Quantification of the capability of micro-CT to detect defects in castings
using a new test piece and a voxel-based comparison method
A. Staude
a,
, M. Bartscher
b
, K. Ehrig
a
, J. Goebbels
a
, M. Koch
b
, U. Neuschaefer-Rube
b
, J. No ¨tel
a
a
BAM Bundesanstalt fu¨r Materialforschung und -pru¨fung, Fachgruppe 8.5, Unter den Eichen 87, 12205 Berlin, Germany
b
Physikalisch-Technische Bundesanstalt, PTB, Bundesallee 100, 38116 Braunschweig, Germany
a r t i c l e i n f o
Article history:
Received 16 December 2010
Received in revised form
12 May 2011
Accepted 20 May 2011
Available online 30 May 2011
Keywords:
X-ray tomography
Casting
Defects
Porosity
a b s t r a c t
We present a modified aluminium casting which is especially suited as test piece for measuring casting
defects and the geometry by means of cone-beam micro-focus X-ray systems, and which may become a
reference standard for dimensional measurements and defect detection.
To obtain a test piece with inner geometries measured by tactile means, we divided a small aluminium
cylinder head into four pieces in such a way that most inner surfaces can be reached with a tactile probe.
Reference geometries (spheres and cylinders) were applied to define a coordinate system for aligning the
measurements in the disassembled and re-assembled state. The four pieces were re-assembled after the
tactile measurement.
The test piece also contains casting defects. In order to be able to use the assembled cylinder head as
reference sample for defect detection, measurements with higher spatial resolution and better signal-to-noise
ratio were performed on the single parts. For improving the reliability of the reference measurements, CT
measurements of each part were carried out in three different orientations, and the individual defect
detections were combined to obtain a reference data set with a high probability of defect detection and a low
rate of erroneous detections.
A new method for comparing the defect detection in a CT measurement to a reference data set is
demonstrated, which provides individual information on every detected flaw.
We discuss the results of measurements in the assembled state with respect to the reference data for flaw
detection.
& 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Computed tomography (CT) is an established method for quality
control in industrial production, e.g. in the automotive industry. It
allows the surface determination of the whole workpiece, even
of hidden inner surfaces. In contrast to 2D metallographic methods
it gives non-destructively 3D information on pores in cast pieces
(e.g. [2]) and allows their classification as gas or shrinkage pores
(e.g. [3,4]).
For a detailed analysis it is necessary to know the performance
and limitations of the measurement method used. To roughly
estimate the properties of pore detection with CT, a comparison
with measurements of pore sizes and distribution in
2D slices is
possible (e.g. [9]).
A method for getting quantitative information really in 3D on
CT’s ability to measure the geometry and to detect defects is to
measure calibrated test pieces (reference standards).
An artificial test piece for studying the reliability of pore
detection with respect to grey-value contrast and voxel size made
out of spheres is presented by Rosc et al. [5]. Another possibility
to create a reference part for defect detection is to form a 3D
structure out of parts where the defects can be calibrated before
assembly, e.g. with a tactile probe [8].
Our aim was to create a test piece with properties similar to
actual cast pieces, e.g. inner free-form surfaces and similar material
(aluminium). As an industry-near test piece we chose an aluminium
single-cylinder head with the dimensions 12 cm 9 cm 6 cm, cast
by the company ACTech in Freiberg/Saxony, Germany, the ‘‘Mini-
Cylinder Head No. 5’’ (Fig. 1).
This cast piece contains inner surfaces and casting defects.
To be able to calibrate the inner surfaces by tactile probing, the
cylinder head was cut into four segments by electrical discharge
machining in such a way that large parts of the surfaces are
accessible by a tactile probe (Figs. 2 and 3). Reference geometries
(three spheres and three cylinders) were applied to each segment.
The positions and dimensions of the reference structures were
determined with a coordinate measuring machine (CMM). They
define a workpiece coordinate system enabling the alignment of
Contents lists available at ScienceDirect
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doi:10.1016/j.ndteint.2011.05.006
Corresponding author. Tel.: t49 30 8104 4140; fax: t49 30 8104 1837.
E-mail address: andreas.staude@bam.de (A. Staude).
NDT&E International 44 (2011) 531–536single measurements. The distances between the spheres of each
segment (Fig. 4) can be used to correct potential scale discrepan-
cies between individual measurements.
The workpiece thus prepared can now be used as test piece for
dimensional measurements and for defect analysis with CT.
In the following an application of the new test piece for defect
detection and an improved data processing work-flow for perform-
ing the defect detection is presented.
2. Mini-Cylinder Head No. 5 as test specimen for defect
detection
A test piece for defect detection must have a known defect
distribution. For this purpose, we performed measurements on
the BAM-225 kV-CT-device (with 210 kV acceleration voltage, and
0.75 mm of silver and 0.5 mm of copper as X-ray-tube pre-filters,
in this order, between tube and object) of the four single
segments of the cylinder head in three different orientations each
(multi-orientation measurement, the ‘‘reference measurement’’)
with a higher resolution (e48:0 mmT
3
voxel size) and a more
‘‘industr
y-like’’ measurement of the assembled cylinder head
(e69:8 mmT
3
voxel size) with otherwise identical settings. The
multi-orientation measurement allows the reduction of orienta-
tion-dependent artefacts in the reconstructed volumes (edge
artefacts, ring artefacts, beam hardening) by averaging data sets
with different alignments of these.
Schmitt et al. [6] described a method for artefact reduction
using two measurements of the object in different orientations
(‘‘dual viewing’’). By comparing the gradients in the reconstructed
voxel data of both measurements, they identified artefact-
affected regions and assigned a reduced weight to the grey values
of the concerned voxels in the data fusion process. We average
the data equally weighted, but by using more (three) orientations,
the artefacts are nevertheless significantly reduced. By averaging
the data, the signal-to-noise ratio is also enhanced. Staude et al.
[7] have already described a method to combine defect detections
in the individual data sets within the voxel data visualisation and
analysis software VGStudio Max 2.0 (Volume Graphics GmbH,
Heidelberg, Germany). There, the defects in the individual mea-
surements of the cylinder head segments were searched using an
adaptive threshold algorithm and converted into regions of
interest (ROIs). After aligning the data sets via the positions and
distances of the reference spheres, the intersection of these ROIs
was calculated and then became the defect reference.
Fig. 1. Mini-Cylinder Head No. 5 before processing.
Fig. 2. Mini-Cylinder Head No. 5, divided into four pieces, 1Al . . . 4Al.
Fig. 3. Two segments of Mini-Cylinder Head No. 5 with the visible spheres.
Fig. 4. Positions and distances of the spheres.
532A. Staude et al. / NDT&E International 44 (2011) 531–536For the data set of the assembled cylinder head the defect
detection was performed also via adaptive threshold and conver-
sion into ROIs.
The comparison of the ROIs provided information on the size
distribution of the detected defects, but it was not possible to
make statements on whether individual defects were detected in
the merged measurement, which detections were erroneous
detections, or how the detected size of the defects differed.
Due to the definition of the ROIs as consisting of complete
voxels in each data set, truncation errors occurred during the
calculation of the intersections of the detected defects.
In this paper we present a different approach which allows
analysing the detection of individual defects without losing
spatial resolution due to resampling or truncation. This is shown
exemplarily in a section of segment 1. The three different
orientations of segment 1 for the reference measurements are
shown in Fig. 5.
The three reference measurements and the assembled-state
measurement were reconstructed in their original orientation, but
with the smaller voxel size (higher resolution) of the reference
me
asurement. With VGStudio Max, the orientations of the refer-
ence measurements relative to that of the assembled state were
determined using the reference spheres. The rotation angles along
the different axes and the respective offsets were used as input
parameters for the reconstruction of the reference measurements,
so that all data sets to be compared were finally reconstructed in
the same voxel grid. To make use of the multi-orientation
measurement, the three reference measurements were averaged,
resulting in a higher signal-to-noise ratio (50 instead of 33,
defined as ratio of the difference between the grey levels of
material and background to the local standard deviation of grey
values) and in a reduction of artefacts (Fig. 6).
All grey values in both volumes (the averaged one and the
assembled-state one) were increased by one, in order to have no
values equal to zero in the data. This is necessary for an
unambiguous distinction between voxels belonging to a defect
and ROIs in the later processing with the open-source image
manipulation program ImageJ.
1
In the same sections of the data
sets, the defect detection with VGStudio Max was now performed
(adaptive threshold, manually defined grey values, minimum
defect size 3
3
voxels). The distribution of the detected defects in
the averaged reference data and in the assembled-state data is
shown in Fig. 7. In the considered section of the averaged
reference data 1787 defects were detected whereas in that of
the assembled-state measurement there are 246.
To be able to analyse the detection of individual defects, those
founded were converted into ROIs (Fig. 8, top). The voxel data of
these ROIs were extracted, leading to voxel data sets with zeros
everywhere except in the ROIs, where the original values (larger
than zero) are preserved (Fig. 8, bottom left).
The data with the extracted ROIs were now loaded into ImageJ.
Using the ImageJ-plugin ‘‘Object counter 3D’’,
2
all connected
regions with grey values larger than zero (where the ROIs had
been) were determined. The results are data arrays where every
voxel of each connected region is filled with its indexing number
(‘‘index data’’, Fig. 8, bottom right), and tables containing infor-
mation on each defect’s size and position.
To merge the information on the defects in both data sets (Fig. 9,
top), it is necessary to assign unique number ranges to the index
data sets of both analyses. This is done by multiplying one index
data set (in this case the one from the reference measurement) by a
number large enough (e.g. 1000, which is larger than the number of
detected defects in the assembled-state measurement—246), so that
no overlap with the indices in the assembled-state measurement
occurs. Then both index data sets were added (Fig. 9, bottom).
The resulting array now contains values smaller than 1000 where
there is only a detection in the assembled-state measurement,
multip
les of 1000 where there was a detection solely in the reference
Fig. 5. Orientations of segment 1 in the three individual CT measurements. The axis of rotation during the measurement is in z-direction and in the image centre.
Fig. 6. Slices through the aligned reference data sets (top and bottom left) and the
averaged data set (bottom right). The signal-to-noise ratio in the averaged data set
is significantly enhanced and artefacts are reduced.
1
Version Fiji 2009-04-29, http://pacific.mpi-cbg.de/.
2
Version 1.5.1, /ij/plugins/track/objects.html.
A. Staude et al. / NDT&E International 44 (2011) 531–536 533data, and values larger than 1000 with the smaller digits deviating
from 0 for detections in both data sets. The calculation of a histogram
of the 3D array with a bin size of 1 now provides information on the
detection of each known defect, e.g. whether it was detected at all or
detected as one or more separate defects. By combining this
information with the information stored in the tables created by
‘‘Object counter 3D’’, the positions of the defects, the size of the
overlap, and the summed size of the overlapping defects can be
retrieved.
Now it is possible to look at certain specific defects, e.g. larger
defects close to the surface, to see how they are detected with the
respective CT setup. Following the VDG-Merkblatt P 201,
3
pores
can be classified with respect to certain parameters like the kind
of load on the part, the porosity in certain regions, and the size.
With the individual information on every pore, the detectability
for different classes of pores can be determined. It is also possible
to perform statistical investigations on the defect detection of all
defects.
Results of this statistical analysis are shown in Figs. 10 and 11.
In Fig. 10 it can be seen that only few small defects are re-
detected, while all defects larger than 600 voxels are found.
Fig. 11 shows the ratio of the measured size of each defect in
the reference measurement to the summed size of all defects in
the assembled-state measurement, which partially or completely
overlap the defect in the reference measurement. Ratios much
smaller than 1 denote a much larger defect (or multiple over-
lapping defects) in the assembled-state measurement at the
position where there is only (a small) one in the reference
measurement. These are obviously erroneous detections and
occur predominantly below a size of 100 voxels. Size ratios larger
Fig. 7. The detected defects (marked black) in the averaged reference data (left)
and in the assembled cylinder head measurement (right); top: the distribution in
3D view; bottom: a horizontal slice (the same in both, marked in the 3D view).
Fig. 8. Scheme for indexing the defects; top left: Defects are darker regions in the
(nearly) homogeneous material. Top right: The detected defects are converted into
ROIs. Bottom left: The extracted voxel data of the ROIs
still contain the original
grey values. Bottom right: The index number of each defect is assigned to all
voxels belonging to it.
Fig. 9. Top: Index data of schematic defect detections in the same region in two
different data sets. Bottom: The merged information of the two index data sets.
3
VDG-Merkblatt P 201, ‘‘Volumendefizite von Gu st u¨cken aus Nichteisenme-
tallen’’, 2002, VDG-Informationszentrum Giesserei, Du ¨sseldorf, Germany.
534A. Staude et al. / NDT&E International 44 (2011) 531–536than 1 occur when not all parts of a defect are re-detected in the
assembled-state measurement, e.g. because of the reduced signal-
to-noise ratio or the reduced spatial resolution. Large defects have
nearly the same size in both measurements (a size ratio near 1)
and are thus detected reliably.
It is also of interest to get information on the properties and on
the distribution of size and position of erroneous detections.
Histogram analysis shows that erroneous detections have sizes
below 300 voxels (Fig. 12).
3. Summary and conclusions
We presented a new test specimen for both dimensional mea-
surement and defect detection with CT. An industry-near workpiece
was divided into suitable segments, and reference geometries were
applied which allow the alignment of individual measurements to
those in the assembled state. With this procedure it is possible to
create test pieces for which reference data for defect detection and
dimensional measurements can be obtained. The reference data
measured at the single segments will thus have the necessary higher
spatial resolution and higher signal-to-noise ratio and will be less
affected by artefacts.
By using a multi-orientation measurement we improved the
signal-to-noise ratio and reduced disturbances by CT artefacts in
the single measurements. We simply averaged the grey values in the
voxel data. With a weighted mean one could achieve even better
results, but the optimum way to calculate the weights is still to be
found. By reconstructing the individual data sets into the same voxel
grid, a loss of resolution due to later resampling as well as the
truncation errors occurring when ROIs of differently oriented voxel
data sets are intersected in VGStudio Max is avoided.
The new method to compare defect detections from different
measurements described here allows detailed individual analysis
of every single defect.
The use of Mini-Cylinder Head No. 5 as test piece for dimen-
sional measurements is not discussed here. A detailed analysis,
including the comparison with tactile measurements of the
surfaces, is described in Bartscher et al. [1].
Acknowledgments
The work of AS was funded by the Deutsche Forschungsge-
meinschaft within the Priority Programme 1159 ‘‘StraMNano’’
under grant number GO530/2-2. The work of KE and MK was
funded by the MNPQ project no. AZ: II D5 – 07/06 of the German
Federal Bundesministerium fu¨r Wirtschaft und Technologie.
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