Non-destructive ultrasonic testing is beneficial for monitoring the structural health of polymer composites. However, owing to scattering and other factors, ultrasonic data often appear as noisy signals or images containing artifacts. The analysis of ultrasound signals highly depends on the expertise of trained human inspectors. Hence, the development of ultrasonic data analysis methods, particularly unsupervised methods, is necessitated. In this study, a novel unsupervised method is developed for the ultrasonic inspection of defects in polymer composites, named manifold learning and segmentation. In a uniform manifold approximation and projection model, nonlinear dimensionality reduction is first performed on high-dimensional ultrasound data for extracting and visualizing defect features. Subsequently, semantic segmentation is performed to predict/discriminate between defects and backgrounds. Consequently, subsurface defects in the composites can be effectively detected. Experimental results and comparisons on two carbon fiber reinforced polymer specimens demonstrate the effectiveness of the proposed method.
REFERENCES
1.
B.
Wang
, S. C.
Zhong
, T. L.
Lee
, K. S.
Fancey
, and J.
Mi
, “Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review
,” Adv. Mech. Eng.
12
, 1
(2020
). 2.
T.
D’orazio
, M.
Leo
, A.
Distante
, C.
Guaragnella
, V.
Pianese
, and G.
Cavaccini
, “Automatic ultrasonic inspection for internal defect detection in composite materials
,” NDT&E Int.
41
, 145
(2008
). 3.
L.
Séguin-Charbonneau
, J.
Walter
, L. D.
Théroux
, L.
Scheed
, A.
Beausoleil
, and B.
Masson
, “Automated defect detection for ultrasonic inspection of CFRP aircraft components
,” NDT&E Int.
122
, 102478
(2021
). 4.
Y.
Liu
, M. K.
Zheng
, K. X.
Liu
, Y.
Yao
, and S.
Sfarra
, “Trimap thermography with convolutional autoencoder for enhanced defect detection of polymer composites
,” J. Appl. Phys.
131
, 144901
(2022
). 5.
S.
Senck
, M.
Scheerer
, V.
Revol
, B.
Plank
, C.
Hannesschläger
, C.
Gusenbauer
, and J.
Kastner
, “Microcrack characterization in loaded CFRP laminates using quantitative two-and three-dimensional X-ray dark-field imaging
,” Composites, Part A
115
, 206
(2018
). 6.
Y. L.
Du
, X. D.
Li
, S. J.
Xie
, S. Y.
Yang
, and Z. M.
Chen
, “Reconstruction of cracks in a carbon fiber-reinforced polymer laminate plate from signals of eddy current testing
,” J. Compos. Mater.
54
, 3527
(2020
). 7.
Y. P.
Liang
, L. B.
Bai
, X.
Zhang
, C.
Ren
, and Y. H.
Cheng
, “Potential of eddy current pulsed thermography as a nondestructive testing method
,” IEEE Instrum. Meas. Mag.
25
, 5
(2022
). 8.
M.
Kharrat
and L.
Gaillet
, “Non-destructive evaluation of anchorage zones by ultrasonics techniques
,” Ultrasonics
61
, 52
(2015
). 9.
K. Y.
Zheng
and Y.
Yao
, “Automatic three-dimensional reconstruction of subsurface defects by segmenting ultrasonic point cloud
,” J. Taiwan Inst. Chem. Eng.
120
, 24
(2021
). 10.
W.
Zeng
, H. T.
Wang
, G. Y.
Tian
, and G. X.
Hu
, “Application of laser ultrasound imaging technology in the frequency domain based on Wigner–Ville algorithm for detecting defect
,” Opt. Laser Technol.
74
, 72
(2015
). 11.
M.
Le
, J.
Kim
, S.
Kim
, and J.
Lee
, “B-scan ultrasonic testing of rivets in multilayer structures based on short-time Fourier transform analysis
,” Measurement
128
, 495
(2018
). 12.
T.
Liu
, J.
Li
, X. F.
Cai
, and S. Z.
Yan
, “A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform
,” Appl. Acoust.
131
, 16
(2018
). 13.
N.
Munir
, J.
Park
, H. J.
Kim
, S. J.
Song
, and S. S.
Kang
, “Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder
,” NDT&E Int.
111
, 102218
(2020
). 14.
T.
Latete
, B.
Gauthier
, and P.
Belanger
, “Towards using convolutional neural network to locate, identify and size defects in phased array ultrasonic testing
,” Ultrasonics
115
, 106436
(2021
). 15.
R. C.
You
, Y.
Yao
, J.
Shi
, K. Y.
Zheng
, and K. H.
Wang
, “Feature-selective clustering for ultrasonic-based automatic defect detection in FRP structures
,” Chemom. Intell. Lab. Syst.
157
, 35
(2016
). 16.
A.
Rodriguez-Hidalgo
, A. M.
Gomez
, N.
Bochud
, J. M.
Soto
, and A. M.
Peinado
, “A clustering-based damage segmentation for ultrasonic C-scans of CFRP plates
,” in Proceedings of 2015 IEEE International Ultrasonics Symposium (IUS)
(IEEE
, Taiwan
, 2018
), pp. 1
–4
.17.
M.
Joswiak
, Y.
Peng
, I.
Castillo
, and L. H.
Chiang
, “Dimensionality reduction for visualizing industrial chemical process data
,” Control Eng. Pract.
93
, 104189
(2019
). 18.
N.
Rajic
, “Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures
,” Compos. Struct.
58
, 521
(2002
). 19.
K. X.
Liu
, Y. J.
Li
, J. G.
Yang
, Y.
Liu
, and Y.
Yao
, “Generative principal component thermography for enhanced defect detection and analysis
,” IEEE Trans. Instrum. Meas.
69
, 8261
(2020
). 20.
X. F.
Zhang
, Y. Z.
He
, T.
Chady
, G. Y.
Tian
, J. W.
Gao
, H. J.
Wang
, and S.
Chen
, “CFRP impact damage inspection based on manifold learning using ultrasonic induced thermography
,” IEEE Trans. Ind. Inform.
15
, 2648
(2019
). 21.
Y.
Liu
, K. X.
Liu
, J. G.
Yang
, and Y.
Yao
, “Spatial-neighborhood manifold learning for nondestructive testing of defects in polymer composites
,” IEEE Trans. Ind. Inform.
16
, 4639
(2020
). 22.
B.
Yousefi
, S.
Sfarra
, C.
Ibarra-Castanedo
, and X. P. V.
Maldague
, “Comparative analysis on thermal non-destructive testing imagery applying candid covariance-free incremental principal component thermography
,” Infrared Phys. Technol.
85
, 163
(2017
). 23.
J. B.
Tenenbaum
, V. D.
Silva
, and J. C.
Langford
, “A global geometric framework for nonlinear dimensionality reduction
,” Science
290
, 2319
(2000
). 24.
L.
Mcinnes
and J.
Healy
, “UMAP: Uniform manifold approximation and projection for dimension reduction
,” J. Open Source Softw.
3
, 861
(2018
). 25.
L.
Bai
, M. K.
Liu
, N. X.
Liu
, X.
Su
, F. Y.
Lai
, and J. F.
Xu
, “Dimensionality reduction of ultrasonic array data for characterization of inclined defects based on supervised locality preserving projection
,” Ultrasonics
119
, 106625
(2022
). 26.
H.
Taheri
and A. A.
Hassen
, “Nondestructive ultrasonic inspection of composite materials: A comparative advantage of phased array ultrasonic
,” Appl. Sci.
9
, 1628
(2019
). 27.
M.
Santos
, J.
Santos
, P.
Reis
, and A.
Amaro
, “Ultrasonic C-scan techniques for the evaluation of impact damage in CFRP
,” Mater. Test.
63
, 131
(2021
). 28.
R. C.
You
, Y.
Yao
, and J.
Shi
, “Tensor-based ultrasonic data analysis for defect detection in fiber reinforced polymer (FRP) composites
,” Chemom. Intell. Lab. Syst.
163
, 24
(2017
). 29.
U.
Von Luxburg
, “A tutorial on spectral clustering
,” Stat. Comput.
17
, 395
(2007
). 30.
L.
McInnes
, see https://umap.scikit-tda.org/index.html for “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, Built with Sphinx Using a Theme Provided by Read the Docs” (2018).31.
A.
Kanezaki
, “Unsupervised image segmentation by backpropagation
,” in Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
(IEEE
, Canada
, 2018
), pp. 1543
–1547
.32.
R.
Achanta
, A.
Shaji
, K.
Smith
, A.
Lucchi
, P.
Fua
, and S.
Süsstrunk
, “SLIC superpixels compared to state-of-the-art superpixel methods
,” IEEE Trans. Pattern Anal. Mach. Intell.
34
, 2274
(2012
). 33.
W. C.
Sun
, F.
Su
, and L. Q.
Wang
, “Improving deep neural networks with multi-layer maxout networks and a novel initialization method
,” Neurocomputing
278
, 34
(2018
). 34.
H.
Rezatofighi
, N.
Tsoi
, J. Y.
Gwak
, A.
Sadeghian
, I.
Reid
, and S.
Savarese
, “Generalized intersection over union: A metric and a loss for bounding box regression
,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
(IEEE
, America
, 2019
), pp. 658
–666
.35.
B. J.
Wei
, Y. S.
Chang
, Y.
Yao
, and J.
Fang
, “Online estimation and monitoring of local permeability in resin transfer molding
,” Polym. Compos.
37
, 1249
(2016
). 36.
S.
Pavlopoulou
, K.
Worden
, and C.
Soutis
, “Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminates
,” J. Intell. Mater. Syst. Struct.
27
, 549
(2016
). © 2022 Author(s). Published under an exclusive license by AIP Publishing.
2022
Author(s)
You do not currently have access to this content.
