Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach

dc.creatorTzu-Chia, Chen
dc.creatorRajiman, Rajiman
dc.creatorElveny, Marischa
dc.creatorGrimaldo Guerrero, John William
dc.creatorLawal, Adedoyin Isola
dc.creatorAcwin Dwijendra, Ngakan Ketut
dc.creatoraravindhan, surendar
dc.creatorDanshina, Svetlana
dc.creatorZHU, Yu
dc.date2021-09-06T21:20:14Z
dc.date2021-09-06T21:20:14Z
dc.date2021-07-07
dc.date2022-07-07
dc.date.accessioned2023-10-03T20:04:18Z
dc.date.available2023-10-03T20:04:18Z
dc.identifier2193567X
dc.identifierhttps://hdl.handle.net/11323/8642
dc.identifierhttps://doi.org/10.1007/s13369-021-05966-0
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174197
dc.descriptionA broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe–B–Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe–B–Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs. © 2021, King Fahd University of Petroleum & Minerals.
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dc.formatapplication/pdf
dc.languagespa
dc.publisherArabian Journal for Science and Engineering
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourceArabian Journal for Science and Engineering
dc.sourcehttps://link.springer.com/article/10.1007%2Fs13369-021-05966-0
dc.subjectBulk metallic glass
dc.subjectGlass-forming ability
dc.subjectMachine learning
dc.subjectMaterials design
dc.titleEngineering of novel fe-based bulk metallic glasses using a machine learning-based approach
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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