Abstract

Advanced three-dimensional (3D) scanning technology has been widely used in many industries to collect the massive point cloud data of artifacts for part dimension measurement and shape analysis. Though point cloud data has product surface quality information, it is challenging to conduct effective surface anomaly classification due to the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data within each sample. To deal with these challenges, this paper proposes a tensor voting-based approach for anomaly classification of artifact surfaces. A case study based on 3D scanned data obtained from a manufacturing plant shows the effectiveness of the proposed method.

References

1.
Popa
,
E. M.
, and
Kiss
,
I.
,
2011
, “
Assessment of Surface Defects in the Continuously Cast Steel
,”
Acta Tech. Corviniensis-Bull. Eng.
,
4
(
4
), p.
109
.
2.
Bao
,
L.
,
Wang
,
K.
, and
Jin
,
R.
,
2014
, “
A Hierarchical Model for Characterising Spatial Wafer Variations
,”
Int. J. Prod. Res.
,
52
(
6
), pp.
1827
1842
.
3.
Colosimo
,
B. M.
,
Cicorella
,
P.
,
Pacella
,
M.
, and
Blaco
,
M.
,
2014
, “
From Profile to Surface Monitoring: SPC for Cylindrical Surfaces via Gaussian Processes
,”
J. Qual. Technol.
,
46
(
2
), pp.
95
113
.
4.
Dastoorian
,
R.
,
Elhabashy
,
A. E.
,
Tian
,
W.
,
Wells
,
L. J.
, and
Camelio
,
J. A.
,
2018
Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing:
A
Case Study
,”
ASME 2018 13th International Manufacturing Science and Engineering Conference
,
College Station, TX
,
June 18–22
, p.
V003T02A036
.
5.
Yan
,
H.
,
Pacella
,
M.
, and
Paynabar
,
K.
,
2019
, “
Structured Point Cloud Data Modeling via Regularized Tensor Decomposition and Regression
,”
Technometrics
,
61
(
3
), pp.
385
395
.
6.
Gahrooei
,
M. R.
,
Yan
,
H.
,
Paynabar
,
K.
, and
Shi
,
J.
,
2020
, “
Multiple Tensor on Tensor Regression: An Approach for Modeling Processes With Heterogeneous Sources of Data
,”
Technometrics
,
63
(
2
), pp. 147–159.
7.
Wells
,
L. J.
,
Megahed
,
F. M.
,
Niziolek
,
C. B.
,
Camelio
,
J. A.
, and
Woodall
,
W. H.
,
2013
, “
Statistical Process Monitoring Approach for High-Density Point Clouds
,”
J. Intell. Manuf.
,
24
(
6
), pp.
1267
1279
.
8.
Colosimo
,
B. M.
,
Mammarella
,
F.
, and
Petro
,
S.
,
2010
, “Quality Control of Manufactured Surfaces,”
Frontiers in Statistical Quality Control 9
,
H. J.
Lenz
,
P. T.
Wilrich
, and
W.
Schmid
, eds.,
Springer
,
New York
, pp.
55
70
.
9.
Huang
,
D.
,
Du
,
S.
,
Li
,
G.
,
Zhao
,
C.
, and
Deng
,
Y.
,
2018
, “
Detection and Monitoring of Defects on Three-Dimensional Curved Surfaces Based on High-Density Point Cloud Data
,”
Precis. Eng.
,
53
, pp.
79
95
.
10.
Ren
,
J.
, and
Wang
,
H.
,
2019
, “
Surface Variation Modeling by Fusing Multiresolution Spatially Nonstationary Data Under a Transfer Learning Framework
,”
ASME J. Manuf. Sci. Eng.
,
141
(
1
), p.
011002
.
11.
Wells
,
L. J.
,
Shafae
,
M. S.
, and
Camelio
,
J. A.
,
2016
, “
Automated Surface Defect Detection Using High-Density Data
,”
ASME J. Manuf. Sci. Eng.
,
138
(
7
), p.
071001
.
12.
Zhao
,
X.
, and
Del Castillo
,
E.
,
2020
, “
An Intrinsic Geometrical Approach for Statistical Process Control of Surface and Manifold Data
,”
Technometrics
,
63
, pp.
1
18
.
13.
Samie Tootooni
,
M.
,
Dsouza
,
A.
,
Donovan
,
R.
,
Rao
,
P. K.
,
Kong
,
Z. J.
, and
Borgesen
,
P.
,
2017
, “
Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches
,”
ASME J. Manuf. Sci. Eng.
,
139
(
9
), p.
091005
.
14.
Yacob
,
F.
,
Semere
,
D.
, and
Nordgren
,
E.
,
2019
, “
Anomaly Detection in Skin Model Shapes Using Machine Learning Classifiers
,”
Int. J. Adv. Manuf. Technol.
,
105
, pp.
1
13
.
15.
Himmelsbach
,
M.
,
Luettel
,
T.
, and
Wuensche
,
H.-J.
,
2009
, “
Real-Time Object Classification in 3D Point Clouds Using Point Feature Histograms
,”
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
,
IEEE
, pp.
994
1000
.
16.
Wohlkinger
,
W.
, and
Vincze
,
M.
,
2011
, “
Ensemble of Shape Functions for 3D Object Classification
,”
2011 IEEE International Conference on Robotics and Biomimetics
,
Karon Beach, Thailand
,
Dec. 7–11
, pp.
2987
2992
.
17.
Qi
,
C. R.
,
Su
,
H.
,
Mo
,
K.
, and
Guibas
,
L. J.
,
2017
, “
Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Honolulu, HI
,
July 21–26
, pp.
652
-
660
.
18.
Rodríguez-Cuenca
,
B.
,
García-Cortés
,
S.
,
Ordóñez
,
C.
, and
Alonso
,
M.
,
2015
, “
Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm
,”
Remote Sens.
,
7
(
10
), pp.
12680
12703
.
19.
Qi
,
C. R.
,
Yi
,
L.
,
Su
,
H.
, and
Guibas
,
L. J.
,
2017
, “
PointNet++: Deep Hierarchical Feature Learning on Point Sets
in
a Metric Space
,”
The Neural Information Processing Systems
,
Long Beach, CA
,
June 7
, pp.
5105
5114
.
20.
Zhao
,
H.
,
Jiang
,
L.
,
Fu
,
C.-W.
, and
Jia
,
J.
,
2019
, “
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
,”
The Computer Vision and Pattern Recognition
,
Long Beach, CA
,
June 15
, pp.
5560
5568
.
21.
Guy
,
G.
, and
Medioni
,
G.
,
1997
, “
Inference of Surfaces, 3D Curves, and Junctions From Sparse, Noisy, 3D Data
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
19
(
11
), pp.
1265
1277
.
22.
Medioni
,
G.
,
Tang
,
C.-K.
, and
Lee
,
M.-S.
,
2000
, “
Tensor Voting: Theory
and
Applications
,”
Proceedings of RFIA
,
Paris, France
,
vol. 3
.
23.
Wu
,
T. P.
,
Yeung
,
S.-K.
,
Jia
,
J.
,
Tang
,
C.-K.
, and
Medioni
,
G.
,
2012
, “
A Closed-Form Solution to Tensor Voting: Theory and Applications
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
34
(
8
), pp.
1482
1495
.
24.
Granlund
,
G. H.
, and
Knutsson
,
H.
,
1995
,
Signal Processing for Computer Vision
,
Springer
,
Berlin
.
25.
D'Errico
,
J.
, “
Shape Language Modeling
.” https://www.mathworks.com/matlabcentral/fileexchange/24443-slm-shape-language-modeling, Accessed April 27, 2018.
26.
Camisani-Calzolari
,
F.
,
Craig
,
I.
, and
Pistorius
,
P.
,
2003
, “
A Review on Causes of Surface Defects in Continuous Casting
,”
IFAC Proc. Vol.
,
36
(
24
), pp.
113
121
.
27.
Söderkvist
,
I.
,
2009
, “
Using SVD for Some Fitting Problems
,” University Lecture, https://www.ltu.se/cms_fs/1.51590!/svd-fitting.pdf, Accessed April 27, 2018.
28.
Boser
,
B. E.
,
1992
, “
A Training Algorithm for Optimal Margin Classifiers
,”
Proceedings of the Fifth Annual Workshop on Computational Learning Theory
,
Pittsburgh, PA
,
July 27–29
,
ACM
, Vol.
5
, pp.
144
152
.
29.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support-Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.
30.
Lee
,
Y.
,
Lin
,
Y.
, and
Wahba
,
G.
,
2004
, “
Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data
,”
J. Am. Stat. Assoc.
,
99
(
465
), pp.
67
81
.
31.
Huang
,
L.
,
Hao
,
H. Z.
,
Zeng
,
Z. B.
, and
Bushel
,
P. R.
,
2013
, “
Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification
,”
Cancer Inf.
,
12
(
12
), pp.
143
153
.
32.
Crammer
,
K.
, and
Singer
,
Y.
,
2002
, “
On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines
,”
J. Mach. Learn. Res.
,
2
(
2
), pp.
265
292
.
33.
Wu
,
T. P.
,
Yeung
,
S. K.
,
Jia
,
J.
, and
Tang
,
C. K.
,
2010
, “
Quasi-Dense 3D Reconstruction Using Tensor-Based Multiview Stereo
,”
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
,
San Francisco, CA
,
June 13–18
, pp.
1482
1489
.
You do not currently have access to this content.