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Arabian Journal for Science and Engineering

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17.07.2021 | Research Article-Mechanical Engineering

Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach

verfasst von: Tzu-Chia Chen, Rajiman Rajiman, Marischa Elveny, John William Grimaldo Guerrero, Adedoyin Isola Lawal, Ngakan Ketut Acwin Dwijendra, Aravindhan Surendar, Svetlana Dmitrievna Danshina, Yu Zhu

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 12/2021

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Abstract

A 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.

Vorheriger Artikel Boundary layer flow of magneto-nanomicropolar liquid over an exponentially elongated porous plate with Joule heating and viscous heating: a numerical study

Nächster Artikel Impact of Inclined Lorentz Force and Schmidt Number on Chemically Reactive Newtonian Fluid Flow on a Stretchable Surface When Stefan Blowing and Thermal Radiation are Significant

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Titel: Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach
verfasst von: Tzu-Chia Chen
Rajiman Rajiman
Marischa Elveny
John William Grimaldo Guerrero
Adedoyin Isola Lawal
Ngakan Ketut Acwin Dwijendra
Aravindhan Surendar
Svetlana Dmitrievna Danshina
Yu Zhu
Publikationsdatum: 17.07.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 12/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05966-0

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