KFU. Card publication. Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

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NEURAL NETWORK AS A TOOL FOR DESIGN OF AMORPHOUS METAL ALLOYS WITH DESIRED ELASTOPLASTIC PROPERTIES

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

Mokshin Anatoliy Vasilevich, author

Bibliographic description in the original language

Galimzyanov B.N, Doronina M.A, Mokshin A.V., Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties // Metals. - 2023. - Vol.13, Is.4. - Art. �812.

Annotation

The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young's modulus E and the yield strength sy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young's modulus E and the yield strength sy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo andWformed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and sy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).

Keywords

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The name of the journal

Metals

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156115563&doi=10.3390%2fmet13040812&partnerID=40&md5=0774fb13ec2526e03b19c62d79bbbb3e

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

https://repository.kpfu.ru/eng/?p_id=281455&p_lang=2

Resource files

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F_Metals__Neural_Network_.pdf	1,23	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Galimzyanov Bulat Nailevich	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, Doronina M.A, Mokshin A.V., Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties // Metals. - 2023. - Vol.13, Is.4. - Art. �812.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=281455&p_lang=2	ru_RU
dc.description.abstract	Metals	ru_RU
dc.description.abstract	The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young's modulus E and the yield strength sy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young's modulus E and the yield strength sy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo andWformed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and sy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	��	ru_RU
dc.subject	��	ru_RU
dc.subject	��	ru_RU
dc.title	Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties	ru_RU
dc.type	Articles in international journals and collections	ru_RU