Conference PaperPDF Available

Characterization of AM Metal Powder with an Industrial Microfocus CT: Potential and Limitations

Authors:

Abstract and Figures

This study demonstrates that, after adequate scaling error compensation, industrial µ-CT could be a viable metrological technique to derive, with one measurement, primary characteristics of Additive Manufacturing metal powders used in Powder Bed Fusion processes. For distribution and shape analyses, special care must be employed on the ISO value determination prior to surface extraction. The current main limitation relies on the finite focal spot size of the µ-CT X-ray source and consequently on the limited spatial resolution. A local thresholding method has been proposed for surface determination and its statistical validation must be done at different µ-CT scan settings and with different powder specimens. The potential for detecting powder cross-contamination and eventually powder degradation has been introduced and will be the main effort in future studies.
Content may be subject to copyright.
CHARACTERIZATION OF AM METAL POWDER WITH
AN INDUSTRIAL MICROFOCUS CT: POTENTIAL AND LIMITATIONS
Mirko Sinico, Evelina Ametova, Ann Witvrouw, and Wim Dewulf
Department of Mechanical Engineering
KU Leuven
Leuven, Belgium
INTRODUCTION
Final precision of parts produced by Selective
Laser Melting (SLM) and other Powder Bed
Fusion (PBF) technologies relies on material
inputs. Powder characteristics not only influence
part density, mechanical properties and
microstructure but also geometrical parameters,
such as the applicable minimum layer thickness,
the maximum overhang angle and the overall
final surface quality [1]. Effective metrology
techniques are therefore needed to characterize
both particle size distribution and morphology,
and to follow the evolution of these
characteristics during different builds after
powder sieving and reuse.
Cone-beam computed tomography (CT), as
studied for instance in the field of sand and soil
particles characterization [2], has shown a
significant empirical potential as an all-in-one
tool to characterize size and shape at grain
scale. However, compared to these previous
studied applications, SLM powder has much
smaller particles which imposes a new challenge
on CT powder characterization.
CT reconstruction generates a 3D voxelized
attenuation map of the measured object from a
series of 2D projections acquired at different
perspectives. Image segmentation is used to
delineate objects for further analysis. A number
of factors affects the cone-beam CT
measurement performance of micro-objects,
such as spatial resolution loss due to the focal
spot blurring effects and high sensitivity to
inaccuracy in a magnification factor.
This work explores potential and limits of
industrial microfocus CT (µ-CT) as an all-in-one
tool to characterize the morphology of metallic
Additive Manufacturing (AM) powder, the
particles size distribution and, possibly, the
contamination of the feedstock powder.
µ-CT STUDY FOR AM POWDER ANALYSIS
AM powder samples
For the study, we selected three different
powder samples from a specialized AM powders
producer, with D10-D90 of the distribution as
listed in the vendor’s specifications:
- Mar300 15-45: Maraging 300 Alloy Steel
powder 15-45 µm;
- Mar300 5-25: Maraging 300 Alloy Steel
powder 5-25 µm;
- Ti-CP 15-45: Titanium CP powder 15-45 µm.
To confirm the vendor’s specifications and to
validate the subsequent µ-CT analysis, laser
diffraction (LD) measurements were obtained by
means of a Beckman Coulter's LS 13 320. The
underlying mathematics of LD measurements
assume spherical particles to reconstruct the
diffraction pattern. This is a reasonable
assumption for virgin powders, and LD analyses
can be adopted as a reference for the following
µ-CT study.
FIGURE 1. LD distribution curves of Maraging
300 powders.
Particle size distributions from LD are presented
in Figure 1 for the Maraging 300 powders. The
particle size distribution for the Ti-CP 15-45
sample was nearly identical to the Mar300 15-
45, therefore it is not shown in the figure.
Results for all three samples are summarized in
Table 1, with D10, D50, D90 cumulative volume
fractions and the average particle sizes.
TABLE 1. Summary of LD analyses of the three
powder samples.
SAM PL E
D10
[µm]
D50
[µm]
D90
[µm]
AVG
[µm]
Mar300 5-25 5.2 9.6 15.6 9.2
Mar300 15-45 15.9 29.2 44.4 27.6
Ti-CP 15-45 19.8 32.2 44.3 30.5
The average particle size for the Mar300 5-25
sample is out of specifications, presenting a D90
of 15.6 µm instead of 25 µm. This could impede
further investigations of this powder with the
selected µ-CT system due to its finite spatial
resolution.
Experimental setup
Spatial resolution is one of the limiting factors in
µ-CT data analysis and, consequently, there
should be a sufficient distance between two
objects to differentiate them. Separating
particles from one another has been reported as
the main issue in CT metrology of fine metallic
powder [3], and a good dispersion of the
particles in the specimen is advised. Specimens’
preparation for this study is realized by dry
spraying powder on a double-sided tape and
subsequently rolling the tape on a 3 mm wooden
stick. This approach allows to relatively
uniformly distribute the powder particles and to
limit sample dimensions to achieve high CT
magnifications.
µ-CT scans were performed on a Nikon XTH
225ST machine at 80 kV, 80 µA, exposure 2000
ms, and tungsten target. In total, 3600
projections were acquired at a magnification of
69 and reconstructed with a voxel size of 2.9
µm. One of the usual concerns associated with
high magnification scans is blurring due to finite
focal spot size. The total power was therefore
limited to constrain the X-ray gun focal spot size
around 3 µm, which is the smallest possible for
the used machine.
Voxel scaling factor
Discrepancy between nominal and actual
positioning of the sample with respect to the
source and the detector due to machine’s
kinematic errors can introduce significant errors
in dimensional measurements [4]. Given the
high magnification, common artifacts [5] were
not suitable; therefore a new object (“fish-eggs
artifact) consisting of 1 mm high precision
(Grade 10, ISO 3290-1:2014) stainless steel
spheres was designed to calculate the voxel
rescaling factor.
FIGURE 2. (Left) Example of µ-CT projection of
the “fish-eggs” artifact; (Right) after
reconstruction, touching high-precision spheres
are selected for center-to-center measurements.
Spheres were poured in a hollow ~3 mm plastic
cylinder (Figure 2) and the “fish-eggs” artifact
was measured with the µ-CT system at the
same position of the rotation stage and under
the same settings prior to any specimen scan.
Voxel scaling factor was calculated as the mean
value of all center-to-center distances between
spheres in contact; at least 10 pairs of spheres
in contact were considered for each
measurement. Rescaling was consistent
between different scans, with an avg. of 0.9205
± 0.0002 and a standard deviation of 2.6E-4.
Surface determination
In previous studies, authors used a thresholding
binarization of CT data with a subsequent
surface smoothing of extracted voxels clusters
[6], which potentially might introduce
inaccuracies in extracted geometrical
information due to discretization errors and
partial volume effect. In this study, we use
VGSTUDIO MAX 3.1 (abbreviated as “VG”) to
perform surface extraction with sub-voxel
resolution. As a result, we extract the object
surface with no intermediate binarization step.
The standard initial contour value is typically
based on the ISO-50% global thresholding,
selected as the arithmetic mean of the grey
value corresponding to the background and the
grey value corresponding to material peak in the
grey-value histogram. This method was not
applicable to our specimens, since no clear
material peak was detected in the grey value
histograms (top of Figure 3, for Mar300 15-45).
This is caused by the high surface/volume ratio
of the particles, which, for this combination of CT
scan settings and amount of material in the
specimens, induces a smooth transition of grey
values between background and material peak.
Consequently, global thresholding methods are
not suitable for surface extraction of our
datasets.
FIGURE 3. Mar300 15-45 dataset (Top)
Automatic global thresholding ISO-50%;
(Bottom) Local thresholding method, example
for a single particle of the acquired dataset.
Therefore, a local thresholding method was
chosen and its settings were tuned based on the
LD measurements. In general, local thresholding
methods can improve the measurement
accuracy by partial compensation of beam
hardening and other CT artifacts [7]. At the
same time, local thresholding is more time
consuming, settings-sensitive and none of the
methods are suitable for all situations.
Our approach takes in consideration the
development of the grey values local profile for
particles of various size in every data set. The
first derivative of the local profile is analyzed to
find a sharp change in grey level denoting the
edge and, consequently, a particle surface.
Differently from Y. Tan et al. [7] approach, we
always look for the minimum gradient between
the ISO-50% calculated by VG and the ISO
value corresponding to the background peak. An
example of selection of the ISO value for the
Mar300 15-45 is presented in Figure 3 (bottom).
The point of deviation from the linear fit of the
right-hand side gradient corresponds to the min.
of the first derivative of the local profile.
Surface determination is finally run on VG with
the selected ISO value in a standard mode and,
for comparison, with the automatic ISO-50%
starting contour in a local adaptive mode (search
distance of 4 voxels). This procedure is repeated
for all three samples, which resulted in a total of
six datasets.
Data analysis
For every dataset, two analysis procedures are
performed: particles pores content estimation
and size/morphology characterization.
A porosity analysis is performed in VG using the
porosity/inclusion analysis module with a
“threshold only” method. For every dataset, the
threshold value was set to the corresponding
ISO value as discussed in the previous section.
For morphology/size analysis, a region of
interest of ~10000 particles is selected in every
dataset, and the particle surface is converted to
a triangular mesh using “manual” mode with a
volumetric sampling interval of 2 μm.
A particle analysis tool developed in-house in
MATLAB was used to process the extracted
particle surface and to calculate characteristics
such as volume, surface area, principal
dimensions, sphericity, bounding sphere, etc.
RESULTS
Particles porosity analysis
As expected, the amount of porosity in the
selected specimens is almost negligible, due to
the small average size of the particles [8] and to
the high quality production processes (generally
gas atomization for Maraging 300 and plasma
atomization for Titanium CP).
FIGURE 4. Normal distribution of the detected
pores in the Mar300 15-45 powder specimen.
Only the Mar300 15-45 sample presents enough
data points to derive a statistical analysis of the
pores content, see Figure 4. The sample
contained 63023 particles, but only 21 pores
were detected with an average dimension of 12
µm. It must be considered that with a roughly 3
µm voxel size resolution, the internal porosity
content might be underestimated, as reported by
F. Bernier et al. [3], and a CT system with a
higher resolution might provide a better
estimation of the total amount of porosity.
Powder distribution analysis
Since particles were distributed on tape using
dry spraying, there were multiple particles either
touching each other or located too close to
resolve them in CT scans. Therefore, prior to
calculation of particle distribution and
geometrical characteristics, we filtered out all
possible artificial agglomerations.
Filtering is implemented in the MATLAB code
based on principal dimensions (with a ≥ b ≥ c):
only spheroids are selected (Figure 5, a & b) for
size distribution estimation and shape analysis,
corresponding to particles with principle
dimensions ratios b/a > 2/3 and c/b > 2/3.
Moreover, particles with a total volume below 8
voxels are also filtered out in 15-45 datasets, to
avoid potential background noise [9] and as no
particles of this size are expected in those
specimens.
FIGURE 5. Examples of a) a particle with
satellites, b) a spherical particle, and c) artificial
particles agglomeration (subsequently filtered)
for the Mar300 15-45 powder.
Even if supported by previous studies [2,9], this
approach will limit further investigations on
powder reuse and powder degradation. The
proposed approach for particle filtration is hence
applicable only for virgin high-quality powder. A
new filtering method, or increased CT scan
resolution is required for more advanced powder
metrology.
Obtained cumulative distributions are presented
in Figure 6. For every specimen we compare the
LD measured distribution against the µ-CT
results.
FIGURE 6. (Top) Mar300 5-25; (Middle) Mar300
15-45; (Bottom) Ti-CP 15-45; calculated
distributions, comparison of LD vs µ-CT.
µ-CT analysis of the Mar300 5-25 was
performed without filtering on size, since a
significant fraction of the particles volume falls
below a total volume of 8 voxels. While
circumscribed sphere measurements can still be
accomplished on those small particles fractions,
erroneous segmentation due to CT noise,
b)
a)
c)
significant blurring and limited data points for
surface extraction hamper the data analysis of
the Mar300 5-25 specimen. For those reasons,
we suggest that specimens with particles below
~8 µm should not be investigated with our
current µ-CT setup, and no shape analysis has
been performed on the Mar300 5-25 dataset.
On the other hand, implementation of the local
thresholding was successful for all the
specimens. Almost a perfect agreement with LD
is found on the average particles size, and total
size spread. Differences in the skewness of the
distributions might be explained by the
differences of the two measuring methods:
- Particle size is obtained as a deconvolution of
the diffraction patterns for LD vs a 3D shape
analysis (circumscribed sphere) for µ-CT;
- Sample measured by LD contained >>10000
vs ~10000 particles measured with µ-CT;
- Sample preparation is obtained with water
dispersion plus sonication for LD vs dry
spraying for µ-CT.
As has been reported in different studies [1,10],
skewness and shape of the powder distribution
are highly dependent on the instrument, which
must be carefully selected based on powder
characteristics and application.
One clear outcome of this investigation is that
standard global thresholding ISO-50% method
with this µ-CT setup fails to provide a reliable
powder distribution, even with local adaptive
surface determination mode: overestimation of
the ISO value for surface determination will
always lead to a huge underestimation of the
particles size.
Powder shape analysis
Shape is a critical aspect for AM metal powders,
since shape will influence the flowability and its
apparent/tapped density. Different shape
parameters have been developed overtime to
describe powders, mainly taking in consideration
only the 2D shape that might result from an
optical/SEM microscopy or laser beam based
measurement. Qualitative descriptions of
powder particles have been as well standardized
[11], and out of all possible metrics, circularity
and aspect ratio are the most commonly used
shape factors for AM powder. A single 2D shape
factor cannot completely describe a particle
shape, and assumptions must normally be made
for every specimen.
The power of µ-CT analyses relies on the fact
that a full 3D shape is acquired, providing
domain specialists with comprehensive
information which potentially can be integrated
into multi-criteria decision making approaches
and advanced SLM process simulations. As an
example, for every powder specimen, 3D shape
factors like roundness, sphericity and
compactness can be calculated using our in-
house MATLAB code. Sphericity results are
summarized in Figure 7 for both Mar300 15-45
and Ti-CP 15-45 samples.
FIGURE 7. Sphericity index for Mar300 15-45
and Ti-CP 15-45.
As expected, a shape analysis results in high
sphericity for both specimens, with Ti-CP being
overall slightly more spherical.
New 3D shape indicators, such as the possibility
to count and describe particles satellites, as well
as more advanced 3D shape analyses [2] will be
the focus of future work.
PRELIMINARY STUDY ON
MULTIMATERIAL µ-CT POWDER ANALYSIS
A preliminary study is hereby introduced to
explore more advanced applications of CT to
metallic powder characterization. Besides
powder porosity, distribution and shape, µ-CT
has already been used to detect cross-
contamination of powder batches either in the
final printed part [12] or in the raw material state
[13]. Although detection was successful for both
cases, a precise assessment was not possible
because of the difficulties in resolving touching
particles and in the multi-material surface
determination.
To overcome those limitations, a new approach
is developed and implemented in MATLAB. The
main intuition relies on the fact that even if no
well-defined and separated material peaks are
present on the scans, a thresholding ISO value
can be selected looking at the local gray
FIGURE 8. Schematic of the multi-material µ-CT powder analysis to detect cross-contamination of
powder batches; example for an artificial contaminated Mar300 15-45 specimen with 10 vol% Ti-CP.
values profile to iteratively isolate the
contaminant from the main powder batch. To
confirm this assumption, an artificial
contamination is produced on a Mar300 15-45
specimen, with 10 vol% amount of Ti-CP. The
full analysis process is schematized in Figure 8.
A total contamination of 9.7 vol% of Ti-CP is
retrieved with the implemented code, in good
agreement with the prepared sample.
Undergoing efforts are focused on the statistical
validation of the implemented method,
comparing results at different percentages of
artificial contamination for different materials.
CT users must be aware that powders of
materials with similar densities will not be
distinguished with current CT machine setups.
Nonetheless, theoretically, even oxides of the
same material could be distinguished if the
difference in X-ray attenuation is high enough,
and µ-CT analysis could consequently
investigate not only powder cross-
contamination, but also powder degradation
after sieving and reuse.
CONCLUSIONS
After adequate scaling error compensation, the
acquired data set demonstrates how µ-CT could
be a viable metrological technique to derive, with
one measurement, primary characteristics of
PBF starting materials.
For distribution and shape analyses, special
care must be employed on the ISO value
determination prior to surface extraction. The
current main limitation relies on the finite focal
spot size of the µ-CT X-ray source and
consequently on the limited spatial resolution.
Statistical validation of the proposed local
thresholding method for surface determination
must be done at different µ-CT scan settings
and with different powder specimens.
The potential for detecting powder cross-
contamination and eventually powder
degradation has been introduced and will be the
main effort in future studies.
ACKNOWLEDGEMENTS
This research was funded by The EU
Framework Programme for Research and
Innovation - Horizon 2020 - Grant Agreement No
721383 within the PAM2 (Precision Additive
Metal Manufacturing) research project.
REFERENCES
[1] Sutton AT, Kriewall CS, Leu MC, Newkirk JW. Powder characterisation
techniques and effects of powder characteristics on part properties in
powder-bed fusion processes. Virtual and Physical Prototyping. 2017
Jan 2;12(1):3–29.
[2] Su, D., and W. M. Yan. “3D Characterization of General-Shape Sand
Particles Using Microfocus X-Ray Computed Tomography and Spherical
Harmonic Functions, and Particle Regeneration Using Multivariate
Random Vector.” Powder Technology 323 (January 1, 2018): 823.
[3] Bernier, Fabrice, Rui Tahara, and Mathieu Gendron. “Additive
Manufacturing Powder Feedstock Characterization Using X-Ray
Tomography.” Metal Powder Report, February 2018.
[4] Stolfi, A., and L. De Chiffre. “3D Artefact for Concurrent Scale Calibration
in Computed Tomography.” CIRP Annals 65, no. 1 (2016): 499502.
[5] Müller P, Hiller J, Dai Y, Andreasen JL, Hansen HN, De Chiffre L.
Quantitative analysis of scaling error compensation methods in
dimensional X-ray computed tomography. CIRP Journal of
Manufacturing Science and Technology. 2015 Aug;10:68–76.
[6] Slotwinski, JA, EJ Garboczi, PE Stutzman, CF Ferraris, SS Watson, and
MA Peltz. “Characterization of Metal Powders Used for Additive
Manufacturing.” Journal of Research of the National Institute of
Standards and Technology 119 (September 16, 2014): 46093.
[7] Tan Y, Kiekens K, Kruth J P, Voet A and Dewulf W. Material dependent
thresholding for dimensional x-ray computed tomography. Int. Symp. on
Digital Industrial Radiology and Computed Tomography (Berlin,
Germany, 2011).
[8] Guo, Rui-Peng, Lei Xu, Bernie Ya-Ping Zong, and Rui Yang.
“Characterization of Prealloyed Ti–6Al–4V Powders from EIGA and
PREP Process and Mechanical Properties of HIPed Powder Compacts.”
Acta Metallurgica Sinica (English Letters) 30, no. 8 (August 2017): 735
44.
[9] Pavan, Michele, Tom Craeghs, Raf Verhelst, Olivier Ducatteeuw, Jean-
Pierre Kruth, and Wim Dewulf. “CT-Based Quality Control of Laser
Sintering of Polymers.” Case Studies in Nondestructive Testing and
Evaluation 6 (November 2016): 62–68.
[10] Iacocca, R. G., and R. M. German. "A comparison of powder particle
size measuring instruments." International journal of powder metallurgy
33.8 (1997): 35-48.
[11] ASTM B243-17, Standard Terminology of Powder Metallurgy, ASTM
International, West Conshohocken, PA, 2017.
[12] Jamshidinia, M, P Boulware, J Marchal, H Mendoza, L Cronley, S Kelly,
and S Newhouse. “In-Process Monitoring of Cross Contamination in
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM).” In
Solid Freeform Fabrication 2016, 15, 2016.
[13] Brandão, Ana D., Romain Gerard, Johannes Gumpinger, Stefano
Beretta, Advenit Makaya, Laurent Pambaguian, and Tommaso Ghidini.
“Challenges in Additive Manufacturing of Space Parts: Powder
Feedstock Cross-Contamination and Its Impact on End Products.”
Materials 10, no. 5 (May 12, 2017).
... Particle size distributions have been assessed using XCT [245] in view of, e.g., developing a better understanding of the influence of powder characteristics on the eventual properties of powder bed fusion AM workpieces [246]. It has been shown that XCT based characterization of powder using principal component analysis outperforms conventional 2D data-based methods for particle analysis, whereby the latter overestimated particle mass by a factor of 2.3 [224]. ...
Article
X-ray computed tomography (XCT) is increasingly being used for evaluating quality and conformance of complex products, including assemblies and additively manufactured parts. The metrological performance and traceability of XCT nevertheless remains an important research area that is reviewed in this paper. The error sources influencing XCT measurement results are discussed, along with related qualification, calibration and optimization procedures. Moreover, progress on performance verification testing and on the determination of task-specific measurement uncertainty is covered. Results of interlaboratory comparisons are summarized and performance in various dimensional measurement fields is illustrated. Conclusions and an outlook for future research activities are also provided.
... In addition, the amount of projections per scan assures that no image quality degradation was introduced by a limited number of projection views [41]. Furthermore, a scan of the reference sample described in [42] (called "fish-eggs" artefact) was acquired at the same voxel size. The sample contains calibrated features, which allows to calculate a voxel scaling factor to compensate for possible scaling errors during the multiple batch scans. ...
Article
Channels and bores in metal components produced by laser powder bed fusion (LPBF) are internal features that are typically affected by defects such as dross and sag formation, dimensional errors and global deformations in different proportions. Such deviations from the ideal geometry may strongly limit the functionality of the channels, but are difficult to prevent, due to complex multi-physical production aspects. Different destructive and non-destructive approaches are available to investigate the geometry of the internal features and possibly correlate their results to the LPBF process parameters; however, such approaches do not offer a systematic method to derive key characteristics of the main contributors for channel deviations. Hence, this work proposes a novel tomographic non-destructive analysis of LPBF channels and bores, focusing on the derivation of sag and dross key parameters. The methodology works on polar-transformed profiles obtained from image stacks which are extracted perpendicularly to the channel axis from the X-ray computed tomography (CT) reconstructed volume. The method allows for the clear determination of surface characteristics and includes the quantitative evaluation of descriptors through an algorithm specifically developed for the purpose. In particular, general form deviations are addressed by fitting sinusoidals on the unwrapped mean surface profile, to tackle deviations induced by thermal residual stresses. Proposed descriptors of sag and dross are the onset angle of protrusions, separation criteria between sag and dross effects, and the peak analysis of the mean profile after approximation with a least squares spline. The developed algorithm is tested in the case study of a LPBF AlSi7Mg0.6 benchmark part comprising hollow cylinders and inter-connecting frusta with different diameters. The resulting evaluation of the benchmark part also corroborates how the proposed methodology can help to obtain more precise information regarding the correlation of LPBF fabrication conditions and obtained channels geometrical deviations. Furthermore, the results show possible routes to enable an a-priori compensation of the nominal channel design for first-time right LPBF manufacturing.
... • Particle size and distribution -the standard powder size distribution, as used in the L-PBF process, is between 15 µm and 45 µm (Sinico et al. 2018). The particle size and the size distribution significantly affect the packing density. ...
... The detection of contaminant particles in AM feedstock has also been reported by some authors. 8,9 In the present paper, a method is described to inspect metal powder by µXCT for contaminant particles. It shall be seen as a contribution to the standardization effort that is required in the AM community for the quality inspection of metal powders. ...
Conference Paper
Full-text available
X-ray computed tomography (XCT) has been used for more than two decades to support the development of additive manufacturing (AM). However, most of the efforts have been put on the printed components: geometry evaluation, dimensional inspection and porosity measurement. More recently, microfocused x-ray computed tomography (µXCT) has been used to characterise morphology and porosity of 3D printing metal powders. This paper shows that this technique can also be used to assess the contaminant content in AM metal powders as well. As a demonstration, results obtained with deliberately contaminated powders of different alloy families are presented. So far, the technique has been used mostly for titanium because of its applications in aerospace and medical devices; two manufacturing fields subjected to severe regulations and strict qualification processes. Detailed description of the method used for titanium is presented. For contaminants having a density significantly above the one of titanium, the method has a sensitivity of 0.005 ppm vol. Finally, future perspectives are discussed.
Book
Additive manufacturing (AM) is a fast-growing sector with the ability to evoke a revolution in manufacturing due to its almost unlimited design freedom and its capability to produce personalised parts locally and with efficient material use. AM companies, however, still face technological challenges such as limited precision due to shrinkage, built-in stresses, and limited process stability and robustness. Moreover, often post-processing is needed due to the high roughness and remaining porosity. Qualified, trained personnel are also in short supply. In recent years, there have been dramatic improvements in AM design methods, process control, post-processing, material properties and material range. However, if AM is going to gain a significant market share it must be developed into a true precision manufacturing method. The production of precision parts relies on three principles: 1. Production is robust (i.e. that all sensitive parameters can be controlled). 2. Production is predictable (for example, the shrinkage that occurs is acceptable because it can be predicted and compensated in the design). 3. Parts are measurable (as without metrology, accuracy, repeatability and quality assurance cannot be known). AM of metals is inherently a high-energy process, with many of sensitive and inter-related process parameters, making it susceptible to thermal distortions, defects and process drift. The complete modelling of these processes is beyond current computational power and novel methods are needed to practicably predict performance and inform design. In addition, metal AM produces highly textured surfaces and complex surface features that stretch the limits of contemporary metrology. With so many factors to consider, there is a significant shortage of background material on how to inject precision into AM processes. Shortage in such material is an important barrier for a wider uptake of advanced manufacturing technologies and a comprehensive book is thus needed. This book aims to inform the reader how to improve the precision of metal AM processes by tackling the three principles of robustness, predictability and metrology, and by developing computer-aided engineering methods that empower rather than limit AM design.
Article
Full-text available
This work studies the tensile properties of Ti-6Al-4V samples produced by laser powder bed based Additive Manufacturing (AM), for different build orientations. The results showed high scattering of the yield and tensile strength and low fracture elongation. The subsequent fractographic investigation revealed the presence of tungsten particles on the fracture surface. Hence, its detection and impact on tensile properties of AM Ti-6Al-4V were investigated. X-ray Computed Tomography (X-ray CT) scanning indicated that these inclusions were evenly distributed throughout the samples, however the inclusions area was shown to be larger in the load-bearing plane for the vertical specimens. A microstructural study proved that the mostly spherical tungsten particles were embedded in the fully martensitic Ti-6Al-4V AM material. The particle size distribution, the flowability and the morphology of the powder feedstock were investigated and appeared to be in line with observations from other studies. X-ray CT scanning of the powder however made the high density particles visible, where various techniques, commonly used in the certification of powder feedstock, failed to detect the contaminant. As the detection of cross contamination in the powder feedstock proves to be challenging, the use of only one type of powder per AM equipment is recommended for critical applications such as Space parts.
Article
Full-text available
Laser Sintering (LS) is an Additive Manufacturing (AM) technology for polymers processing which is increasingly being used to produce functional products with designs not achievable with traditional manufacturing technologies. Lightweight cellular structures are a good example of complex designs which are increasingly finding applications in AM parts. However, it is not yet clear how the LS process affects the porosity and geometrical characteristics of the cell structural elements. Getting this information allows to perform quality control of the LS process, gives insights into how to improve it, and might help to take into account manufacturing process variability during the design phase. In this work a test artifact containing cylindrical elements with diameters in the range typically used in lightweight cellular structures is used to investigate the influence of features' size and printing orientation on the porosity and shape deviation of each feature. In order to assess the reproducibility of the process, several replicas of the test object are produced in polyamide-12 (PA12) using the same LS process conditions. An X-ray Computed Tomography (CT)-based quality control approach, which uses both image processing of CT-slices and porosity analysis (porosity content, pores count and pores volume distributions) is used to gather the information.
Article
Full-text available
Additive manufacturing (AM) techniques(1) can produce complex, high-value metal parts, with potential applications as critical parts, such as those found in aerospace components. The production of AM parts with consistent and predictable properties requires input materials (e.g., metal powders) with known and repeatable characteristics, which in turn requires standardized measurement methods for powder properties. First, based on our previous work, we assess the applicability of current standardized methods for powder characterization for metal AM powders. Then we present the results of systematic studies carried out on two different powder materials used for additive manufacturing: stainless steel and cobalt-chrome. The characterization of these powders is important in NIST efforts to develop appropriate measurements and standards for additive materials and to document the property of powders used in a NIST-led additive manufacturing material round robin. An extensive array of characterization techniques was applied to these two powders, in both virgin and recycled states. The physical techniques included laser diffraction particle size analysis, X-ray computed tomography for size and shape analysis, and optical and scanning electron microscopy. Techniques sensitive to structure and chemistry, including X-ray diffraction, energy dispersive analytical X-ray analysis using the X-rays generated during scanning electron microscopy, and X-Ray photoelectron spectroscopy were also employed. The results of these analyses show how virgin powder changes after being exposed to and recycled from one or more Direct Metal Laser Sintering (DMLS) additive manufacturing build cycles. In addition, these findings can give insight into the actual additive manufacturing process.
Article
To answer the need for efficient quality control protocols for additive manufacturing processes and materials, specific testing methods for powder feedstocks should be developed. A powder feedstock may contain some defects, such as porosities, that will remain in the final parts after the building process. X-ray tomography combined with 3D image analysis offers unique advantages over other characterization methods, such as pycnometry and metallography, in respect to quantifying internal porosity in the individual particles of the feedstock. This paper presents the effect of X-ray tomography parameters on the quality of the obtained images and its impact on the image analysis. An automated image analysis routine was also developed to allow the visualization of the pores inside the particles but also, more importantly, to quantify this internal porosity contents, as well as to provide information on the morphological features of these pores, such a size distribution, number of particles containing pores and the volume fraction of a pore inside a particle.
Article
The morphological features of sand particles play a key role in the mechanical response of the particle assemblage. Advancement of microfocus X-ray computed tomography (μXCT) technology has enabled 3D visualization of particles at the grain-scale with reasonably high resolution to reveal the particle morphology. This paper utilizes the real part of spherical harmonic (SH) functions to describe the morphology of general-shape sand particles acquired from μXCT images. The influence of the maximum degree of SH functions and mesh fineness on the determination of size and shape descriptors of the particles are systematically investigated. Correlations between different shape descriptors of the studied sands are examined. Utilizing principal component analysis (PCA) and the empirical cumulative distribution function (ECDF), a probabilistic approach considering both intrinsic and phenomenological correlations between SH coefficients is proposed to three-dimensionally regenerate the sand particles. Based on comprehensive and quantitative comparisons between the morphological characteristics of scanned and generated particles, we conclude that the proposed approach performs satisfactorily.
Article
Prealloyed Ti–6Al–4V powders were prepared by electrode induction melting gas atomization (EIGA) and plasma rotating electrode process (PREP) in this work. A comparative study of EIGA and PREP powders for hot isostatic pressing (HIPing) compaction was conducted. Characterization of important technological parameters such as particle size distribution, powder surface morphology and flowability was carried out. Microstructure and mechanical properties of Ti–6Al–4V powder compacts HIPed from EIGA and PREP powders were also investigated. The results showed that the EIGA powder has a finer average particle size and higher tap density, while the PREP powder has better flowability and less pores. Micropores can be observed in heat-treated EIGA powder compacts by X-ray tomography and the porosity was found to be about 0.02%. There are no micropores (≥4 μm) to be detected in heat-treated PREP powder compacts. Transgranular fracture mode as well as micropores contributes to the scatter in fatigue property of heat-treated PREP powder compacts. The respective advantages and disadvantages of both EIGA and PREP powders for producing Ti-based complex parts through HIPing were also discussed.
Article
Powder-bed fusion is a class of Additive Manufacturing (AM) processes that bond successive layers of powder to facilitate the creation of parts with complex geometries. As AM technology transitions from the fabrication of prototypes to end-use parts, the understanding of the powder properties needed to reliably produce parts of acceptable quality becomes critical. Consequently, this has led to the use of powder characterisation techniques such as scanning electron microscopy, laser light diffraction, X-ray photoelectron spectroscopy, and differential thermal analysis to study the effect of powder characteristics on part properties. Utilisation of these powder characterisation methods to study particle morphology, chemistry, and microstructure has resulted in significant strides being made towards the optimisation of powder properties. This paper reviews methods commonly used in characterising AM powders, and the effects of powder characteristics on the part properties in powder-bed fusion processes.
Article
A novel artefact for calibration of the scale in 3D X-ray Computed Tomography (CT) is presented. The artefact comprises a carbon fibre tubular structure on which a number of reference ruby spheres are glued. The artefact is positioned and scanned together with the workpiece inside the CT scanner providing a reference system for measurement. The artefact allows a considerable reduction of time by compressing the full process of calibration, scanning, measurement, and re-calibration, into a single process. The method allows a considerable reduction of the amount of data generated from CT scanning. A prototype was calibrated and its applicability demonstrated.
Article
Eight different powders commonly employed in powder injection molding were used as the basis for examíng the effects of powder characteristics on particle size measurement, Several different techniques were applied to measure particle she, including: laser diffraction with the powder dispersed both wet and dry, aerodynamic time-of-flight, electrical zone sensing, dynamic light scattering or photon correlation spectroscopy, and optical image analysis. After reviewing the particle size data obtained from these different techniques, it is concluded that accuracy is dependent strongly on dispresion of the powder in the carrier fluid. With adequate dispersion analogous particle size information is obtained independent of instrument type.
Article
X-ray Computed Tomography (CT) has become an important technology for quality control of industrial components. As with other technologies, e.g., tactile coordinate measurements or optical measurements, CT is influenced by numerous quantities which may have negative impact on the accuracy and repeatability of dimensional and geometrical measurements. The aim of this paper is to discuss different methods for the correction of scaling errors and to quantify their influence on dimensional measurements. Scaling errors occur first and foremost in CT systems with no built-in compensation of positioning errors of the manipulator system (magnification axis). This article also introduces a new compensation method for scaling errors using a database of reference scaling factors and discusses its advantages and disadvantages. In total, three methods for the correction of scaling errors – using the CT ball plate, using calibrated features measured by CMM and using a database of reference values – are presented and applied within a case study. The investigation was performed on a dose engine component of an insulin pen, for which several dimensional measurands were defined. The component has a complex geometry and is made of brass, which makes its measurements with CT challenging. It is shown that each scaling error correction method results in different deviations between CT measurements and reference measurements from a CMM. Measurement uncertainties were estimated for each method, taking into consideration the contributions related to the applied correction method. The newly suggested approach using the database appeared to work well, indicating, that if the properties of a CT system under investigation are monitored using a reference object (ball bar in our case), a correction factor based on individual selected magnification factors can be applied for scaling error correction of any object, and thus no additional scanning of a reference object is needed.