Article Article
NDE of Discontinuities in Thermal Barrier Coatings with Terahertz Time-Domain Spectroscopy and Machine Learning Classifiers

Internal discontinuities are critical factors that can lead to premature failure of thermal barrier coatings (TBCs). This paper proposes a technique that combines terahertz (THz) time-domain spectroscopy and machine learning classifiers to identify discontinuities in TBCs. First, the finite-difference time-domain method was used to build a theoretical model of THz signals due to discontinuities in TBCs. Then, simulations were carried out to compute THz waveforms of different discontinuities in TBCs. Further, six machine learning classifiers were employed to classify these different discontinuities. Principal component analysis (PCA) was used for dimensionality reduction, and the Grid Search method was utilized to optimize the hyperparameters of the designed machine learning classifiers. Accuracy and running time were used to characterize their performances. The results show that the support vector machine (SVM) has a better performance than the others in TBC discontinuity classification. Using PCA, the average accuracy of the SVM classifier is 94.3%, and the running time is 65.6 ms.

DOI: https://doi.org/10.32548/2021.me-04189

References

Ahmadian, S., A. Browning, and E.H. Jordan, 2015, “Three-Dimensional X-ray Micro-Computed Tomography of Cracks in a Furnace Cycled Air Plasma Sprayed Thermal Barrier Coating,” Scripta Materialia, Vol. 97, pp. 13–16, https://doi.org/10.1016/j.scriptamat.2014.10.026.

Aktaa, J., K. Sfar, and D. Munz, 2005, “Assessment of TBC Systems Failure Mechanisms Using a Fracture Mechanics Approach,” Acta Materialia, Vol. 53, No. 16, pp. 4399–4413, https://doi.org/10.1016/j.actamat .2005.06.003.

Altman, N.S., 1992, “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” The American Statistician, Vol. 46, No. 3, pp. 175–185, https://doi.org/10.2307/2685209.

Arenas, M.P., T.J. Rocha, C.S. Angani, A.L. Ribeiro, H.G. Ramos, C.B. Eckstein, J.M.A. Rebello, and G.R. Pereira, 2018, “Novel Austenitic Steel Ageing Classification Method Using Eddy Current Testing and a Support Vector Machine,” Measurement, Vol. 127, pp. 98–103, https://doi.org/10.1016/j.measurement.2018.05.101.

Breiman, L., 1996, “Bagging Predictors,” Machine Learning, Vol. 24, No. 2, pp. 123–140, https://doi.org/10.1007/BF00058655.

Breiman, L., 2001, “Random Forests,” Machine Learning, Vol. 45, No. 1, pp. 5–32, https://doi.org/10.1023/A:1010933404324.

Bu, C., Q. Tang, Y. Liu, F. Yu, C. Mei, and Y. Zhao, 2016, “Quantitative Detection of Thermal Barrier Coating Thickness Based on Simulated Annealing Algorithm Using Pulsed Infrared Thermography Technology,” Applied Thermal Engineering, Vol. 99, pp. 751–755, https://doi.org/10.1016/j.applthermaleng.2016.01.143.

Cao, B., H. Li, M. Fan, W. Wang, and M. Wang, 2018, “Determination of Pesticides in a Flour Substrate by Chemometric Methods Using Terahertz Spectroscopy,” Analytical Methods, Vol. 10, No. 42, pp. 5097–5104, https://doi.org/10.1039/C8AY01728J.

Cao, B., M. Wang, X. Li, M. Fan, and G. Tian, 2020, “Noncontact Thick-ness Measurement of Multilayer Coatings on Metallic Substrate Using Pulsed Terahertz Technology,” IEEE Sensors Journal, Vol. 20, No. 6, pp. 3162–3171, https://doi.org/10.1109/JSEN.2019.2958674.

Chen, H.-L.R., B. Zhang, M.A. Alvin, and Y. Lin, 2012, “Ultrasonic Detection of Delamination and Material Characterization of Thermal Barrier Coatings,” Journal of Thermal Spray Technology, Vol. 21, No. 6, pp. 1184–1194, https://doi.org/10.1007/s11666-012-9811-9.

Cortes, C., and V. Vapnik, 1995, “Support-Vector Networks,” Machine Learning, Vol. 20, No. 3, pp. 273–297, https://doi.org/10.1023/A:1022627411411.

Doaei, M., M.S. Tavallali, and H. Nejati, 2019, “Fault Classification in Electrofusion Polyethylene Joints by Combined Machine Learning, Thermal Pulsing and IR Thermography Methods – a Comparative Study,” Infrared Physics & Technology, Vol. 96, pp. 262–266, https://doi.org/10.1016/j.infrared.2018.11.032.

Fan, M., B. Cao, and G. Tian, 2017, “Enhanced Measurement of Paper Basis Weight Using Phase Shift in Terahertz Time-Domain Spectroscopy,” Journal of Sensors, Vol. 2017, pp. 1–14, https://doi.org/10.1155/2017/3520967.

Fan, M., Q. Wang, B. Cao, B. Ye, A. Sunny, and G. Tian, 2016, “Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique,” Sensors, Vol. 16, No. 5, https://doi.org/10.3390/s16050649.

Franke, B., Y.H. Sohn, X. Chen, J.R. Price, and Z. Mutasim, 2005, “Monitoring Damage Evolution in Thermal Barrier Coatings with Thermal Wave Imaging,” Surface and Coatings Technology, Vol. 200, No. 5, pp. 1292–1297, https://doi.org/10.1016/j.surfcoat.2005.07.090.

Fukuchi, T., N. Fuse, M. Mizuno, and K. Fukunaga, 2013, “THz Measurement of Refractive Index and Thickness of Ceramic Coating on a Metal Substrate,” 2013 Conference on Lasers and Electro-Optics Pacific Rim (CLEOPR), Kyoto, pp. 1–2, https://doi.org/10.1109/CLEOPR.2013 .6600103. 

Fukuchi, T., N. Fuse, M. Okada, T. Fujii, M. Mizuno, and K. Fukunaga, 2013, “Measurement of Refractive Index and Thickness of Topcoat of Thermal Barrier Coating by Reflection Measurement of Terahertz Waves,” Electronics and Communications in Japan, Vol. 96, No. 12, pp. 37–45, https://doi.org/10.1002/ecj.11551.

He, W., and Y. Liu, 2018, “To Regularize or Not: Revisiting SGD with Simple Algorithms and Experimental Studies,” Expert Systems with Applications, Vol. 112, pp. 1–14, https://doi.org/10.1016/j.eswa.2018.06.026.

Huang, H., C. Liu, L. Ni, and C. Zhou, 2011, “Evaluation of TGO Growth in Thermal Barrier Coatings Using Impedance Spectroscopy,” Rare Metals, Vol. 30, pp. 643–646, https://doi.org/10.1007/s12598-011-0363-z.

Jen, C.-Y., and C. Richter, 2014, “Sample Thickness Measurement with THz-TDS: Resolution and Implications,” Journal of Infrared, Millimeter, and Terahertz Waves, Vol. 35, pp. 840–859, https://doi.org/10.1007/s10762-014-0093-9.

Kawase, K., T. Shibuya, S. Hayashi, and K. Suizu, 2010, “THz Imaging Techniques for Nondestructive Inspections,” Comptes Rendus Physique, Vol. 11, No. 7–8, pp. 510–518, https://doi.org/10.1016/j.crhy.2010.04.003.

Lopes, F., J. Agnelo, C.A. Teixeira, N. Laranjeiro, and J. Bernardino, 2020, “Automating Orthogonal Defect Classification Using Machine Learning Algorithms,” Future Generation Computer Systems, Vol. 102, pp. 932–947, https://doi.org/10.1016/j.future.2019.09.009.

McCallum, A., and K.A. Nigam, 1998, “Comparison of Event Models for Naive Bayes Text Classification,” AAAI-98 Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press. 

Mcgill, R., J.W. Tukey, and W.A. Larsen, 1978, “Variations of Box Plots,” The American Statistician, Vol. 32, No. 1, pp. 12–16, https://doi.org/10.1080/00031305.1978.10479236.

Mehboob, G., M.-J. Liu, T. Xu, S. Hussain, G. Mehboob, and A. Tahir, 2020, “A Review on Failure Mechanism of Thermal Barrier Coatings and Strategies to Extend their Lifetime,” Ceramics International, Vol. 46, No. 7, pp. 8497–8521, https://doi.org/10.1016/j.ceramint.2019.12.200.

Padture, N.P., M. Gell, and E.H. Jordan, 2002, “Thermal Barrier Coatings for Gas-Turbine Engine Applications,” Science, Vol. 296, pp. 280–284, https://doi.org/10.1126/science.1068609.

Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, and D. Cournapeau, 2011, “Scikit-Learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825–2830.

Qiao, X., W.X. Weng, and Q. Li, 2019, “Acoustic Emission Monitoring and Failure Behavior Discrimination of 8YSZ Thermal Barrier Coatings Under Vickers Indentation Testing,” Surface and Coatings Technology, Vol. 358, pp. 913–922, https://doi.org/10.1016/j.surfcoat.2018.12.024.

Rodrigues, L.F.M., F.C. Cruz, M.A. Oliveira, E.F. Simas Filho, M.C.S. Albuquerque, I.C. Silva, and C.T.T. Farias, 2019, “Carburization Level Identification in Industrial HP Pipes Using Ultrasonic Evaluation and Machine Learning,” Ultrasonics, Vol. 94, pp. 145–151, https://doi.org /10.1016/j.ultras.2018.10.005.

Schlichting, K.W., N.P. Padture, E.H. Jordan, and M. Gell, 2003, “Failure Modes in Plasma-Sprayed Thermal Barrier Coatings,” Materials Science and Engineering: A, Vol. 342, No. 1, pp. 120–130, https://doi.org/10.1016/S0921-5093(02)00251-4.

Stoik, C.D., M.J. Bohn, and J.L. Blackshire, 2008, “Nondestructive Evalua-tion of Aircraft Composites Using Transmissive Terahertz Time Domain Spectroscopy,” Optics Express, Vol. 16, No. 21, pp. 17039–17051, https://doi.org/10.1364/OE.16.017039.

Tang, Q., J. Dai, C. Bu, L. Qi, and D. Li, 2016, “Experimental Study on Debonding Defects Detection in Thermal Barrier Coating Structure Using Infrared Lock-In Thermographic Technique,” Applied Thermal Engineering, Vol. 107, pp. 463–468, https://doi.org/10.1016/j.applthermaleng .2016.07.008. 

Tu, W., S. Zhong, A. Incecik, and X. Fu, 2018, “Defect Feature Extraction of Marine Protective Coatings by Terahertz Pulsed Imaging,” Ocean Engineering, Vol. 155, pp. 382–391, https://doi.org/10.1016/j.oceaneng.2018.01.033.

Tu, W., S. Zhong, Y. Shen, A. Incecik, and X. Fu, 2019, “Neural Network-Based Hybrid Signal Processing Approach for Resolving Thin Marine Protective Coating by Terahertz Pulsed Imaging,” Ocean Engineering, Vol. 173, pp. 58–67, https://doi.org/10.1016/j.oceaneng.2018.12.051.

Tu, W., S. Zhong, Y. Shen, and A. Incecik, 2016, “Nondestructive Testing of Marine Protective Coatings Using Terahertz Waves with Stationary Wavelet Transform,” Ocean Engineering, Vol. 111, pp. 582–592, https://doi.org/10.1016/j.oceaneng.2015.11.028.

Tu, W., S. Zhong, Y. Shen, Q. Zhou, and L. Yao, 2014, “FDTD-based Quantitative Analysis of Terahertz Wave Detection for Multilayered Structures,” Journal of the Optical Society of America A, Vol. 31, No. 10, pp. 2285–2293, https://doi.org/10.1364/JOSAA.31.002285.

Waszczyszyn, Z., 1999, “Fundamentals of Artificial Neural Networks,” in Neural Networks in the Analysis and Design of Structures, Vol. 404, Springer, Vienna, https://doi.org/10.1007/978-3-7091-2484-0_1.

Wu, N.Q., K. Ogawa, M. Chyu, and S.X. Mao, 2004, “Failure Detection of Thermal Barrier Coatings Using Impedance Spectroscopy,” Thin Solid Films, Vol. 457, No. 2, pp. 301–306, https://doi.org/10.1016/j.tsf.2003.10.009.

Yang, L., Z. Zhong, Y. Zhou, W. Zhu, Z. Zhang, C. Cai, and C. Lu, 2016, “Acoustic Emission Assessment of Interface Cracking in Thermal Barrier Coatings,” Acta Mechanica Sinica, Vol. 32, No. 2, pp. 342–348, https://doi.org/10.1007/s10409-015-0514-6.

Yang, L., Z.-C. Zhong, Y.-C. Zhou, and C.-S. Lu, 2014, “Quantitative Assessment of the Surface Crack Density in Thermal Barrier Coatings,” Acta Mechanica Sinica, Vol. 30, No. 2, pp. 167–174, https://doi.org/10.1007/s10409-014-0019-8.

Yang, Y., M. Fan, B. Cao, E. Cai, and P. Wang, 2019, “Reliable Characteri-zation of Bearing Rings Using Eddy Current and Barkhausen Noise Data Fusion,” Journal of Magnetism and Magnetic Materials, Vol. 489, https://doi.org/10.1016/j.jmmm.2019.165349.

Ye, D., W. Wang, J. Huang, X. Lu, and H. Zhou, 2019, “Nondestructive Interface Morphology Characterization of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy,” Coatings, Vol. 9, No. 2, pp. 89, https://doi.org/10.3390/coatings9020089.

Yong, L., Z. Chen, Y. Mao, and Q. Yong, 2012, “Quantitative Evaluation of Thermal Barrier Coating Based on Eddy Current Technique,” NDT & E International, Vol. 50, pp. 29–35, https://doi.org/10.1016/j.ndteint .2012.04.006.

Zhang, H., X. Xiao, F. Mercaldo, S. Ni, F. Martinelli, and A.K. Sangaiah, 2019, “Classification of Ransomware Families with Machine Learning Based on N-gram of Opcodes,” Future Generation Computer Systems, Vol. 90, pp. 211–221, https://doi.org/10.1016/j.future.2018.07.052.

Zhao, Y., L. Lin, X.M. Li, and M.K. Lei, 2010, “Simultaneous Determination of the Coating Thickness and its Longitudinal Velocity by Ultrasonic Nondestructive Method,” NDT & E International, Vol. 43, No. 7, pp. 579–585, https://doi.org/10.1016/j.ndteint.2010.06.001.

Zhu, W., X.N. Cai, L. Yang, J. Xia, Y.C. Zhou, and Z.P. Pi, 2019, “The Evolution of Pores in Thermal Barrier Coatings Under Volcanic Ash Corrosion Using X-ray Computed Tomography,” Surface and Coatings Technology, Vol. 357, pp. 372–378, https://doi.org/10.1016/j.surfcoat.2018.10.029.

Zhu, W., Z. Liu, D. Jiao, and H. Xie, 2018, “Eddy Current Thermography with Adaptive Carrier Algorithm for Non-Destructive Testing of Debonding Defects in Thermal Barrier Coatings,” Journal of Nondestructive Evaluation, Vol. 37, https://doi.org/10.1007/s10921-018-0483-3.

 

Metrics
Usage Shares
Total Views
42 Page Views
Total Shares
0 Tweets
42
0 PDF Downloads
0
0 Facebook Shares
Total Usage
42