Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals
A key parameter in discriminating the failure types of thermal barrier coatings (TBCs) was found out by using the k-means cluster analysis of acoustic emission (AE) signals. It is shown that there are five classes of mechanisms, including surface vertical cracks, opening interface cracks, sliding in...
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| Veröffentlicht in: | Surface & coatings technology 2015-02, Vol.264, p.97-104 |
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| container_title | Surface & coatings technology |
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| creator | Yang, L. Kang, H.S. Zhou, Y.C. Zhu, W. Cai, C.Y. Lu, C. |
| description | A key parameter in discriminating the failure types of thermal barrier coatings (TBCs) was found out by using the k-means cluster analysis of acoustic emission (AE) signals. It is shown that there are five classes of mechanisms, including surface vertical cracks, opening interface cracks, sliding interface cracks, substrate deformation and macroscopic cleavage or spallation. Except for the last one, the other four classes can be clearly distinguished from their peak frequency distributions in the ranges of 170–250, 400–500, 260–350 and 40–150kHz, respectively. However, AE signals overlap with each other in other parameter spaces, e.g., amplitude, energy, rise time, and duration time. The results indicate that the frequency can be applied to identify the AE source mechanisms in TBCs.
•Five failure modes in TBCs are discriminated by the k-means cluster analysis of AE signals.•Frequency is the key parameter in identifying the AE source mechanism.•The characteristic frequency of each failure mode is determined. |
| doi_str_mv | 10.1016/j.surfcoat.2015.01.014 |
| format | Article |
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•Five failure modes in TBCs are discriminated by the k-means cluster analysis of AE signals.•Frequency is the key parameter in identifying the AE source mechanism.•The characteristic frequency of each failure mode is determined.</description><identifier>ISSN: 0257-8972</identifier><identifier>EISSN: 1879-3347</identifier><identifier>DOI: 10.1016/j.surfcoat.2015.01.014</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Acoustic emission ; Cleavage ; Cluster analysis ; Coatings ; Cracks ; Failure analysis ; k-Means clustering ; Key parameter ; Peak frequency ; Spallation ; Thermal barrier coatings</subject><ispartof>Surface & coatings technology, 2015-02, Vol.264, p.97-104</ispartof><rights>2015 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-7b35edd7656c6befeb1c9f71a883cb85cda7fa39e0c6c0d03469733acec4f3f73</citedby><cites>FETCH-LOGICAL-c415t-7b35edd7656c6befeb1c9f71a883cb85cda7fa39e0c6c0d03469733acec4f3f73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0257897215000201$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Yang, L.</creatorcontrib><creatorcontrib>Kang, H.S.</creatorcontrib><creatorcontrib>Zhou, Y.C.</creatorcontrib><creatorcontrib>Zhu, W.</creatorcontrib><creatorcontrib>Cai, C.Y.</creatorcontrib><creatorcontrib>Lu, C.</creatorcontrib><title>Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals</title><title>Surface & coatings technology</title><description>A key parameter in discriminating the failure types of thermal barrier coatings (TBCs) was found out by using the k-means cluster analysis of acoustic emission (AE) signals. It is shown that there are five classes of mechanisms, including surface vertical cracks, opening interface cracks, sliding interface cracks, substrate deformation and macroscopic cleavage or spallation. Except for the last one, the other four classes can be clearly distinguished from their peak frequency distributions in the ranges of 170–250, 400–500, 260–350 and 40–150kHz, respectively. However, AE signals overlap with each other in other parameter spaces, e.g., amplitude, energy, rise time, and duration time. The results indicate that the frequency can be applied to identify the AE source mechanisms in TBCs.
•Five failure modes in TBCs are discriminated by the k-means cluster analysis of AE signals.•Frequency is the key parameter in identifying the AE source mechanism.•The characteristic frequency of each failure mode is determined.</description><subject>Acoustic emission</subject><subject>Cleavage</subject><subject>Cluster analysis</subject><subject>Coatings</subject><subject>Cracks</subject><subject>Failure analysis</subject><subject>k-Means clustering</subject><subject>Key parameter</subject><subject>Peak frequency</subject><subject>Spallation</subject><subject>Thermal barrier coatings</subject><issn>0257-8972</issn><issn>1879-3347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkMFu2zAMhoViBZqlfYVCx12cSpFt2TttCJa1QIBd1rNA01Sq1LEz0RngZ-hLT162cwECBMj_J35-QtxrtdJKlw-HFZ-jxwHG1VrpYqV0qvxKLHRl68yY3H4QC7UubFbVdn0jPjIflFLa1vlCvG0j_TpTj5MEliBfaZIniHCkkaIMvWwDYwzH0MMY-r0cX0h6CN05khynE7Ec_DyMR-hkAzGGZJuzJDF_lpvuzPMh6KGbOPxVAw5pGFDSMTCHoZcc9mnPt-Lap0Z3__pSPG-__dw8Zrsf3582X3cZ5roYM9uYgtrWlkWJZUOeGo21txqqymBTFdiC9WBqUliiapXJy9oaA0iYe-OtWYpPl7unOKTfeXQpCFLXQU8pmtOltXVVrGuVpOVFinFgjuTdKcGAODmt3EzfHdx_-m6m75ROlSfjl4uR0iO_ExTHGBJmakMkHF07hPdO_AFPR5ZO</recordid><startdate>20150225</startdate><enddate>20150225</enddate><creator>Yang, L.</creator><creator>Kang, H.S.</creator><creator>Zhou, Y.C.</creator><creator>Zhu, W.</creator><creator>Cai, C.Y.</creator><creator>Lu, C.</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20150225</creationdate><title>Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals</title><author>Yang, L. ; Kang, H.S. ; Zhou, Y.C. ; Zhu, W. ; Cai, C.Y. ; Lu, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-7b35edd7656c6befeb1c9f71a883cb85cda7fa39e0c6c0d03469733acec4f3f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acoustic emission</topic><topic>Cleavage</topic><topic>Cluster analysis</topic><topic>Coatings</topic><topic>Cracks</topic><topic>Failure analysis</topic><topic>k-Means clustering</topic><topic>Key parameter</topic><topic>Peak frequency</topic><topic>Spallation</topic><topic>Thermal barrier coatings</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, L.</creatorcontrib><creatorcontrib>Kang, H.S.</creatorcontrib><creatorcontrib>Zhou, Y.C.</creatorcontrib><creatorcontrib>Zhu, W.</creatorcontrib><creatorcontrib>Cai, C.Y.</creatorcontrib><creatorcontrib>Lu, C.</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Surface & coatings technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, L.</au><au>Kang, H.S.</au><au>Zhou, Y.C.</au><au>Zhu, W.</au><au>Cai, C.Y.</au><au>Lu, C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals</atitle><jtitle>Surface & coatings technology</jtitle><date>2015-02-25</date><risdate>2015</risdate><volume>264</volume><spage>97</spage><epage>104</epage><pages>97-104</pages><issn>0257-8972</issn><eissn>1879-3347</eissn><abstract>A key parameter in discriminating the failure types of thermal barrier coatings (TBCs) was found out by using the k-means cluster analysis of acoustic emission (AE) signals. It is shown that there are five classes of mechanisms, including surface vertical cracks, opening interface cracks, sliding interface cracks, substrate deformation and macroscopic cleavage or spallation. Except for the last one, the other four classes can be clearly distinguished from their peak frequency distributions in the ranges of 170–250, 400–500, 260–350 and 40–150kHz, respectively. However, AE signals overlap with each other in other parameter spaces, e.g., amplitude, energy, rise time, and duration time. The results indicate that the frequency can be applied to identify the AE source mechanisms in TBCs.
•Five failure modes in TBCs are discriminated by the k-means cluster analysis of AE signals.•Frequency is the key parameter in identifying the AE source mechanism.•The characteristic frequency of each failure mode is determined.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.surfcoat.2015.01.014</doi><tpages>8</tpages></addata></record> |
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| source | Elsevier ScienceDirect Journals |
| subjects | Acoustic emission Cleavage Cluster analysis Coatings Cracks Failure analysis k-Means clustering Key parameter Peak frequency Spallation Thermal barrier coatings |
| title | Frequency as a key parameter in discriminating the failure types of thermal barrier coatings: Cluster analysis of acoustic emission signals |
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