DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, CS | - |
dc.contributor.author | Hwang, W | - |
dc.contributor.author | Park, HC | - |
dc.contributor.author | Han, KS | - |
dc.date.accessioned | 2016-03-31T13:38:28Z | - |
dc.date.available | 2016-03-31T13:38:28Z | - |
dc.date.created | 2009-03-17 | - |
dc.date.issued | 1999-01 | - |
dc.identifier.issn | 0266-3538 | - |
dc.identifier.other | 1999-OAK-0000000926 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20278 | - |
dc.description.abstract | Biaxial tests have been conducted on cross-ply carbon/epoxy composite tube under combined torsion and axial tension/compression up to failure. Strength properties and distributions were evaluated with reference to the biaxial loading ratio. The scatter of the biaxial strength data was analyzed by using a Weibull distribution function. Artificial neural networks were introduced to pre diet failure strength by means of the error back-propagation algorithm for learning, providing a different and new approach to the representation of complicated behavior of composite materials. further prediction is made from experimental data by the use of Tsai-Wu theory and a combined optimized tensor polynomial theory. Comparison shows that the artificial neural network has the smallest root-mean-square error of the three prediction methods. (C) 1999 Elsevier Science Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.relation.isPartOf | COMPOSITES SCIENCE AND TECHNOLOGY | - |
dc.subject | stress/strain curves | - |
dc.subject | failure criterion | - |
dc.subject | artificial neural networks (ANN) | - |
dc.subject | biaxial strength | - |
dc.subject | fiber reinforced plastics (FRP) | - |
dc.subject | COMBINED EXTERNAL-PRESSURE | - |
dc.subject | WINDING ANGLE | - |
dc.subject | STRAIN | - |
dc.subject | STRESS | - |
dc.title | Fail-are of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural networks | - |
dc.type | Article | - |
dc.contributor.college | 기계공학과 | - |
dc.identifier.doi | 10.1016/S0266-3538(99)00038-X | - |
dc.author.google | Lee, CS | - |
dc.author.google | Hwang, W | - |
dc.author.google | Park, HC | - |
dc.author.google | Han, KS | - |
dc.relation.volume | 59 | - |
dc.relation.issue | 12 | - |
dc.relation.startpage | 1779 | - |
dc.relation.lastpage | 1788 | - |
dc.contributor.id | 10053430 | - |
dc.relation.journal | COMPOSITES SCIENCE AND TECHNOLOGY | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | COMPOSITES SCIENCE AND TECHNOLOGY, v.59, no.12, pp.1779 - 1788 | - |
dc.identifier.wosid | 000082561700001 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1788 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 1779 | - |
dc.citation.title | COMPOSITES SCIENCE AND TECHNOLOGY | - |
dc.citation.volume | 59 | - |
dc.contributor.affiliatedAuthor | Hwang, W | - |
dc.contributor.affiliatedAuthor | Park, HC | - |
dc.contributor.affiliatedAuthor | Han, KS | - |
dc.identifier.scopusid | 2-s2.0-0032722381 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 39 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | COMBINED EXTERNAL-PRESSURE | - |
dc.subject.keywordPlus | WINDING ANGLE | - |
dc.subject.keywordPlus | STRAIN | - |
dc.subject.keywordPlus | STRESS | - |
dc.subject.keywordAuthor | stress/strain curves | - |
dc.subject.keywordAuthor | failure criterion | - |
dc.subject.keywordAuthor | artificial neural networks (ANN) | - |
dc.subject.keywordAuthor | biaxial strength | - |
dc.subject.keywordAuthor | fiber reinforced plastics (FRP) | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Composites | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Materials Science | - |
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