DC Field | Value | Language |
---|---|---|
dc.contributor.author | Reddy, NS | - |
dc.contributor.author | Lee, YH | - |
dc.contributor.author | Park, CH | - |
dc.contributor.author | Lee, CS | - |
dc.date.accessioned | 2016-04-01T01:12:10Z | - |
dc.date.available | 2016-04-01T01:12:10Z | - |
dc.date.created | 2009-04-08 | - |
dc.date.issued | 2008-09-25 | - |
dc.identifier.issn | 0921-5093 | - |
dc.identifier.other | 2008-OAK-0000008076 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/22545 | - |
dc.description.abstract | Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex and nonlinear relationship with them. A number of semi-empirical models were reported by others to predict the flow stress during hot deformation. This work attempts to develop a back-propagation neural network model to predict the flow stress of Ti-6Al-4V alloy for any given processing conditions. The network was successfully trained across different phase regimes (alpha + beta to beta phase) and various deformation domains. This model can predict the mean flow stress within an average error of similar to 5.6% from the experimental values, using strain, strain rate and temperature as inputs. This model seems to have an edge over existing constitutive model, like hyperbolic sine equation, and has a great potential to be employed in industries. (C) 2008 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.relation.isPartOf | MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | - |
dc.subject | hot deformation | - |
dc.subject | neural networks | - |
dc.subject | hyperbolic sine function | - |
dc.subject | flow stress | - |
dc.subject | HOT DEFORMATION-BEHAVIOR | - |
dc.subject | HIGH-SPEED STEEL | - |
dc.subject | AUSTENITIC STEELS | - |
dc.subject | STRENGTH | - |
dc.subject | WORKING | - |
dc.subject | ZR-2.5NB-0.5CU | - |
dc.subject | TEMPERATURES | - |
dc.subject | MODEL | - |
dc.title | Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed alpha plus beta microstructure by artificial neural networks | - |
dc.type | Article | - |
dc.contributor.college | 신소재공학과 | - |
dc.identifier.doi | 10.1016/j.msea.2008.03.030 | - |
dc.author.google | Reddy, NS | - |
dc.author.google | Lee, YH | - |
dc.author.google | Park, CH | - |
dc.author.google | Lee, CS | - |
dc.relation.volume | 492 | - |
dc.relation.issue | 1-2 | - |
dc.relation.startpage | 276 | - |
dc.relation.lastpage | 282 | - |
dc.contributor.id | 10071833 | - |
dc.relation.journal | MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, v.492, no.1-2, pp.276 - 282 | - |
dc.identifier.wosid | 000258644500039 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 282 | - |
dc.citation.number | 1-2 | - |
dc.citation.startPage | 276 | - |
dc.citation.title | MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | - |
dc.citation.volume | 492 | - |
dc.contributor.affiliatedAuthor | Lee, CS | - |
dc.identifier.scopusid | 2-s2.0-47249131718 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 63 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | HOT DEFORMATION-BEHAVIOR | - |
dc.subject.keywordPlus | HIGH-SPEED STEEL | - |
dc.subject.keywordPlus | AUSTENITIC STEELS | - |
dc.subject.keywordPlus | STRENGTH | - |
dc.subject.keywordPlus | WORKING | - |
dc.subject.keywordPlus | ZR-2.5NB-0.5CU | - |
dc.subject.keywordPlus | TEMPERATURES | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | hot deformation | - |
dc.subject.keywordAuthor | neural networks | - |
dc.subject.keywordAuthor | hyperbolic sine function | - |
dc.subject.keywordAuthor | flow stress | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
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