KFU. Card publication. Machine learning-based prediction of elastic properties of amorphous metal alloys

KAZAN
FEDERAL UNIVERSITY

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MACHINE LEARNING-BASED PREDICTION OF ELASTIC PROPERTIES OF AMORPHOUS METAL ALLOYS

Form of presentation

Articles in international journals and collections

Year of publication

2023

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Authors, employees of KFU

Galimzyanov Bulat Nailevich, author

Doronina Mariya Alekseevna, author

Mokshin Anatoliy Vasilevich, author

Bibliographic description in the original language

Galimzyanov B.N., Machine learning-based prediction of elastic properties of amorphous metal alloys / B.N. Galimzyanov, M.A. Doronina, A.V. Mokshin // Physica A: Statistical Mechanics and its Applications. - 2023. - V. 617. - P. 128678 1-7.

Annotation

Physica A: Statistical Mechanics and its Applications

Keywords

Machine learning, Neural network. Regression analysis, Alloys, Metallic glasses, Mechanical properties

The name of the journal

Physica A: Statistical Mechanics and its Applications

URL

https://doi.org/10.1016/j.physa.2023.128678

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=278020&p_lang=2

Resource files

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F_Physica_A__2023.pdf	1,03	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Galimzyanov Bulat Nailevich	ru_RU
dc.contributor.author	Doronina Mariya Alekseevna	ru_RU
dc.contributor.author	Mokshin Anatoliy Vasilevich	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Galimzyanov B.N., Machine learning-based prediction of elastic properties of amorphous metal alloys / B.N. Galimzyanov, M.A. Doronina, A.V. Mokshin // Physica A: Statistical Mechanics and its Applications. - 2023. - V. 617. - P. 128678 1-7.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=278020&p_lang=2	ru_RU
dc.description.abstract	Physica A: Statistical Mechanics and its Applications	ru_RU
dc.description.abstract	The Young's modulus E is the key mechanical property that determines the resistance of solids to tension/compression. In the present work, the correlation of the quantity E with such characteristics as the total molar mass M of alloy components, the number of components n forming an alloy, the yield stress sigma_y and the glass transition temperature Tg has been studied in detail based on a large set of empirical data for the Young's modulus of different amorphous metal alloys. It has been established that the values of the Young's modulus of metal alloys under normal conditions correlate with such a mechanical characteristic as the yield stress as well as with the glass transition temperature. As found, the specificity of the ''chemical formula'' of alloy, which is determined by molar mass M and number of components n, does not affect on elasticity of the material. The machine learning algorithm identified both the quantities M and n as insignificant factors in determining E. A simple non-linear regression model is obtained that relates the Young's modulus with Tg and sigma_y, and this model correctly reproduces the experimental data for metal alloys of different types. This obtained regression model generalizes the previously presented empirical relation for amorphous metal alloys.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Machine learning	ru_RU
dc.subject	Neural network. Regression analysis	ru_RU
dc.subject	Alloys	ru_RU
dc.subject	Metallic glasses	ru_RU
dc.subject	Mechanical properties	ru_RU
dc.title	Machine learning-based prediction of elastic properties of amorphous metal alloys	ru_RU
dc.type	Articles in international journals and collections	ru_RU