| 引用本文: | 曹枝军,袁建辉,苏怀宇,万家宝,苏佳卉,吴倩,王亮.声发射技术在热障涂层失效机理中的研究进展及展望[J].中国表面工程,2023,36(2):34~53 |
| CAO Zhijun,YUAN Jianhui,SU Huaiyu,WAN Jiabao,SU Jiahui,WU Qian,WANG Liang.Research Progress and Prospect of Acoustic Emission Technology in failure Mechanism of Thermal Barrier Coatings[J].China Surface Engineering,2023,36(2):34~53 |
|
| |
|
|
| 本文已被：浏览 1424次 下载 743次 |
码上扫一扫！ |
|
|
| 声发射技术在热障涂层失效机理中的研究进展及展望 |
|
曹枝军1,2, 袁建辉1, 苏怀宇2, 万家宝2, 苏佳卉2, 吴倩2, 王亮2
|
|
1.上海工程技术大学材料工程学院 上海 201620;2.中国科学院上海硅酸盐研究所集成计算材料研究中心 上海 201899
|
|
| 摘要: |
| 热障涂层(TBCs)具有优异的高温抗氧化、高温力学和抗热腐蚀性能而备受关注，广泛应用于航空发动机和燃气轮机热端部件中。热障涂层服役环境的恶劣和涂层体系结构的复杂，极易导致涂层发生界面分层或剥落失效，因此通过对热障涂层的裂纹萌生和扩展问题进行实时监测，对于失效机理研究显得尤为重要。简述光激发荧光压电光谱(PLPS)、红外热成像 (IRT)、阻抗谱(IS)的原理及其在热障涂层失效行为研究中的应用，重点介绍声发射技术在热障涂层失效机理方面的研究成果。基于声发射的热障涂层失效过程的信号分析和深度处理，结合声发射技术在热障涂层中的参数分析和波形分析，对热障涂层失效过程及失效形态进行模式识别，通过损伤程度的定量评估来进行热障涂层的寿命预测。对声发射技术在热障涂层失效预测及寿命评估指明了方向，并创新性地对未来声发射技术在热障涂层的疲劳损伤方面研究趋势提出展望。 |
| 关键词: 热障涂层 声发射 失效机理 模式识别 机器学习 |
| DOI：10.11933/j.issn.1007?9289.20220728001 |
| 分类号:TG156;TB114 |
| 基金项目:国家自然科学基金(51301192，51671208，91960107)、NSAF 联合基金(U1730139)、中国博士后科学基金(2021M691341)和上海市自然科学基金(19ZR1479600)资助项目 |
|
| Research Progress and Prospect of Acoustic Emission Technology in failure Mechanism of Thermal Barrier Coatings |
|
CAO Zhijun1,2, YUAN Jianhui1, SU Huaiyu2, WAN Jiabao2, SU Jiahui2, WU Qian2, WANG Liang2
|
|
1.School of Material 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
|
| Abstract: |
| Thermal barrier coatings (TBCs) have attracted much attention because of their excellent high-temperature oxidation resistance and high-temperature mechanical and hot corrosion resistance. They are widely used in aero-engine and gas turbine hot-end components. Owing to the poor service environment and complex structure of TBCs, elastic–plastic deformation of the substrate, surface cracks, and interface cracks easily occur, leading to interface delamination or spalling failure. To understand the crack initiation and propagation in TBCs further, it is important to predict the cracking, delamination time, delamination or failure location, and damage mode of TBCs accurately. In this study, the principles of photo-stimulated luminescence piezo spectroscopy, infrared thermography, and impedance spectroscopy, as well as their applications in the study of the failure behavior of TBCs, are briefly reviewed, with emphasis on the application of acoustic emission (AE) technology as an important nondestructive testing method in the study of the failure behavior of TBCs. The research achievements of scholars worldwide in exploring the failure mechanisms of TBCs under tension, compression, three-point bending, high-temperature thermal shock, thermal shock, etc. are summarized. Based on the signal analysis and deep processing of the failure process of TBCs, the characteristics of AE signals are represented by waveform characteristic parameters (such as amplitude, energy, count, duration, rise time, peak frequency, and center frequency). Combined with parameter analysis and waveform analysis of AE technology in TBCs, the failure process and failure patterns of the TBCs are recognized based on AE signals. The service life of TBCs is predicted by a quantitative evaluation of the degree of damage. These results can provide a reference for the development of an AE monitoring platform for TBCs and promote the development of engineering applications. Although significant progress has been made in analyzing the degradation behavior and reliability of TBC systems in recent years, some shortcomings in the ability to predict damage evolution in terms of crack initiation and propagation remain. AE can be used for permanent structural-health monitoring of TBCs. For the damage evolution of microcracks and pores, adverse changes in the structure are detected and explained to improve reliability, and the elastic waves carrying the information about defects and potential defects are analyzed. In summary, AE technology plays an important role in the characterization, service status, and life prediction of TBCs. It is also an effective auxiliary method in the failure research of TBCs. Machine learning is a subset of artificial intelligence, and the application of nondestructive testing technology in TBCs using machine-learning methods is briefly described. Methods to realize intelligent recognition and life prediction of machine learning in TBCs based on AE technology are discussed. These research results not only enrich the understanding of AE technology but also indicate the direction for failure prediction and life assessment of TBCs. Finally, a future AE technology research trend in the fatigue damage of TBCs is proposed. |
| Key words: thermal barrier coatings acoustic emission failure mechanism pattern recognition machine learning |
|
|
|
|
