Bayesian analysis of critical fatigue failure sources
•The inclusion size distribution from fatigue testing is filtered by fatigue process.•Observation of an inclusion limits the possible sizes of other failure sources.•The presented model has enhanced fatigue size effect prediction capabilities. A novel approach for inferring the underlying non-metall...
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| Veröffentlicht in: | International journal of fatigue 2020-01, Vol.130, p.105282, Article 105282 |
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| container_title | International journal of fatigue |
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| creator | Vaara, Joona Väntänen, Miikka Kämäräinen, Panu Kemppainen, Jukka Frondelius, Tero |
| description | •The inclusion size distribution from fatigue testing is filtered by fatigue process.•Observation of an inclusion limits the possible sizes of other failure sources.•The presented model has enhanced fatigue size effect prediction capabilities.
A novel approach for inferring the underlying non-metallic inclusion distribution from fatigue test fractography is presented. It is shown that the non-metallic inclusion size distribution obtained from fatigue testing differs from the extreme value distributions, which do not take fatigue into account. Fatigue, as a process, acts as a filter for the observed inclusions, and by taking advantage of this allows us to extract more refined information from the fractography using statistical inference. The emphasis in this paper is on analysis of axial fatigue testing of smooth specimens. The concepts presented here apply to all fatigue testing where the data from fracture surfaces is collected. |
| doi_str_mv | 10.1016/j.ijfatigue.2019.105282 |
| format | Article |
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A novel approach for inferring the underlying non-metallic inclusion distribution from fatigue test fractography is presented. It is shown that the non-metallic inclusion size distribution obtained from fatigue testing differs from the extreme value distributions, which do not take fatigue into account. Fatigue, as a process, acts as a filter for the observed inclusions, and by taking advantage of this allows us to extract more refined information from the fractography using statistical inference. The emphasis in this paper is on analysis of axial fatigue testing of smooth specimens. The concepts presented here apply to all fatigue testing where the data from fracture surfaces is collected.</description><identifier>ISSN: 0142-1123</identifier><identifier>EISSN: 1879-3452</identifier><identifier>DOI: 10.1016/j.ijfatigue.2019.105282</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Bayesian analysis ; Bayesian inference ; Extreme values ; Failure analysis ; Fatigue failure ; Fatigue size effect ; Fatigue tests ; Fractography ; Fracture surfaces ; Inclusion size distribution ; Materials fatigue ; Nonmetallic inclusions ; Size distribution ; Statistical inference</subject><ispartof>International journal of fatigue, 2020-01, Vol.130, p.105282, Article 105282</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Jan 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-b44fe7615af5dcc645fb4aa6228975593f7cdde42e06d54c5951671568e80c383</citedby><cites>FETCH-LOGICAL-c392t-b44fe7615af5dcc645fb4aa6228975593f7cdde42e06d54c5951671568e80c383</cites><orcidid>0000-0002-8015-5998 ; 0000-0003-2288-0902</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijfatigue.2019.105282$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Vaara, Joona</creatorcontrib><creatorcontrib>Väntänen, Miikka</creatorcontrib><creatorcontrib>Kämäräinen, Panu</creatorcontrib><creatorcontrib>Kemppainen, Jukka</creatorcontrib><creatorcontrib>Frondelius, Tero</creatorcontrib><title>Bayesian analysis of critical fatigue failure sources</title><title>International journal of fatigue</title><description>•The inclusion size distribution from fatigue testing is filtered by fatigue process.•Observation of an inclusion limits the possible sizes of other failure sources.•The presented model has enhanced fatigue size effect prediction capabilities.
A novel approach for inferring the underlying non-metallic inclusion distribution from fatigue test fractography is presented. It is shown that the non-metallic inclusion size distribution obtained from fatigue testing differs from the extreme value distributions, which do not take fatigue into account. Fatigue, as a process, acts as a filter for the observed inclusions, and by taking advantage of this allows us to extract more refined information from the fractography using statistical inference. The emphasis in this paper is on analysis of axial fatigue testing of smooth specimens. The concepts presented here apply to all fatigue testing where the data from fracture surfaces is collected.</description><subject>Bayesian analysis</subject><subject>Bayesian inference</subject><subject>Extreme values</subject><subject>Failure analysis</subject><subject>Fatigue failure</subject><subject>Fatigue size effect</subject><subject>Fatigue tests</subject><subject>Fractography</subject><subject>Fracture surfaces</subject><subject>Inclusion size distribution</subject><subject>Materials fatigue</subject><subject>Nonmetallic inclusions</subject><subject>Size distribution</subject><subject>Statistical inference</subject><issn>0142-1123</issn><issn>1879-3452</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLAzEUhYMoWKu_wQHXU_N-LGvxBQU3ug5p5kYyjJ2azAjz702Z4tbVgcs5h3M_hG4JXhFM5H27im1wQ_wcYUUxMeUqqKZnaEG0MjXjgp6jBSac1oRQdomucm4xxgYrsUDiwU2Qo9tXbu-6Kcdc9aHyKQ7Ru646FReN3Zigyv2YPORrdBFcl-HmpEv08fT4vnmpt2_Pr5v1tvbM0KHecR5ASSJcEI33kouw485JSrVRQhgWlG8a4BSwbAT3wggiFRFSg8aeabZEd3PvIfXfI-TBtmVAGZotZQRTfSRQXGp2-dTnnCDYQ4pfLk2WYHt02Nb-MbJHRnZmVJLrOQnliZ8IyWYfYe-hiQn8YJs-_tvxC2NWcuo</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Vaara, Joona</creator><creator>Väntänen, Miikka</creator><creator>Kämäräinen, Panu</creator><creator>Kemppainen, Jukka</creator><creator>Frondelius, Tero</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><orcidid>https://orcid.org/0000-0002-8015-5998</orcidid><orcidid>https://orcid.org/0000-0003-2288-0902</orcidid></search><sort><creationdate>202001</creationdate><title>Bayesian analysis of critical fatigue failure sources</title><author>Vaara, Joona ; Väntänen, Miikka ; Kämäräinen, Panu ; Kemppainen, Jukka ; Frondelius, Tero</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-b44fe7615af5dcc645fb4aa6228975593f7cdde42e06d54c5951671568e80c383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bayesian analysis</topic><topic>Bayesian inference</topic><topic>Extreme values</topic><topic>Failure analysis</topic><topic>Fatigue failure</topic><topic>Fatigue size effect</topic><topic>Fatigue tests</topic><topic>Fractography</topic><topic>Fracture surfaces</topic><topic>Inclusion size distribution</topic><topic>Materials fatigue</topic><topic>Nonmetallic inclusions</topic><topic>Size distribution</topic><topic>Statistical inference</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vaara, Joona</creatorcontrib><creatorcontrib>Väntänen, Miikka</creatorcontrib><creatorcontrib>Kämäräinen, Panu</creatorcontrib><creatorcontrib>Kemppainen, Jukka</creatorcontrib><creatorcontrib>Frondelius, Tero</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>International journal of fatigue</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vaara, Joona</au><au>Väntänen, Miikka</au><au>Kämäräinen, Panu</au><au>Kemppainen, Jukka</au><au>Frondelius, Tero</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian analysis of critical fatigue failure sources</atitle><jtitle>International journal of fatigue</jtitle><date>2020-01</date><risdate>2020</risdate><volume>130</volume><spage>105282</spage><pages>105282-</pages><artnum>105282</artnum><issn>0142-1123</issn><eissn>1879-3452</eissn><abstract>•The inclusion size distribution from fatigue testing is filtered by fatigue process.•Observation of an inclusion limits the possible sizes of other failure sources.•The presented model has enhanced fatigue size effect prediction capabilities.
A novel approach for inferring the underlying non-metallic inclusion distribution from fatigue test fractography is presented. It is shown that the non-metallic inclusion size distribution obtained from fatigue testing differs from the extreme value distributions, which do not take fatigue into account. Fatigue, as a process, acts as a filter for the observed inclusions, and by taking advantage of this allows us to extract more refined information from the fractography using statistical inference. The emphasis in this paper is on analysis of axial fatigue testing of smooth specimens. The concepts presented here apply to all fatigue testing where the data from fracture surfaces is collected.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ijfatigue.2019.105282</doi><orcidid>https://orcid.org/0000-0002-8015-5998</orcidid><orcidid>https://orcid.org/0000-0003-2288-0902</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0142-1123 |
| ispartof | International journal of fatigue, 2020-01, Vol.130, p.105282, Article 105282 |
| issn | 0142-1123 1879-3452 |
| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Bayesian analysis Bayesian inference Extreme values Failure analysis Fatigue failure Fatigue size effect Fatigue tests Fractography Fracture surfaces Inclusion size distribution Materials fatigue Nonmetallic inclusions Size distribution Statistical inference |
| title | Bayesian analysis of critical fatigue failure sources |
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