Noise reduction for fatigue crack growth test data under random loading

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dc.contributor.authorChoi, Jae-Manko
dc.contributor.authorKim, Chung-Youbko
dc.contributor.authorSong, Ji Hoko
dc.date.accessioned2013-03-08T15:48:34Z-
dc.date.available2013-03-08T15:48:34Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2009-04-
dc.identifier.citationMATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, v.505, no.1-2, pp.163 - 168-
dc.identifier.issn0921-5093-
dc.identifier.urihttp://hdl.handle.net/10203/93464-
dc.description.abstractThe noise included in electrical signal significantly influences on the estimating results of crack length and crack opening point. The noise can be effectively reduced by averaging some consecutive loading cycles without distortion of the signal for constant amplitude loading tests, whereas the averaging technique cannot be applied to random loading tests due to the variation of load. In this study, a noise reduction technique was developed by using a load-based averaging technique, which can be applied to fatigue crack growth test data under random loading. Additionally, the developed noise reduction method was applied to random loading test data to verify the effectiveness of the method. (C) 2008 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherElsevier Science Sa-
dc.subjectBEHAVIOR-
dc.subjectCLOSURE-
dc.titleNoise reduction for fatigue crack growth test data under random loading-
dc.typeArticle-
dc.identifier.wosid000264364100025-
dc.identifier.scopusid2-s2.0-59649085483-
dc.type.rimsART-
dc.citation.volume505-
dc.citation.issue1-2-
dc.citation.beginningpage163-
dc.citation.endingpage168-
dc.citation.publicationnameMATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING-
dc.identifier.doi10.1016/j.msea.2008.11.005-
dc.contributor.localauthorSong, Ji Ho-
dc.contributor.nonIdAuthorKim, Chung-Youb-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorFatigue crack growth-
dc.subject.keywordAuthorRandom loading-
dc.subject.keywordAuthorNoise reduction-
dc.subject.keywordAuthorLoad-based averaging-
dc.subject.keywordPlusBEHAVIOR-
dc.subject.keywordPlusCLOSURE-
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