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The Role of Computed Tomography in Additive Manufacturing

Conference Paper

The Role of Computed Tomography in Additive Manufacturing

Abstract and Figures

Additive manufacturing (AM) technologies—or 3D printing, as they are popularly known—show promise as a way to transform traditional production manufacturing because they can produce highly complex geometries and customized parts directly from the part design model without dedicated tooling. However, many questions about the structural integrity of 3D printed parts—tolerance limits, layer defects, residual stress, and material inclusions—remain unanswered because AM process parameters and disruptions during the material layering (generally in powder form) may induce a variety of dimensional deviations and internal flaws (e.g., cracks or voids) in the final product. These flaws might affect the performance of AM devices and create risks of potential failures, so the support of metrology and non-destructive testing (NDT) techniques for better assessment of AM parts is needed [1-5]. One of the challenges to the assessment of AM parts is that many will have internal features, these are generally inaccessible from the outside to vision and contact-based inspection techniques for quality control. While destructive methods can be used to extract measurements, dimensional information from the disassembled state of a product may differ from the actual geometrical dimensions in the original assembled state. This paper describes some of the main issues associated with the measurement of AM parts and some future trends for the development of alternative techniques for measuring complex shaped AM parts. Along with a brief discussion of limitations of traditional inspection technologies, such as coordinate measuring machines (CMMs) and optical-based systems, the particular case of X-ray computed tomography (CT) as a technology to support AM inspection and development is discussed. The benefits of X-ray CT for the assessment of the structural integrity of AM parts and deviations typically encountered in AM dimensional geometry when compared to reference/nominal geometry will be considered.
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AMERICAN SOCIETY FOR PRECISION ENGINEERING 201
THE ROLE OF COMPUTED TOMOGRAPHY IN ADDITIVE MANUFACTURING
Herminso Villarraga-Gómez1, 3, Christopher M. Peitsch2, Andrew Ramsey3, and Stuart T. Smith1
1Center for Precision Metrology, University of North Carolina at Charlotte, NC, USA
2Johns Hopkins University Applied Physics Laboratory, MD, USA
3Nikon Metrology, Inc., Brighton, MI, USA
INTRODUCTION
Additive manufacturing (AM) technologiesor
3D printing, as they are popularly knownshow
promise as a way to transform traditional
production manufacturing because they can
produce highly complex geometries and
customized parts directly from the part design
model without dedicated tooling. However, many
questions about the structural integrity of 3D
printed partstolerance limits, layer defects,
residual stress, and material inclusionsremain
unanswered because AM process parameters
and disruptions during the material layering
(generally in powder form) may induce a variety
of dimensional deviations and internal flaws
(e.g., cracks or voids) in the final product. These
flaws might affect the performance of AM
devices and create risks of potential failures, so
the support of metrology and non-destructive
testing (NDT) techniques for better assessment
of AM parts is needed [1-5].
One of the challenges to the assessment of AM
parts is that many will have internal features,
these are generally inaccessible from the
outside to vision and contact-based inspection
techniques for quality control. While destructive
methods can be used to extract measurements,
dimensional information from the disassembled
state of a product may differ from the actual
geometrical dimensions in the original
assembled state. This paper describes some of
the main issues associated with the
measurement of AM parts and some future
trends for the development of alternative
techniques for measuring complex shaped AM
parts. Along with a brief discussion of limitations
of traditional inspection technologies, such as
coordinate measuring machines (CMMs) and
optical-based systems, the particular case of X-
ray computed tomography (CT) as a technology
to support AM inspection and development is
discussed. The benefits of X-ray CT for the
assessment of the structural integrity of AM
parts and deviations typically encountered in AM
dimensional geometry when compared to
reference/nominal geometry will be considered.
AM QUALIFICATION CHALLENGES
Due to its almost unlimited capability to produce
highly complex geometries and customized
parts directly from the part model, AM
techniques have recently received significant
attention as technologies that may have the
potential to transform traditional manufacturing
techniques as a more cost-effective production
process. AM is being considered for safety-
critical components, such as those found in
propulsion engine components and customized
biomedical implants. Consequently,
development of AM technologies should be
accompanied by quality control processes and
inspection technologies. The need for
established methods of AM qualification has
been previously highlighted by several authors
[1-7]. The rationale underpinning the call for AM
qualification usually appeals to one or more of
the following necessities: to understand the
processing techniques and geometry impact on
AM material properties; to understand and
develop AM process optimization; to design
methods of engineering that take advantage of
the AM possibilities; to identify suitable parts for
AM; to develop qualification standards for AM
materials and quality control processes; and to
develop in-situ measurement systems for
inspecting AM processes. Whereas some
researchers may stress the importance of raw
material control and identifying the key variables
impacting manufacture, others tend to
accentuate a qualification approach that focuses
on inspecting finished AM parts by combining
the existing knowledge of qualification methods
for conventional manufacturing processes. In
reality, these approaches are related and
connected because the impact of product
geometry on the material microstructure is an
inherent characteristic of the AM processes that,
in turn, directly affects the final product.
However, the qualification of finished AM parts is
challenging because such parts can be complex
in form (e.g. freeform-shaped parts). They
typically have a high surface roughness, and
they may also have internal geometry with
interconnected structures and channels. As
202 2018 ASPE and euspen Summer Topical Meeting Volume 69
such, they can potentially contain voids and
inclusions (such as un-melted particles or
powder residues) inside their volume. These
challenges make it difficult for conventional NDT
techniquessuch as ultrasonic, infrared, eddy
current, radiographic inspection, and light-based
technologiesto achieve comprehensive
inspections on the quality of AM parts. X-ray CT
is an alternative technique that proves useful for
examining parts of complex internal geometry
and can quantitatively identify many of the
aforementioned defects. Although it still has
limitations related to resolution, sensitivity, and
speed, industrial CT equipment continues to
develop and improve technologically. As a
result, X-ray CT has been successful in
addressing some of the quality inspection needs
for the AM industry and is increasingly used as a
tool for qualifying AM parts.
FIGURE 1. Comparison of some relevant NDT
techniques according to detectable defect
location and their achievable spatial resolution.
Adapted from [8].
Figure 1 shows a comparison of NDT
techniques typically used for defect detection in
AM parts for dimensional measurements, which
is categorized according to detectable defect
location and spatial resolution. Accordingly,
optical methods can achieve very high
measuring resolutions, in the range of
nanometers when interferometry techniques are
employed, but they can only inspect defects at
the surface (or through a surface opening).
Eddy-current testing and ultrasonic techniques
can detect defects within the volume if they are
not located very deep inside the testing sample,
but the one drawback is the limited spatial
resolution of detection, which is in the millimeter
range or some fraction of millimeters in the most
optimal situations and for even more limited
depths into the surface. In contrast, the best
method for nondestructive inspection of complex
structures and geometries inside the volume of a
part is currently X-ray CT, with a resolution from
millimeter to micrometer ranges, and sub-micron
levels in some special cases, e.g., by using
synchrotron X-ray sources or with the help of
focusing elements such as Fresnel zone plates
or Kirkpatrick-Baez mirrors [9-12].
FIGURE 2. Basic setup for an X-ray CT scan.
After collecting several radiographic images
also known as projection images (or simply
‘projections’)—taken from a large number of
different angular positions around the specimen
during a 360º rotation, with the aid of a
reconstruction algorithm, it is possible to
reconstruct a three-dimensional image of the
object detailing all of its internal and external
features.
X-RAY COMPUTED TOMOGRAPHY
Since CT shows several advantages as a non-
destructive method for acquiring structural
characterization of both internal and external
geometries of AM parts, it is often the only viable
option to extract component dimensions of
internal or hidden features [13-16]. This makes
CT technology a partner in the “3D printing
revolution” for the study and inspection of AM
products. Thus X-ray CT is typically used to
detect porosity and cracks in AM parts,
dimensional deviations from computer-aided
AMERICAN SOCIETY FOR PRECISION ENGINEERING 203
design (CAD) models, and powder residues or
inclusions left inside the AM built part. Figure 2
shows a typical CT setup featuring the scanning
of an AM acetabular hip prosthesis cup made of
Ti-6Al-4V and produced by an electron beam
melting process, which has a prescribed non-
uniform lattice structure forming struts over the
surface with an interconnected porosity (to
stimulate bone adhesion). In recent work [17],
along with the use of image processing software
such as MATLAB and ImageJ, X-ray CT has
been used to provide quality inspection and
characterization of the porous structure that
affects the implants fixation. In such an
application, the non-destructive and non-contact
nature of CT shows a clear advantage over
other inspection methods, such as tactile CMMs
and optical systems; it provides an analysis of
interconnectivity of the porous structure, the
standard deviation of the size of the pores and
struts, and the local thickness of the lattice
structure in its size and spatial location [17].
Additional examples of the X-ray CT usage for
inspection of AM parts and processes can be
found elsewhere, e.g., [18-19].
In general, tactile CMMs or optical measuring
instruments like laser scanners are limited to the
measurement of the external surface of an AM
part and can provide additional measurements
for partial qualification of CT measurements. In
addition, tactile CMMs can produce compressive
stresses and friction during sliding that could
produce wear at the surface [20-21]. In contrast,
X-ray CT eliminates the above difficulties
because it is a non-contact technique that can
access internal features. Since the principles of
X-ray CTwhen used for obtaining dimensional
measurementsdiffer substantially from tactile
CMMs and optical or light-based systems, each
technique has its own distinctive attributes that
accentuate and limit their capabilities and range
of applications. Table 1 shows a comparative list
of some of the main factors influencing the
metrological performance of tactile CMMs,
optical systems, and X-ray CT machines.
Naturally, the capability of defect detection by X-
ray CT depends very much on the part’s size,
complexity, and material, but in principle, high
scanning metrological resolutions in the order of
10 or 5 µm are possible [22-23].
Table 1. Main influencing factors that can affect the performance of optical, tactile, and X-ray CT
inspection systems for dimensional measurement tasks [21].
Optical
Tactile
X-ray CT
Resolution limit (diffraction)
Diameter of stylus tip
Optical magnification
Stylus shaft length and stiffness
Sensor receptor gain/reading
Sampling strategy
Surface slope or tilt
Number of probing points
Surface roughness/finish
Surface characteristics
Object geometry and size
Object geometry and size
Illumination type/intensity
Object clamping/mounting
Material transparency/translucency
Probing speed/force/direction
Surface reflectivity/opacity
Probe calibration
Edge sub-pixel interpolation
Fitting algorithms
Environmental conditions
Environmental conditions
DEFECT ANALYSIS WITH X-RAY CT
Recently, there has been a growing interest in
the determination of material porosity in AM
manufactured parts, e.g., see [24-26]. In cases
of AM processes that use metal powders, the
powder typically comes in solid grains, and if
such grains are fine, the powder particles are
within the size range of 15 µm to 150 µm [27].
However, some of the powder particles might be
hollow, i.e., some of them may come with pores
inside them, as can be seen in Figure 3. In
addition to porosity, particle size distribution,
morphology, heterogeneity, geometry, and grain
microstructure are of particular interest in AM
powders [27-31]. There are certain anomalies
that could potentially induce defects in powder-
based AM parts. For example, powders with
uniform size distribution are known to promote
homogeneous melting, and good interlayer
bonding [29]. In contrast, powders prone to form
grain agglomerates may assume irregular
shapes or lead to porosity in the component
when metal AM laser or electron beam melting
is used. As can be seen from the right image (in
Figure 3), most grains of aluminum powder
seems to have shapes that are non-spherical, of
204 2018 ASPE and euspen Summer Topical Meeting Volume 69
different size, and irregular morphology. Several
of them are agglomerated in small clusters. In
addition, several grains show porosity channels
or voids in their internal structure. In contrast, as
seen on the left side of Figure 3, Inconel powder
grains seem to be consistently round, almost
identical in size, and relatively small as
compared to the aluminum powder. However,
some of the Inconel powder grains seem to still
exhibit micro-porosity structures inside them.
The presence of these channels or
microstructures in the powder material could
potentially influence the porosity percentage of a
finished AM part built from such material or
induce fractures and cracks inside by the lack of
fusion voids. In the current literature, there are
several studies on the influence of powder
related parameterssuch as particle size,
uniformity, shape and distribution, or grain
porosityon the final result of an AM process,
including evidence of part defects induced by
unmolten powder particles or agglomeration of
powder trapped in the inside, e.g., see [28-33].
Figure 5 illustrates the application of X-ray CT
for the inspection of porosity in a finished AM
part, revealing a variety of voids of different size,
shape, and frequency distribution.
FIGURE 3. A composed X-ray CT image of two
different raw AM powders that reveals porosity
inside the AM particles. Left: nickel-chromium
alloy (Inconel 625). Right: aluminum 225 mesh
(65 µm). Data were obtained using identical CT
geometrical configuration (with a Nikon XTH 225
ST system) but different X-ray power settings:
93 kV and 50 µm for the Inconel sample, and 60
kV and 95 µA for the aluminum powder.
In a broader sense, when testing and qualifying
AM components, it is vital to understand defects
effects on the structural performance and overall
quality of the final product [4, 24-26]. For
example, along with an additional AM cylindrical
rod part acting as witness coupon’, the turbine
blade from Figure 5 has previously been used to
illustrate the usefulness of CT data on the study
of property/characteristics transfer from a
witness coupon to a component (made at the
same time and of the same material as the
actual component). Initial results of these
studies, as presented elsewhere [4], show that
non-fracture-critical properties may have a much
lower dependency on the defect distribution in
comparison to fatigue.
FIGURE 5. X-ray CT porosity analysis of an AM
turbine blade, of approximately 5.5 cm height,
made of Ti-6Al-4V, and produced by electron
beam melting process.
In actuality, measuring and characterizing each
internal defect from the CT data can be a
daunting task, mainly due to the sheer quantity
of data that resides in high-resolution, volumetric
CT datasets. Fortunately, many tools are
available to assist in the measurement and
analysis of large quantities of internal defects,
e.g., see [34-37], and automated defect
recognition algorithms are increasingly being
implemented in practice, including tools for
image processing and statistical analysis
techniques that catalogue and summarize
defects inside a particular volume. In the end,
whether or not to pass or fail an AM component
will need to rely upon comprehensive knowledge
of the defects and their relationship to material
properties and input process parameters.
Additional testing and more advanced analysis
may be required to establish the proper baseline
to determine what level of defects constitutes a
failed part.
AMERICAN SOCIETY FOR PRECISION ENGINEERING 205
FIGURE 6. X-ray CT inspection of an AM test part (designed and fabricated by NIST) with an internal
structure intended to recreate flaws inside the part. The test part was made of a nickel-chromium alloy
(Inconel 625) and produced by a laser-based powder bed fusion process. The color-coded bar features
dimensional deviations in the geometry of the AM part when compared to the nominal intended design,
i.e., with respect to the reference/nominal geometry of the CAD model.
DIMENSIONAL METROLOGY OF AM PARTS
CT technologies are particularly useful to study
the outcome of AM processes. The appeal of CT
in dimensional metrology is evident in the recent
growth of surveys in the field, e.g., see [38-41],
where X-ray CT is considered a tool for
dimensional quality control and geometrical
tolerance verification of industrial components.
One of the critical steps in evaluating AM
performance of different systems would be to
intentionally place simulated flaws of variated
geometrical shapes inside the AM part designs.
Then, after manufacturing, these features can
be evaluated to identify the limits inherent to the
AM process. Figure 6 presents one of these
case studies that has been performed in
collaboration with NIST (the National Institute of
Standards and Technology) [37]. From the
measurements of actual to nominal comparison,
variations in dimensional geometry were
detected for the AM parts with deviations up to
±0.1 mm with respect to reference geometry
(nominal data). Material inclusions in the internal
cavities were also detected as remnants from
the AM process. The design of test parts for the
evaluation of AM machines dimensional
performance has also been a subject of recent
investigations. One example is the test object
shown in Figure 7, which was first proposed by
NIST [42]. Given its original geometry and large
dimensions, such an object was designed so
that the final AM part could be measured by
CMM and surface texture instruments. However,
it is not surprising to see researchers trying to
use it for X-ray CT measurement, which, for a
part with such high aspect ratios is challenging
with metals.
FIGURE 7. Solid model of a test part designed
and proposed by NIST for AM performance
evaluation and standardization purposes [42].
FIGURE 8. CT image of an AM built prototype of
the NIST test part. The arrows in the figure point
toward features and dimensions from nominal
geometry of the NIST design (see Figure 7).
206
Metal parts often scatter X-rays, which may
disrupt the CT reconstructions and produce
unwanted artifacts in the data. Using a 2D fan
beam of X-rays and a linear detector can reduce
the scattering of X-rays and allow high quality
CT data to be obtained. A highly penetrating
beam, from a small X-ray source, is required for
scanning highly absorbent materials such as
nickel alloys. Figure 8 shows an image obtained
by 2D fan beam X-ray CT of an AM prototype,
made of Inconel 625, for the NIST test part. A
Nikon XTH 450 system was used for CT data
acquisition. For this sample, 420 kV was used
with several millimeters of tin and copper filters
to harden the polychromatic X-ray beam and
reduce beam hardening artifacts. The image in
Figure 8 was composed by joining two different
halves of CT sectional slices, both extracted
from equal separation distance from the ‘top
surface’ but opposite in direction: the left half is
0.4 mm above from the ‘top surface’ and the left
half is 0.4 mm below from the same surface. In
future work, the authors plan to evaluate
dimensional geometry assessments of the NIST
test part based on three-dimensional CT data.
NEW PERSPECTIVES
One important focus of current research efforts
in AM is to understand how process parameters
influence defect formation in AM builds. Like any
manufacturing process, there exists a large
combination of input parameters (i.e., laser
power, build speed, layer thickness, etc.) that
can be chosen by the user before, during, and
after the manufacturing process [43]. A
complete, universal understanding of all of these
inputs and how they affect the overall
performance of a component is difficult to
determine. Mathematical models based on
empirical test data are one of the tools most
commonly used for understanding the effects of
AM process parameters [44]. To this end,
statistical analysis of defect data can be used to
determine the quality of a build. Given the
correct design of experiments, optimization of
process parameters can be performed to
minimize the quantity and severity of internal
defects. Given the vast amount of input
parameters in many AM methods, this is not a
speedy process. In many cases, it may require a
large number of samples and large quantities for
CT inspection. To shorten the time and effort in
this process, some more recent research is
leveraging advanced analytical methods such as
artificial intelligence and machine learning
algorithms [45]. These tools have the ability to
analyze the complete defect data for a large set
of samples to uncover hidden patterns and
dependencies that may exist between the
defects and input parameters, not apparent in
other methods. There has also been growing
interest in finding new in-situ techniques that can
collect in-process data to feedback into the AM
systems. This process would allow corrective
actions by optimizing the parameters during the
AM process. Integrating NDT techniques
capable of sub-surface inspection during the AM
process, such as X-ray CT, seem to be a
challenging task, although some progress has
been recently reported by using simple X-ray
imaging [46-47] and X-ray phase contrast [48].
On the other hand, there has been growing
interest in the possibility of using X-ray CT data
for obtaining surface topography measurements
of AM internal geometries without destroying or
sectioning the part [49-52].
In general, a simple workflow optimization
process to improve design and production of AM
parts could be as follows: knowing the deviation
from the intended form and dimensions of the
design helps to quickly refine the model for an
AM part (see Figure 9).
FIGURE 9. An iterative and cyclical optimization
process for design and production in AM parts.
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... This is important because with a destructive method the dimensions extracted from the disassembled state of a product may differ from the actual geometrical dimensions in the original assembled state. At the present time, for example, X-ray CT based inspection is of particular relevance to the industry of additive manufacturing (AM) as a viable option to explore the internal structure of AM parts and potentially answer questions about their structural integrity, tolerance limits, residual stresses, and a variety of dimensional deviations and internal flaws (e.g., cracks or voids) that may affect the functional performance of AM devices or create risks of potential failures [179,190,191,192,193,194,195,196]. Tactile coordinate measurement machines (CMMs 1 ) or optical measuring instruments like a laser scanner can measure the external surface of a part, but not internal structures inaccessible to tactile or vision-based inspection. ...
... Along with the discussion presented here about the differences between the measurement principle of CT and other measurement systems, Table 4 shows a comparative list of some of the main factors influencing the metrological performance of tactile CMMs, optical systems, and X-ray CT machines. Table 4 Brief summary of some of the main influencing factors that contribute to uncertainties of measurements performed with optical, tactile, and X-ray CT inspection systems for dimensional metrology tasks [108,179]. ...
... a) Comparison of some relevant NDT techniques according to detectable defect location and achievable spatial resolution, adapted from[165,179]; (b) Typical spatial resolutions that are achievable by multi-scale CT as a function of sample size (based on[108,180]): conventional CT or macro CT (>10 μm), micro CT (currently >3 μm), nano CT (currently >0.4 μm), synchrotron CT (currently >0.2 μm), synchrotron CT with Kirkpatrick-Baez mirrors (currently >0.04 μm), and focused ion beam tomography (FIBT, currently >0.01 μm). The diagrams presented in this figure are drawn only for conceptual illustration. ...
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The authors regret to inform that Figure 8 on the original article “X-ray computed tomography: from medical imaging to dimensional metrology” [Precis. Eng. 60 (2019) 544-569] needs to be corrected. The authors would like to apologize for any inconvenience caused.
... CT has a negligible response time due to the speed of light. CT is also immune to electrical noise or interference [2,18,[60][61][62][63]. ...
... To deal with the large amounts of sample data that must be considered for determining ideal process parameters, techniques like machine learning and artificial intelligence are used. These methods decrease the time and effort to calculate the parameters through statistical methods [63][64][65]. ...
... Inexpensive instruments can increase the market for CT to make it feasible for budget-friendly experiments and applications and by instruments that the workman can efficiently operate. The product should direct the person working with it and reduce the risk of maloperation [21,39,63]. ...
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Additive manufacturing is the process of producing three-dimensional objects in a layer-by-layer technique. Due to its ability to produce complex parts directly without any additional machining, it has found a valuable place in the manufacturing sector of the industry with applications ranging from producing minutely detailed body implants to the nose of spacecrafts. Different types and processes of additive manufacturing will be discussed. Since many shapes and complexities can be produced, it is difficult to test these products for defects using conventional methods. To overcome this difficulty and to analyze the parts nondestructively, optical tomography is used. A detailed study of optical tomography is done. We will be surveying how this method helps identify the porosity and defects in additively manufactured parts nondestructively, making this method efficient and economical. This paper will discuss the advantages of using optical tomography to analyze different materials used in additive manufacturing.
... The presence of AM defects, such as pores, lack of fusion or cracks, can lead to catastrophic failure of thin AM walls and clustered holes (Villarraga-Gómez et al. 2018;Sanaei et al. 2019). Moreover, Sanaei et al. (2019) reported that surface proximity can negatively influence defect size and density, which can affect functionality (or the outcome of a qualification test) in a component with a large number of surface holes. ...
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The uncertainties and variation of additive manufacturing (AM) material properties and their impact on product quality trouble designers. The lack of experience in AM technologies renders the experts' assessment of AM components and the establishment of safety margins difficult. Consequently, unexpected qualification difficulties resulting in expensive and lengthy redesign processes might arise. To reduce the risk of qualification failure, engineers might perform copious time-consuming and expensive specimen testing in early phases, or establish overconservative design margins, overriding the weight reduction benefits of AM technologies. In this article, a model-based design method is proposed for the conceptual design of AM space components with affordable test phases. The method utilizes fuzzy logics to systematically account for experts' assessment of AM properties variation, and to provide an early estimation of a product qualification likelihood related to design parameters of interest, without the need for copious testing. The estimation of qualification likelihood can also point out which are the unique AM material uncertainties that require further specific testing, to enable the design of a product with a better performance and more affordable test phases. The method is demonstrated with the design for AM gridded of ion thrusters for satellite applications.
... Prior work in using dimensional metrology with X-ray CT has shown its efficacy in geometric dimensioning and tolerancing of metallic components [1][2][3][4]. The workflow elements involved in the analysis include tomographic reconstruction, surface determination (segmentation or thresholding) and some dimensional measurements. ...
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High-resolution X-ray computed tomography (CT) instruments, also known as three-dimensional (3D) X-ray microscopes, can be adapted for dimensional metrology applications such as geometric dimensioning and tolerancing of metallic components. However, CT scanning times can be prohibitively high for industrial measurement inspection tasks owing to the poor contrast from X-ray attenuation in Ferrous metals, especially if the measurement of spatial resolutions under 5 µm are required. This paper describes a software-defined approach to dramatically reducing total exposure time (or scanning time) while maintaining resolution loss within 2 micrometers as compared to the baseline scans acquired over 6 hours. Here, we combine two deep learning (DL) codes in our surface extraction workflow to compensate for lower signal-to-noise ratio in short exposure data (acquired with lower number of projections): (1) a surface determination (post-reconstruction) , and (2) a denoising algorithm (pre-reconstruction). Training data was acquired from a scan of an 8-hole automotive fuel injector (sample 1) with a 165 µm nominal diameter per hole. For testing the accuracy of the workflow, a separate scan of a 6-hole side-mount injector (sample 2) was acquired. For both samples, the acquired X-ray projections (or radiographs) were binned down to 10X such as to simulate faster scans. For training and testing workflows, the full exposure scans (baseline) were used as target and the shorter exposure scans as inputs to the deep learning models. To determine loss of surface accuracy from the baseline case, a metric is formulated (in micrometers) and the trends are reported for when the total measurement time was reduced by up to 10X (up to 0.6 hours, using only 360 projections). We report that scan times can be reduced by over 10X while retaining the limiting the resolution loss to under 1 micrometer.
... It is particularly relevant in the medical field, both for diagnostic purposes and for more advanced applications such as guiding surgical operations [24]. Reconstruction speed requirement is also present in the industry, for such applications as quality control on assembly lines [25]. Another important issue of computed tomography is radiation exposure, since only a small class of studied objects has radiation resistance. ...
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The purpose of this research is to develop a novel approach to benchmark smart community health centers in order to achieve continuous improvement of service quality. Three methods are presented: the fuzzy DEMATEL method is used to determine the criteria weights, the fuzzy ELECTRE III method is employed to obtain the ranking of smart community health centers, and IPA (Importance-Performance Analysis) is employed to formulate improvement strategies. The proposed approach clearly identifies the strengths and weakness of each smart community health center by ranking its performance with respect to a system of five service quality criteria. In addition, IPA is able to develop the most effective improvement strategies for each smart community health center. The proposed approach was applied to five smart community health centers in Beijing and service strengths and weakness are discussed. The proposed approach has three notable advantages. First, the novel approach can address ambiguity and uncertainty in the process of decision making. Second, interdependent relationships among the evaluation criteria are analyzed by the fuzzy DEMATEL method, so that the weights obtained are more in line with reality. Third, the fuzzy ELECTRE III method considers non-compensatory behavior for service quality comparisons among smart community health centers. The novel fuzzy-based approach presented in this paper is a powerful and highly effective tool to benchmark smart community health centers and develop successful improvement strategies of service quality.
... It is particularly relevant in the medical field, both for diagnostic purposes and for more advanced applications such as guiding surgical operations [24]. Reconstruction speed requirement is also present in the industry, for such applications as quality control on assembly lines [25]. Another important issue of computed tomography is radiation exposure, since only a small class of studied objects has radiation resistance. ...
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Computed tomography is an important technique for non-destructive analysis of an object’s internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the time required to obtain the X-ray projections, and reducing the radiation dose imparted to the object. In this paper, we introduce a model of a monitored reconstruction process, in which the acquiring of projections is interspersed with image reconstruction. This model allows to examine the tomographic reconstruction process as an anytime algorithm and consider a problem of finding the optimal stopping point, corresponding to the required number of X-ray projections for the currently scanned object. We outline the theoretical framework for the monitored reconstruction, propose ways of constructing stopping rules for various reconstruction quality metrics and provide their experimental evaluation. Due to stopping at different times for different objects, the proposed approach allows to achieve a higher mean reconstruction quality for a given mean number of X-ray projections. Conversely, fewer projections on average are used to achieve the same mean reconstruction quality.
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Additive manufacturing technology is being used to process microspiral inductors. In this work, we applied stereolithography to print a microspiral inductor mold and fabricated microspiral inductors with 2.5, 4.5, and 6.5 turns by injecting eutectic gallium indium (EGaIn), which is a liquid metal. Based on a reconstruction model (obtained by reconstructing micro-computed tomography data), an algorithm was established for a nondestructive measurement of the structural parameters of the inductors. The electrical performance of the inductors was tested by conducting physical experiments and then characterized by simulating the reconstructed model. The measurement results of the structural parameters showed that the processing error of the wire diameter could be controlled within 20%; the machining error of the other structural parameters could be well controlled within 5%, with a high molding accuracy. Compared to the theoretical model results, the simulation results based on the reconstructed model showed improvement in the accuracy of the inductance values by 20.5, 16.8, and 7.6%, the accuracy of the Q factor by 13.2, 20.7, and 19.5%, and the accuracy of the self-resonant frequencies by 9.3, 6.31, and 8%, respectively.
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One of the most promising manufacturing processes for producing complex geometries in a shorter amount of time is additive manufacturing. However, the majority of additively manufactured industrial components fail to meet their intended specifications. The surface quality achieved by additive manufacturing parts is one of the major concerns of the industries. Furthermore, the additively manufactured components are prone to a wide range of interior and exterior flaws. Non-destructive evaluation has been identified as one of the most effective methods for resolving this issue. This review paper provides an overview of the most common occurring defects in the additive manufactured components, as well as the various non-destructive evaluation methods applicable to additive manufacturing components and their capability to detect and control the defects formed during manufacturing and service of the components. The suitability and challenges of applying non-destructive techniques to various additive manufacturing processes and parts are also discussed in this paper.
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In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter.
Article
Additive manufactured polymers and metallized microstructures are widely used in the production of electronic components. However, such three-dimensional printed metallized electrical components inevitably have processing errors that affect their performance. It is vital to understand defects' effects on the performance of the final product. In this study, we simulated micro-computed tomography (CT) data. A spiral cavity is printed by stereolithography and spiral inductors with different numbers of turns are fabricated by injecting Gallium Indium liquid metal (EGaIn). Through the theoretical simulation of the spiral inductor and the characterization of the electrical performance, we found that the relative error in the simulation of 2.5-turn, 4.5-turn, and 6.5-turn spiral inductors is +30.6%, +13%, and +6%, respectively, compared with the experimental data. The CT data are obtained by a CT scanning microcoil and a reconstruction model with real structural features is established based on the data. The results show that the relative error between the reconstructed model with real defects and the experimental data is +10.4%, -3.7%, and -1.5%, respectively, which is closer to the experimental data. According to the CT data simulation that provides a more accurate theoretical prediction, the actual effect of a defect on the final product can be assessed. The difference between the experimental results and the theoretical simulation can be inferred from the reconstruction model.
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The process of melting the metal and filling the gap in laser butt welding of aluminum was firstly in-situ observed with the X-ray phase contrast method. Because the molten metal dropped down through the gap firstly, keyhole and molten pool were initially observed in the middle of the samples, then grew up towards both the upper surface and the bottom. The typical twodimensional evolution routes of bubbles in the molten pool of laser butt welding were captured, which acted as an indicator for metal flow to fill the gap.
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Additive manufacturing (AM) is uniquely suitable for healthcare applications due to its design flexibility and cost effectiveness for creating complex geometries. Successful arthroplasty requires integration of the prosthetic implant with the bone to replace the damaged joint. Bone-mimetic biomaterials are utilised due to their mechanical properties and porous structure that allows bone ingrowth and implant fixation. The predictability of predetermined interconnected porous structures produced by AM ensures the required shape, size and properties that are suitable for tissue ingrowth and prevention of the implant loosening. The quality of the manufacturing process needs to be established before the utilisation of the parts in healthcare. This paper demonstrates a novel examination method of acetabular hip prosthesis cups based on X-ray computed tomography (CT) and image processing. The method was developed based on an innovative hip prosthesis acetabular cup prototype with a prescribed non-uniform lattice structure forming struts over the surface, with the interconnected porosity encouraging bone adhesion. This non-destructive, non-contact examination method can provide information of the interconnectivity of the porous structure, the standard deviation of the size of the pores and struts, the local thickness of the lattice structure in its size and spatial distribution. In particular, this leads to easier identification of weak regions that could inhibit a successful bond with the bone.
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The laser–matter interaction and solidification phenomena associated with laser additive manufacturing (LAM) remain unclear, slowing its process development and optimisation. Here, through in situ and operando high-speed synchrotron X-ray imaging, we reveal the underlying physical phenomena during the deposition of the first and second layer melt tracks. We show that the laser-induced gas/vapour jet promotes the formation of melt tracks and denuded zones via spattering (at a velocity of 1 m s−1). We also uncover mechanisms of pore migration by Marangoni-driven flow (recirculating at a velocity of 0.4 m s−1), pore dissolution and dispersion by laser re-melting. We develop a mechanism map for predicting the evolution of melt features, changes in melt track morphology from a continuous hemi-cylindrical track to disconnected beads with decreasing linear energy density and improved molten pool wetting with increasing laser power. Our results clarify aspects of the physics behind LAM, which are critical for its development. Additive manufacturing of metals is now widely available, but the interaction of the metal powder with the laser remains unclear. Here, the authors use X-rays to image melt features and pore behaviour during laser melting of powders.
Conference Paper
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In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
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Recent advances in X-ray computed tomography (XCT) have allowed for measurement resolutions approaching the point where XCT can be used for measuring surface topography. These advances make XCT appealing for measuring hard-to-reach or internal surfaces, such as those often present in additively manufactured parts. To demonstrate the feasibility and potential of XCT for topography measurement, topography datasets obtained using two XCT systems are compared to those acquired using coherence scanning interferometry and focus variation microscopy. A hollow Ti6Al4V part produced by laser powder bed fusion is used as a measurement artefact. The artefact comprises two component halves that can be separated to expose the internal surfaces. Measured surface datasets are accurately aligned and similarly cropped, and compared by various qualitative and quantitative means, including the computation of ISO 25178-2 areal surface texture parameters, commonly used in part quality assessment. Results show that XCT can non-destructively provide surface information comparable with more conventional surface measurement technologies, thus representing a viable alternative to more conventional measurement, particularly appealing for hard-to-reach and internal surfaces.
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Additive Manufacturing (AM) has the potential to remove boundaries that traditional manufacturing processes impose on engineering design work. The space industry pushes product development and technology to its edge, and there can be a lot to gain by introducing AM. However, the lack of established qualification procedures for AM parts has been highlighted, especially for critical components. While the space industry sees an advantage in AM due to expensive products in low volumes and long lead-times for traditional manufacturing processes (e.g. casting), it also acknowledges the issue of qualifying mission critical parts within its strict regulations. This paper focuses on the challenges with the qualification of AM in space applications. A qualitative study is presented where conclusions have been drawn from interviews within the aerospace industry. The results highlight important gaps that need to be understood before AM can be introduced in critical components, and gives insight into conventional component qualification.
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Automatic localization of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. Convolutional neural networks (CNN) have recently shown outstanding performance in both image classification and localization tasks. We examine how several different CNN architectures can be used to localize casting defects in X-ray images. We take advantage of transfer learning to allow state-of-the-art CNN localization models to be trained on a relatively small dataset. In an alternative approach, we train a defect classification model on a series of defect images and then use a sliding classifier method to develop a simple localization model. We compare the localization accuracy and computational performance of each technique. We show promising results for defect localization on the GRIMA database of X-ray images (GDXray) dataset and establish a benchmark for future studies on this dataset.
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In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ∼1.1 μm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ∼50 × 100 μm area. We also discuss the utility of these measurements for model validation and process improvement.
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