Article(id=1319222530519445915, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220928, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666886400000, receivedDateStr=2022-10-28, revisedDate=1668009600000, revisedDateStr=2022-11-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456761244, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456761244, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456761244, creator=13701087609, updateTime=1792456761244, updator=13701087609, issue=Issue{id=1319222524555153701, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='2', pageStart='283', pageEnd='560', issueExtLink='null', onlineDate='null', pubDate='1676822400000', pubDateStr='2023-02-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456759823, creator='13701087609', updateTime=1792456885477, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223051636560419, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223051636560420, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=373, endPage=388, ext={EN=ArticleExt(id=1319222530712383900, articleId=1319222530519445915, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Failure Mode of Thermal Barrier Coatings Based on Acoustic Emission Under Three-Point Bending via Machine Learning Based on in-situ Acoustic Emission Signals, columnId=1321891532131652078, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Research Articles, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Zhijun CAO^{1, 2}, Jianhui YUAN¹, Huaiyu SU², Jiabao WAN², Jiahui SU², Qian WU², Liang WANG², authorsList=Zhijun CAO, Jianhui YUAN, Huaiyu SU, Jiabao WAN, Jiahui SU, Qian WU, Liang WANG, authorCompany=null, correspAuthors=Jianhui YUAN, Liang WANG, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319222531127619997, articleId=1319222530519445915, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=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, columnId=1321891532337172975, journalTitle=硅酸盐学报, columnName=机器学习在材料研发中的应用专题—研究论文, runingTitle=null, highlight=null, articleAbstract=

由于热障涂层体系结构的复杂和服役环境的恶劣，极易导致涂层发生界面分层、宏观断裂和剥落失效。首先利用声发射技术实时监测了热障涂层在三点弯曲载荷下的失效过程，结合微观形貌特征、声发射参数分析、K-means聚类分析识别了热障涂层损伤失效模式。然后利用Fourier变换、小波包变换等分析了4种失效模式的波形特征，其中宏观断裂或剥落失效信号无明显频带，而基底变形、表面垂直裂纹、剪切型界面裂纹、张开型界面裂纹对应的频率分布范围分别在62.5~125.0 kHz、187.5~250.0 kHz、250.0~312.5 kHz、375.0~437.5 kHz。采用机器学习的方法对原位声发射信号进行了深度处理，提取小波能量系数作为机器学习反向传播神经网络的特征向量，结合收敛曲线、混淆矩阵、受试者工作特征曲线、F₁值评价了该模型优劣性，实现了对于热障涂层失效模式的判别，为热障涂层失效预测和寿命评估提供参考价值。

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CAO Zhijun (1998-), male, Master candidate. E-mail: zjcao_siccas@126.com

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CAO Zhijun (1998-), male, Master candidate. E-mail: zjcao_siccas@126.com

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