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/content/aip/journal/adva/6/9/10.1063/1.4962550
1.
G. Y. Tian and A. Sophian, Ndt & E International 38(1), 77 (2005).
http://dx.doi.org/10.1016/j.ndteint.2004.06.001
2.
T. Chen, G. Y. Tian, A. Sophian, and P. W. Que, Ndt & E International 41(6), 467 (2008).
http://dx.doi.org/10.1016/j.ndteint.2008.02.002
3.
X. Qiu, P. Zhang, J. Wei, X. Cui, C. Wei, and L. Liu, Sensor. Actuat. A. Phys 203, 272 (2013).
http://dx.doi.org/10.1016/j.sna.2013.09.004
4.
R. M. Pidaparti, B. S. Aghazadeh, A. Whitfield, A. S. Rao, and G. P. Mercier, Corrosi. Sci 52(11), 3661 (2010).
http://dx.doi.org/10.1016/j.corsci.2010.07.017
5.
N. Merah, J. Qual. Technol. 9(2), 160 (2003).
6.
M. K. Raja, S. Mahadevan, B. P. C. Rao, S. P. Behera, T. Jayakumar, and Baldev Raj, Meas. Sci. Technol. 21(10), 105702 (2010).
http://dx.doi.org/10.1088/0957-0233/21/10/105702
7.
W. D. Dover and C. C. Monahan, Fatigue. Fract. Eng. M. 17(12), 1485 (1994).
http://dx.doi.org/10.1111/j.1460-2695.1994.tb00790.x
8.
I.S. Hwang, Meas. Sci. Technol. 3(1), 62 (1992).
http://dx.doi.org/10.1088/0957-0233/3/1/009
9.
H. Saguy and D. Rittel, Applied Physics Letters 87(8), 084103 (2005).
http://dx.doi.org/10.1063/1.2033131
10.
N. Bowler, Meas. Sci. Technol. 22(1), 012001 (2010).
http://dx.doi.org/10.1088/0957-0233/22/1/012001
11.
Y. Li, F. Gan, Z. Wan, J. Liao, and W. Li, Meas. Sci. Rev. 15(5), 268 (2015).
12.
C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM. T. Intel. Syst. and Tec. 2(3), 27 (2011).
13.
H. G. Ramos, T. Rocha, J. Král, D. Pasadas, and A. L. Ribeiro, Measurement 54, 201 (2014).
http://dx.doi.org/10.1016/j.measurement.2014.01.035
14.
H. N. Ho, K. D. Kim, Y. S. Park, and J. J. Lee, Ndt & E International 58, 18 (2013).
http://dx.doi.org/10.1016/j.ndteint.2013.04.006
15.
N. A. Akram, D. Isa, R. Rajkumarv, and L. H. Lee, Ultrasonics 54(6), 1534 (2014).
http://dx.doi.org/10.1016/j.ultras.2014.03.017
16.
Y. Yao, G. L. Marcialis, M. Pontil, P. Frasconi, and F. Roli, Pattern. Recogn 36(2), 397 (2003).
http://dx.doi.org/10.1016/S0031-3203(02)00039-0
17.
H. A. Wheeler, Proceedings of the IRE 30(9), 412 (1942).
http://dx.doi.org/10.1109/JRPROC.1942.232015
18.
N. Bowler, J. Phys. D. Appl. Phys. 39(3), 584 (2006).
http://dx.doi.org/10.1088/0022-3727/39/3/024
19.
Z. Wan, J. Liao, G. Y. Tian, and L. Cheng, Meas. Sci. Technol. 22(8), 085708 (2011).
http://dx.doi.org/10.1088/0957-0233/22/8/085708
20.
F. Gan, G. Tian, Z. Wan, J. Liao, and W. Li, Measurement 82, 46 (2016).
http://dx.doi.org/10.1016/j.measurement.2015.12.040
21.
J. D. Rodriguez, A. Perez, and J. A. Lozano, IEEE. T. Pattern. Anal. 32(3), 569 (2010).
http://dx.doi.org/10.1109/TPAMI.2009.187
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/content/aip/journal/adva/6/9/10.1063/1.4962550
2016-09-07
2016-09-30

Abstract

The alternating current potential drop (ACPD) is a nondestructive technique that is widely used to detect and size defects in conductive material. This paper describes a combined ACPD and support vector machine (SVM) approach to accurately recognize typical defects on the bottom surface of a metal plate, i.e., pits and cracks. We first conducted a simulation study, and then, based on ACPD, measured five voltage ratios between the test region and reference region. The analysis of finite simulation data enables the binary classification of two kinds of defects. To obtain an accurate separating hyperplane, key parameters of the SVM classifier were optimized using a genetic algorithm with training data from the simulations. Based on the optimized SVM classifier, reliable estimates of the defects in a metal plate were then obtained. The recognition results of the simulation dataset shows that the trained and optimized SVM model has a high classification accuracy, and the metal plate experiment also indicates that the model has good precision in actual defect classification.

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