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Cited 25 time in webofscience Cited 26 time in scopus
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Characterization of Hot Deformation Behavior and Processing Maps of Ti-19Al-22Mo Alloy

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dc.contributor.authorNarayana, P. L.-
dc.contributor.authorLi, Cheng-Lin-
dc.contributor.authorHong, Jae-Keun-
dc.contributor.authorChoi, Seong-Woo-
dc.contributor.authorPark, Chan Hee-
dc.contributor.authorKim, Seong-Woong-
dc.contributor.authorKim, Seung Eon-
dc.contributor.authorReddy, N. S.-
dc.contributor.authorYeom, Jong-Taek-
dc.date.accessioned2022-12-26T14:47:20Z-
dc.date.available2022-12-26T14:47:20Z-
dc.date.issued2019-07-
dc.identifier.issn1598-9623-
dc.identifier.issn2005-4149-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/9002-
dc.description.abstractThe isothermal compression tests were carried out to study the hot deformation behavior and microstructure evolution of Ti-19Al-22Mo alloy. The samples were deformed in the temperature range from 1100 to 1250 degrees C with an interval of 50 degrees C, strain rate ranging from 0.01 to 1 s(-1) and the height reduction of 50% using Gleeble-3800 thermal-mechanical simulator. By using this experimental data an artificial neural network (ANN) model was developed and evaluated with unseen data. Further, the developed ANN model was used to predict flow stress correction from adiabatic heating at finer intervals of strain rates and temperatures. The predicted isothermal flow stress values were utilized to construct processing maps for Ti-19Al-22Mo alloy at true strain of 0.4 and 0.6. The maximum efficiency was noticed at 1100 degrees C with the strain rate of 0.01 s(-1) associated with dynamic recrystallization and dynamic recovery. The deformation conditions of the instability domains in processing map showed wedge cracking and flow localization. Using the processing maps safe working parameters for hot deformation of Ti-19Al-22Mo alloy was identified.-
dc.format.extent9-
dc.language영어-
dc.language.isoENG-
dc.publisher대한금속·재료학회-
dc.titleCharacterization of Hot Deformation Behavior and Processing Maps of Ti-19Al-22Mo Alloy-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1007/s12540-018-00237-4-
dc.identifier.scopusid2-s2.0-85061066824-
dc.identifier.wosid000473163200023-
dc.identifier.bibliographicCitationMetals and Materials International, v.25, no.4, pp 1063 - 1071-
dc.citation.titleMetals and Materials International-
dc.citation.volume25-
dc.citation.number4-
dc.citation.startPage1063-
dc.citation.endPage1071-
dc.type.docTypeArticle-
dc.identifier.kciidART002482150-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaMetallurgy & Metallurgical Engineering-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMetallurgy & Metallurgical Engineering-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusB2 PHASE-
dc.subject.keywordPlusTITANIUM-
dc.subject.keywordPlusSTABILITY-
dc.subject.keywordPlusMO-
dc.subject.keywordPlusMICROSTRUCTURE-
dc.subject.keywordPlusTEMPERATURE-
dc.subject.keywordPlusWORKING-
dc.subject.keywordPlusALPHA-
dc.subject.keywordAuthorTi-19Al-22Mo alloy-
dc.subject.keywordAuthorHot compression test-
dc.subject.keywordAuthorArtificial neural networks-
dc.subject.keywordAuthorProcessing map-
dc.subject.keywordAuthorMicrostructure-
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공과대학 (나노신소재공학부금속재료공학전공)
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