Many types of artifacts appear in X-ray computed tomography (CT) volume data, which influence measurement quality of industrial cone beam X-ray CT. Most of those artifacts are associated to CT scanning parameters; therefore, a good scanning parameter setting can weaken the influence to improve measurement accuracy. This paper presents a simulation method for evaluating CT scanning parameters for dimensional metrology. The method can aid CT metrology to achieve high measurement accuracy. In the method, image entropy is used as a criterion to evaluate the quality of CT volume data. For entropy calculation of CT volume data, a detailed description about bin width and entropy zone is given. The relationship between entropy values of CT volume data and error parameters of CT metrology is shown and discussed. By use of this method, mainly we focus on specimen orientation evaluation, and some other typical scanning parameters are used to evaluate the proposed method. Two typical specimens are used to evaluate the performance of the proposed method.

References

References
1.
Reimers
,
P.
, and
Goebbels
,
J.
,
1983
, “
New Possibilities of Non-Destructive Evaluation by X-Ray Computed Tomography
,”
Mater. Eval.
,
41
(6), pp.
732
737
.
2.
Kruth
,
J. P.
,
Bartscher
,
M.
,
Carmignato
,
S.
,
Schmitt
,
R.
,
De Chiffre
,
L.
, and
Weckenmann
,
A.
,
2011
, “
Computed Tomography for Dimensional Metrology
,”
CIRP Ann.-Manuf. Technol.
,
60
(
2
), pp.
821
842
.
3.
Hsieh
,
J.
,
2003
,
Computed Tomography: Principles, Design, Artifacts and Recent Advances
,
SPIE Press
, Bellingham, WA.
4.
Tuy
,
H. K.
,
1983
, “
An Inversion Formula for Cone–Beam Reconstruction
,”
SIAM J. Appl. Math.
,
43
(
3
), pp.
546
552
.
5.
Giedl-Wagner
,
R.
,
Miller
,
T.
, and
Sick
,
B.
,
2012
, “
Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks
,” Conference on Industrial Computed Tomography
(ICT)
, Sept, pp.
221
228
.
6.
Schmitt
,
R.
, and
Niggemann
,
C.
,
2011
, “
Method for Efficient Identification of Similar Work Pieces for X-Ray Computed Tomography
,”
International Symposium on Digital Industrial Radiology and Computed Tomography
, Berlin, Germany, Paper No. Mo.4.1.
7.
Reiter
,
M.
,
Krumm
,
M.
,
Kasperl
,
S.
,
Kuhn
,
C.
,
Erler
,
M.
,
Weiß
,
D.
,
Heinzl
,
C.
,
Gusenbauer
,
C.
, and
Kastner
,
J.
,
2012
, “
Evaluation of Transmission Based Image Quality Optimisation for X-Ray Computed Tomography
,” Conference on Industrial Computed Tomography
(ICT)
, Sept, pp.
241
250
.
8.
Reisinger
,
S.
,
Kasperl
,
S.
,
Franz
,
M.
,
Hiller
,
J.
, and
Schmid
,
U.
,
2011
, “
Simulation-Based Planning of Optimal Conditions for Industrial Computed Tomography
,”
International Symposium on Digital Industrial Radiology and Computed Tomography
, Berlin, Germany, Paper No. Mo.3.1.
9.
Müller
,
P.
,
Hiller
,
J.
,
Cantatore
,
A.
,
Bartscher
,
M.
, and
De Chiffre
,
L.
,
2012
, “
Investigation on the Influence of Image Quality in X-Ray CT Metrology
,”
Conference on Industrial Computed Tomography
, Sept, pp.
229
238
.
10.
Schmitt
,
R.
, and
Niggemann
,
C.
,
2010
, “
Uncertainty in Measurement for X-Ray-Computed Tomography Using Calibrated Work Pieces
,”
Meas. Sci. Technol.
,
21
(
5
), p.
054008
.
11.
Xue
,
L.
,
Suzuki
,
H.
,
Ohtake
,
Y.
,
Fujimoto
,
H.
,
Abe
,
M.
,
Sato
,
O.
, and
Takatsuji
,
T.
,
2016
, “
Quality Evaluation of X-Ray Computed Tomography Volume Data in Dimensional Metrology
,”
6th Conference on Industrial Computed Tomography
, Wells, Austria.
12.
Kyriakou
,
Y.
,
Lapp
,
R. M.
,
Hillebrand
,
L.
,
Ertel
,
D.
, and
Kalender
,
W. A.
,
2008
, “
Simultaneous Misalignment Correction for Approximate Circular Cone-Beam Computed Tomography
,”
Phys. Med. Biol.
,
53
(
22
), pp.
6267
6289
.
13.
Atkinson
,
D.
,
Hill
,
D. L.
,
Stoyle
,
P. N.
,
Summers
,
P. E.
, and
Keevil
,
S. F.
,
1997
, “
Automatic Correction of Motion Artifacts in Magnetic Resonance Images Using an Entropy Focus Criterion
,”
IEEE Trans. Med. Imaging
,
6
(
6
), pp.
903
910
.
14.
Zhang
,
X.
,
Li
,
L.
,
Zhang
,
F.
,
Xi
,
X. Q.
,
Deng
,
L.
, and
Yan
,
B.
,
2014
, “
Improving the Accuracy of CT Dimensional Metrology by a Novel Beam Hardening Correction Method
,”
Meas. Sci. Technol.
,
26
(1), p.
015007
.
15.
Sturges
,
H. A.
,
1926
, “
The Choice of a Class Interval
,”
J. Am. Stat. Assoc.
,
21
(
153
), pp.
65
66
.
16.
Scott
,
D. W.
,
1992
,
Multivariate Density Estimation: Theory, Practice and Visualization
,
Wiley
,
New York
.
17.
Freedman
,
D.
, and
Diaconis
,
P.
,
1981
, “
On the Histogram as a Density Estimator: L2 Theory
,”
Z. Wahrscheinlichkeitstheorie Verw. Geb.
,
57
(
4
), pp.
453
476
.
18.
Feldkamp
,
L. A.
,
Davis
,
L. C.
, and
Kress
,
J. W.
,
1984
, “
Practical Cone-Beam Algorithm
,”
J. Opt. Soc. Am. A
,
1
(
6
), pp.
612
619
.
19.
Xue
,
L.
,
Suzuki
,
H.
,
Ohtake
,
Y.
,
Fujimoto
,
H.
,
Abe
,
M.
,
Sato
,
O.
, and
Takatsuji
,
T.
,
2015
, “
Numerical Analysis of the Feldkamp–Davis–Kress Effect on Industrial X-Ray Computed Tomography for Dimensional Metrology
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(2), p.
021008
.
20.
Volume Graphics, 2015, “
VGStudio Max
,” Volume Graphics, Heidelberg, Germany, accessed June 2015, http://volumegraphics.com/en/products/vgstudio-max/software-bundles/
21.
GOM, 2013, “
GOM Inspect
,” GOM, Braunschweig, Germany, accessed June 2015, http://www.gom.com/3d-software/gom-inspect.html
22.
Fraunhofer, 2013, “
Scorpius XLab
,” Fraunhofer, Fürth, Germany, accessed June 2015, http://www.iis.fraunhofer.de/en/ff/zfp.html
23.
GrabCAD, 2011, “
Realistic Specimen
,” GrabCAD, Cambridge, UK, accessed June 2015, https://grabcad.com/library/aero-model-engine-block
24.
Carl Zeiss, 2013, “
METROTOM1500
,” Carl Zeiss, Oberkochen, Germany, accessed June 2015, http://www.zeiss.com/industrial-metrology/en_de/home.html
You do not currently have access to this content.