2016) 5:2, DOI 10,1186/s40192-016-0045-4

5. Use of In-Situ Sensing for the Assessment of
Part Quality
• Powder property measurement
and control
• Powder thermal and morphological
properties have a great influence
• Thermal conductivity  influenced
by density of powder bed
• Chemical composition can change
by recycling, oxygen concentration
can increase. Ratio of version and
recycled powder is maintained
• Particle size distribution can change
by recycling, as it is more likely that
the finer particles will be consumed
more than coarser particles
• Environmental effect like humidity
can also influence the flow
properties
Lucy Grainger, Investigating the effects of multiple powder re-use cycle in AM, Renishaw 5

6. In-Situ Process Sensing: State of the Art
• Voice of process measurement: Electromagnetic melt pool monitoring
• These signatures include melt pool geometrical measures and electromagnetic
addition from melt pool and associated plasma pume
• Sensors utilized for evaluating these signals can be classified broadly as
• spatially integrated such as photodiodes and pyrometers
• Spatially resolved such as cameras
• Spectrally resolved i.e., spectrometers
• Sensor should have very fast response time and high degree of spatial
resolution matching the laser scanning rates (100 to 1000 mm/s) and laser
focus area in the order of 10-100 μm
Thomas G. Spears and Scott A. Gold, In process sensing in Selective laser melting (SLM) additive manufacturing, Spears and Gold Intergrating Materials and Manufactureing Innovation (2016) 5:2, DOI 10,1186/s40192-016-0045-4 6

7. • Co-axially installed IR high speed
camera and photodiode are installed.
An additional IR translucent static
optical mirror in optical path between
laser and scanning system allows the
on axis detection of melt pool
emission.
• Camera has resolution 35 micron and
a sampling rate of 15 kHz, where as
photodiode operates at 50kHz, the
collected data of melt pool emissions
are correlated to corresponding
scanner and processed in an
industrial PC. The QM Meltpool 3D on
the PC computes a grey scale map as
TIF file. The generated image files can
be analyzed during or after
manufacturing process.
Thomas Toeppel, et al., 3D Analysis in laser beam melting based on real-time process monitoring, https://www.concept-laser.de/fileadmin/user_upload/PDFs/Meltpool3D-Fraunhofer-Studie.pdf 7
In-Situ Process Sensing: State of the Art

8. • QM Meltpool 3D offers completely new possibilities for quality inspection, materials and process development.
• This is a fundamental step towards the self regulating SLM process control, before that extensive research is needed to establish a
correlation between sensors signals and process conditions.
Thomas Toeppel, et al., 3D Analysis in laser beam melting based on real-time process monitoring, https://www.concept-laser.de/fileadmin/user_upload/PDFs/Meltpool3D-Fraunhofer-Studie.pdf
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In-Situ Process Sensing: State of the Art

9. • The optical tomography (OT) system monitors
the exposure operation during the SLM
process. OT system records all the radiations
during the SLM process and any changes in
radiations are associated with variations in
SLM process and potentially to the formation
of defects.
• Resolution of camera = 100 μm
• The image data acquired with high frequency
are stored in one image for each layer
• 3D images are constructed from 2D image
layers.
• A co-relation between defects and variations
in OT images can be found.
• Development of process control is still under
the development
• EOS has adapted the technology and selling
under the name of EOSTATE MeltPool
Joachim Bamberg, et.al., Process monitoring of additive manufacturing by using optical tomography, DOI: 10.1063/1.4914606.
https://www.eos.info/software/monitoring-software/meltpool-monitoring 9
In-Situ Process Sensing: State of the Art

10. • Co-axial monitoring of melt pool using pyrometer
• For melt pool monitoring, SLM solutions is using an on-axis tool to
determine the thermal radiation from the melt on the powder bed.
The process radiation occurring from the exposure of several
vector types and the locally welded powder particles is a thermal
signal diffusely emitted from the melt pool. Using pyrometers,
measurements are taken at a rate of up to 100,000 times per
second and the data is recorded in real time and saved layer by
layer.
• The recorded data enables analysis to identify potential
irregularities during fusion that can indicate anomalies during the
build. The system also supports the efficient further development
and evaluation of process parameters
• Advantages
• Measurement parameters considered independent of material
• Sequential output of thermal emission plot images of individual
layers
• Supports the development and evaluation of process parameters
M. Pavlov, Pyrometric analysis of thermal processes in SLM technology, Physics Procedia 5 (2010) 523-531,
http://slm-solutions.us/process-monitoring/
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In-Situ Process Sensing: State of the Art

11. • Use of IR camera also provides
an other option of non-contact
temperature measurement
• Krauss et al. investigated limits
for detecting pores (100 μm)
and other irregularities caused
by insufficient heat dissipation
during laser-PBF processing.
• A part of the build plate form
was observed, the setup can
not be installed inside the,
shielding gas is needed to
protect the damage during
laser scanning and for cleaning.
H. Krauss, et.al., Thermography for monitoring the selective laser melting process, 23rd international solid freeform fabrication symposium; Austin. TX, 2012.
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In-Situ Process Sensing: State of the Art

12. • Along with Powder bed
imaging is also performed to
detect the defects in powder
layer deposition and in the
parts due to curling.
• Concept laser is selling a
system QM coating
• Other QM tools are also
available to monitor the laser
power and atmosphere of
the chamber.
Kruth et.al, Online quality control of selective laser melting. 22nd international solid freeform fabrication symposium; Austin. TX, 2011.
S. Klesczczynski, et al., Error detection in laser beam melting system by high resolution imaging. 23rd international solid freeform fabrication symposium; Austin. TX, 2012.
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In-Situ Process Sensing: State of the Art