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Failure Mode of Thermal Barrier Coatings Based on Acoustic Emission Under Three-Point Bending via Machine Learning Based on in-situ Acoustic Emission Signals
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Zhijun CAO1, 2, Jianhui YUAN1, Huaiyu SU2, Jiabao WAN2, Jiahui SU2, Qian WU2, Liang WANG2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 373 - 388
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Journal of the Chinese Ceramic Society | 2023, 51(2): 373-388
• Research Articles •
Failure Mode of Thermal Barrier Coatings Based on Acoustic Emission Under Three-Point Bending via Machine Learning Based on in-situ Acoustic Emission Signals
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Zhijun CAO1, 2, Jianhui YUAN1, Huaiyu SU2, Jiabao WAN2, Jiahui SU2, Qian WU2, Liang WANG2
Affiliations
  • 1.School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • 2.Integrated Computational Materials Research Center, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, China

    • CAO Zhijun (1998-), male, Master candidate. E-mail:

About Author:

CAO Zhijun (1998-), male, Master candidate. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220928
Outline
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It is ready to cause the interface delamination, macroscopic fracture and spalling failure of thermal barrier coatings (TBCs) due to their complexity of architecture and harsh service environment. In this paper, the failure process of TBCs under three-point bending (3PB) load was monitored in real time via acoustic emission (AE) technology, and the damage failure modes of TBCs were identified based on micro-morphology analysis of AE parameters and K-means cluster. The waveform characteristics of four failure modes were analyzed by fast Fourier transform and wavelet packet transform. The macroscopic fracture or spalling failure signals have no obvious frequency band, while the corresponding frequency components of substrate deformation, surface vertical crack, sliding interface crack and opening interface crack are 62.5-125.0 kHz, 187.5-250.0 kHz, 250.0-312.5 kHz and 375.0-437.5 kHz, respectively. The method of deep machine learning was used to process the in-situ acoustic emission signals. The wavelet energy coefficient was extracted as a characteristic vector of the Back propagation neural network, and the advantages and disadvantages of the model were evaluated by convergence curve, confusion matrix, Receiver operating characteristic curve and F1 value, thus realizing the discrimination of failure modes of TBCs under 3PB test and providing a reference value for failure prediction and life assessment of thermal barrier coatings.

thermal barrier coating  /  three-point bending  /  acoustic emission  /  failure mode  /  back propagation neural network
曹枝军, 袁建辉, 苏怀宇, 万家宝, 苏佳卉, 吴倩, 王亮. 基于原位声发射信号的热障涂层三点弯曲失效模式的机器学习. 硅酸盐学报, 2023 , 51 (2) : 373 -388 . DOI: 10.14062/j.issn.0454-5648.20220928
Zhijun CAO, Jianhui YUAN, Huaiyu SU, Jiabao WAN, Jiahui SU, Qian WU, Liang WANG. Failure Mode of Thermal Barrier Coatings Based on Acoustic Emission Under Three-Point Bending via Machine Learning Based on in-situ Acoustic Emission Signals[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 373 -388 . DOI: 10.14062/j.issn.0454-5648.20220928
Appendix
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Year 2023 volume 51 Issue 2
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Article Info
doi: 10.14062/j.issn.0454-5648.20220928
  • Receive Date：2022-10-28
  • Online Date：2026-10-20
  • Published：2023-02-20
Article Data
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History
  • Received：2022-10-28
  • Revised：2022-11-10
Funding
Affiliations
    1.School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    2.Integrated Computational Materials Research Center, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, China

Corresponding:

WANG Liang (1982-), male, Associate Professor. E-mail:
YUAN Jianhui (1981-), male, Associate Professor.
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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