The surface appearance is sensitive to change in the manufacturing process and is one of the most important product quality characteristics. The classification of workpiece surface patterns is critical for quality control, because it can provide feedback on the manufacturing process. In this study, a novel classification approach for engineering surfaces is proposed by combining dual-tree complex wavelet transform (DT-CWT) and selective ensemble classifiers called modified matching pursuit optimization with multiclass support vector machines ensemble (MPO-SVME), which adopts support vector machine (SVM) as basic classifiers. The dual-tree wavelet transform is used to decompose three-dimensional (3D) workpiece surfaces, and the features of workpiece surface are extracted from wavelet sub-bands of each level. Then MPO-SVME is developed to classify different workpiece surfaces based on the extracted features and the performance of the proposed approach is evaluated by computing its classification accuracy. The performance of MPO-SVME is validated in case study, and the results demonstrate that MPO-SVME can increase the classification accuracy with only a handful of selected classifiers.

References

References
1.
Whitehouse
,
D.
,
1997
, “
Surface Metrology
,”
Meas. Sci. Technol.
,
8
(
9
), pp.
955
972
.10.1088/0957-0233/8/9/002
2.
Whitehouse
,
D.
,
1982
, “
The Parameter Rash—Is There a Cure?
Wear
,
83
(
1
), pp.
75
78
.10.1016/0043-1648(82)90341-6
3.
International Organization for Standardization ISO 4287
,
1997
,
Geometrical Product Specifications (GPS)—Surface Texture: Profile Method—Terms, Definitions and Surface Texture Parameters
,
ISO
,
Geneva
.
4.
ASME B46.1
,
2002
, Surface Texture (Surface Roughness, Waviness, and Lay) ASME, New York.
5.
Leopold
,
J.
,
Günther
,
H.
, and
Leopold
,
R.
,
2003
, “
New Developments in Fast 3D-Surface Quality Control
,”
Measurement
,
33
(
2
), pp.
179
187
.10.1016/S0263-2241(02)00056-8
6.
Nguyen
,
H. T.
,
Wang
,
H.
, and
Hu
,
S. J.
,
2013
, “
Characterization of Cutting Force Induced Surface Shape Variation Using High-Definition Metrology
,”
ASME, J. Manuf. Sci. Eng.
,
135
(
4
), p.
041014
.10.1115/1.4024290
7.
Blunt
,
L.
, and
Jiang
,
X.
,
2003
,
Advanced Techniques for Assessment Surface Topography: Development of a Basis for 3D Surface Texture Standards
,
Kogan Page Science
,
London
.
8.
Graziano
,
A. A.
,
Ganguly
,
V.
,
Schmitz
,
T.
, and
Yamaguchi
,
H.
,
2014
, “
Control of Lay on Cobalt Chromium Alloy Finished Surfaces Using Magnetic Abrasive Finishing and Its Effect on Wettability
,”
ASME, J. Manuf. Sci. Eng.
,
136
(
3
), p.
031016
.10.1115/1.4026935
9.
Wit
,
G.
, and
Krzysztof
,
Ż.
,
2014
, “
Characterization of Surface Integrity Produced by Sequential Dry Hard Turning and Ball Burnishing Operations
,”
ASME, J. Manuf. Sci. Eng.
,
136
(
3
), p.
031017
.10.1115/1.4026936
10.
VenkatRamana
,
K.
, and
Ramamoorthy
,
B.
,
1996
, “
Statistical Methods to Compare the Texture Features of Machined Surfaces
,”
Pattern Recognit.
,
29
(
9
), pp.
1447
1459
.10.1016/0031-3203(96)00008-8
11.
Dong
,
W.
,
Sullivan
,
P.
, and
Stout
,
K.
,
1994
, “
Comprehensive Study of Parameters for Characterizing Three-Dimensional Surface Topography: IV: Parameters for Characterizing Spatial and Hybrid Properties
,”
Wear
,
178
(
1
), pp.
45
60
.10.1016/0043-1648(94)90128-7
12.
Satoh
,
G.
,
Huang
,
X.
,
Ramirez
,
A. G.
, and
Yao
,
Y. L.
,
2012
, “
Characterization and Prediction of Texture in Laser Annealed NiTi Shape Memory Thin Films
,”
ASME, J. Manuf. Sci. Eng.
,
134
(
5
), p.
051006
.10.1115/1.4007459
13.
Tsa
,
D.-M.
, and
Wu
,
S.-K.
,
2000
, “
Automated Surface Inspection Using Gabor Filters
,”
Int. J. Adv. Manuf. Technol.
,
16
(
7
), pp.
474
482
.10.1007/s001700070055
14.
Zhang
,
M.
,
Levina
,
E.
,
Djurdjanovic
,
D.
, and
Ni
,
J.
,
2008
, “
Estimating Distribution of Surface Parameters for Classification Purposes
,”
ASME, J. Manuf. Sci. Eng.
,
130
(
4
), p.
031010
.10.1115/1.2844588
15.
Fu
,
S.
,
Muralikrishnan
,
B.
, and
Raja
,
J.
,
2003
, “
Engineering Surface Analysis with Different Wavelet Bases
,”
ASME, J. Manuf. Sci. Eng.
,
125
(
4
), pp.
844
852
.10.1115/1.1616947
16.
Liao
,
Y.
,
Stephenson
,
D. A.
, and
Ni
,
J.
,
2012
, “
Multiple-Scale Wavelet Decomposition, 3D Surface Feature Exaction and Applications
,”
ASME, J. Manuf. Sci. Eng.
,
134
(
1
), p.
11005
.10.1115/1.4005352
17.
Li
,
Y.
, and
Ni
,
J.
,
2011
, “
B-Spline Wavelet-Based Multiresolution Analysis of Surface Texture in End-Milling of Aluminum
,”
ASME, J. Manuf. Sci. Eng.
,
133
(
1
), p.
011014
.10.1115/1.4002452
18.
Yu
,
J.
,
2012
, “
Machine Tool Condition Monitoring Based on an Adaptive Gaussian Mixture Model
,”
ASME, J. Manuf. Sci. Eng.
,
134
(
3
), p.
031004
.10.1115/1.4006093
19.
Raja
,
J.
,
Muralikrishnan
,
B.
, and
Fu
,
S.
,
2002
, “
Recent Advances in Separation of Roughness, Waviness and Form
,”
Precis. Eng.
,
26
(
2
), pp.
222
235
.10.1016/S0141-6359(02)00103-4
20.
Badashah
,
S. J.
, and
Subbaiah
,
P.
,
2011
, “
Image Enhancement and Surface Roughness With Feature Extraction Using DWT
,”
International Conference on Sustainable Energy and Intelligent Systems
,
IET, Chennai, India
, July 20–22, pp.
754
759
.
21.
Kingsbury
,
N.
,
1999
, “
Image Processing With Complex Wavelets
,”
Philos. Trans. R. Soc. London
,
357
(
1760
), pp.
2543
2560
.10.1098/rsta.1999.0447
22.
Kingsbury
,
N.
,
1998
, “
The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters
,” Proceedings of the 8th IEEE Digital Signal Processing Workshop, IEEE, Bryce Canyon, UT, Aug. 9–12, pp. 120–131.
23.
Kingsbury
,
N.
,
2001
, “
Complex Wavelets for Shift Invariant Analysis and Filtering of Signals
,”
Appl. Comput. Harmonic Anal.
,
10
(
3
), pp.
234
253
.10.1006/acha.2000.0343
24.
Selesnick
,
I.
,
Baraniuk
,
R.
, and
Kingsbury
,
N.
,
2005
, “
The Dual-Tree Complex Wavelet Transform
,”
IEEE Signal Process. Mag.
,
22
(
6
), pp.
123
151
.10.1109/MSP.2005.1550194
25.
Vapnik
,
V.
,
1999
,
The Nature of Statistical Learning Theory
,
Springer
, New York.
26.
Gunn
,
S. R.
,
1998
, “
Support Vector Machines for Classification and Regression
,” ISIS Technical Report No. 14.
27.
Cristianini
,
N.
, and
Shawe-Taylor
,
J.
,
2000
,
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
,
Cambridge University
, Cambridge, UK.
28.
Paredis
,
C. J.
,
2010
, “
Using Support Vector Machines to Formalize the Valid Input Domain of Predictive Models in Systems Design Problems
,”
ASME J. Mech. Des.
,
132
(
10
), p.
101001
.10.1115/1.4002151
29.
Du
,
S.
,
Lv
,
J.
, and
Xi
,
L.
,
2012
, “
On-Line Classifying Process Mean Shifts in Multivariate Control Charts Based on Multiclass Support Vector Machines
,”
Int. J. Prod. Res.
,
50
(
22
), pp.
6288
6310
.10.1080/00207543.2011.631596
30.
Pal
,
S.
, and
Pal
,
A.
,
2001
,
Pattern Recognition: From Classical to Modern Approaches
,
World Scientific Publishing Company
,
Singapore
, pp.
427
451
.
31.
Ho
,
T.
,
2000
, “
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
,”
Multi. Classifier Syst.
,
1857
, pp.
97
106
.10.1007/3-540-45014-9
32.
Kim
,
H. C.
,
Pang
,
S.
,
Je
,
H.-M.
,
Kim
,
D.
, and
Yang Bang
,
S.
,
2003
, “
Constructing Support Vector Machine Ensemble
,”
Pattern Recognit.
,
36
(
12
), pp.
2757
2767
.10.1016/S0031-3203(03)00175-4
33.
Wang
,
S. J.
,
Mathew
,
A.
,
Chen
,
Y.
,
Xi
,
L. F.
,
Ma
,
L.
, and
Lee
,
J.
,
2009
, “
Empirical Analysis of Support Vector Machine Ensemble Classifiers
,”
Expert Syst. Appl.
,
36
(
3
), pp.
6466
6476
.10.1016/j.eswa.2008.07.041
34.
Pang
,
S.
,
Kim
,
D.
, and
Bang
,
S. Y.
,
2005
, “
Face Membership Authentication Using SVM Classification Tree Generated by Membership-Based LLE Data Partition
,”
IEEE Trans. Neural Networks
,
16
(
2
), pp.
436
446
.10.1109/TNN.2004.841776
35.
Lei
,
Z.
,
Yang
,
Y.
, and
Wu
,
Z.
,
2006
, “
Ensemble of Support Vector Machine for Text-Independent Speaker Recognition
,”
Int. J. Comput. Sci. Networks Secur.
,
6
(
5
), pp.
163
167
.
36.
Hung
,
C.
, and
Chen
,
J. H.
,
2009
, “
A Selective Ensemble Based on Expected Probabilities for Bankruptcy Prediction
,”
Expert Syst. Appl.
,
36
(
3
), pp.
5297
5303
.10.1016/j.eswa.2008.06.068
37.
Jaya Priya
,
K.
, and
Rajesh
,
R.
,
2010
, “
Local Fusion of Complex Dual-Tree Wavelet Coefficients Based Face Recognition for Single Sample Problem
,”
Procedia Comput. Sci.
,
2
, pp.
94
100
.10.1016/j.procs.2010.11.012
38.
Hsu
,
C. W.
, and
Lin
,
C. J.
,
2002
, “
A Comparison of Methods for Multiclass Support Vector Machines
,”
IEEE Trans. Neural Networks
,
13
(
2
), pp.
415
425
.10.1109/72.991427
39.
Breiman
,
L.
,
1996
, “
Bagging Predictors
,”
Mach. Learn.
,
24
(
2
), pp.
123
140
.10.1007/BF00058655
40.
Schapire
,
R. E.
,
1990
, “
The Strength of Weak Learnability
,”
Mach. Learn.
,
5
(
2
), pp.
197
227
.10.1109/SFCS.1989.63451
41.
Ho
,
T. K.
,
1998
, “
The Random Subspace Method for Constructing Decision Forests
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
20
(
8
), pp.
832
844
.10.1109/34.709601
42.
Zhou
,
Z.-H.
,
Wu
,
J.
, and
Tang
,
W.
,
2002
, “
Ensembling Neural Networks: Many Could be Better Than All
,”
Artif. Intell.
,
137
(
1
), pp.
239
263
.10.1016/S0004-3702(02)00190-X
43.
Partalas
,
I.
,
Tsoumakas
,
G.
,
Katakis
,
I.
, and
Vlahavas
,
I.
,
2006
, “
Ensemble Pruning Using Reinforcement Learning
,”
Adv. Artif. Intell.
,
3955
(
1
), pp.
301
310
.10.1007/11752912
44.
Giacinto
,
G.
,
Roli
,
F.
, and
Fumera
,
G.
,
2000
, “
Design of Effective Multiple Classifier Systems by Clustering of Classifiers
,”
Proceedings of the 15th International Conference on Pattern Recognition
,
Barcelona
, September 3–7, Vol.
2
, pp.
160
163
.
45.
Martınez-Munoz
,
G.
, and
Suárez
,
A.
,
2006
, “
Pruning in Ordered Bagging Ensembles
,”
Proceedings of the 23rd International Conference on Machine Learning
,
ACM, New York
, June 25–29, pp.
609
616
.
46.
Mao
,
S.
,
Jiao
,
L.
,
Xiong
,
L.
, and
Gou
,
S.
,
2011
, “
Greedy Optimization Classifiers Ensemble Based on Diversity
,”
Pattern Recognit.
,
44
(
6
), pp.
1245
1261
.10.1016/j.patcog.2010.11.007
48.
Chang
,
C. C.
, and
Lin
,
C. J.
,
2011
, “
LIBSVM: A Library for Support Vector Machines
,”
ACM Trans. Intell. Syst. Technol.
,
2
(
3
), p.
27
.10.1145/1961189.1961199
49.
Freund
,
Y.
, and
Schapire
,
R.
,
1995
, “
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
,”
Proceedings of the Second European Conference on Computer and System Sciences
,
Springer
, Berlin-Heidelberg, Germany, March 13–15, pp.
23
37
.
You do not currently have access to this content.