Publication: Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400? utilizing artificial intelligence
dc.contributor.coauthor | Yılmaz, R. | |
dc.contributor.department | Department of Mechanical Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Canadinç, Demircan | |
dc.contributor.kuauthor | Çatal, Aysel Aysu | |
dc.contributor.kuauthor | Kılıç, Elif Bedir | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.date.accessioned | 2024-11-09T23:01:15Z | |
dc.date.issued | 2022 | |
dc.description.abstract | This paper details the design process of a ternary NiTiHf shape memory alloy (SMA) with an austenite finish temperature (A(f)) beyond 400 ?. Specifically, available experimental data on the ternary NiTiHf SMA system was utilized to construct a database, which was employed to train and test a machine learning (ML) algorithm to predict the ideal NiTiHf SMA composition to exhibit an A(f) beyond 400 ?& nbsp;and a relatively smaller hysteresis. For this purpose, a multi-layer feedforward neural network (MLFFNN) model was proposed, trained, and tested. Consequently, the Ni49.7Ti26.6Hf23.7 and Ni(50)Ti27Hf23 alloys predicted by this ML algorithm were selected for validation experiments to assess the accuracy of the ML model's predictions. As a result, the Ni49.7Ti26.6Hf23.7 alloy with an A(f) temperature of 403.5 ? and remarkable cyclic stability was established as a new NiTiHf SMA composition, which can be utilized in applications demanding reversible austenite-to-martensite phase transformation beyond 400 ?. (C) 2022 Elsevier B.V. All rights reserved. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.openaccess | NO | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.volume | 904 | |
dc.identifier.doi | 10.1016/j.jallcom.2022.164135 | |
dc.identifier.eissn | 1873-4669 | |
dc.identifier.issn | 0925-8388 | |
dc.identifier.quartile | Q1 | |
dc.identifier.scopus | 2-s2.0-85124247508 | |
dc.identifier.uri | https://doi.org/10.1016/j.jallcom.2022.164135 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/8197 | |
dc.identifier.wos | 779695600002 | |
dc.keywords | High-temperature shape memory alloy | |
dc.keywords | NiTiHf | |
dc.keywords | Machine learning | |
dc.keywords | Alloy design | |
dc.keywords | Martensitic phase transformation | |
dc.language.iso | eng | |
dc.publisher | Elsevier Science Sa | |
dc.relation.ispartof | Journal of Alloys and Compounds | |
dc.subject | Chemistry | |
dc.subject | Physical | |
dc.subject | Materials science | |
dc.subject | Engineering | |
dc.subject | Metallurgy metallurgical engineering | |
dc.title | Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400? utilizing artificial intelligence | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Çatal, Aysel Aysu | |
local.contributor.kuauthor | Bedir, Elif | |
local.contributor.kuauthor | Canadinç, Demircan | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit2 | Department of Mechanical Engineering | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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