Application of artificial neural networks in atomic force microscopy.
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| Title: | Application of artificial neural networks in atomic force microscopy. |
|---|---|
| Authors: | Sokolov, A. K.1 (AUTHOR) aleksandr_sokol@mail.ru, Garishin, O. K.1 (AUTHOR), Svistkov, A. L.1,2 (AUTHOR) |
| Source: | Mechanics of Advanced Materials & Structures. 2024, Vol. 31 Issue 26, p8388-8396. 9p. |
| Subjects: | Artificial neural networks, Numerical solutions to boundary value problems, Databases, Atomic force microscopy, Finite element method, Nanomechanics |
| Abstract: | The paper describes a method of artificial neural network application to build a computer database for numerical simulation of the process of indentation of an atomic force microscope probe into an elastomeric composite with a granular filler (nonlinear elastic medium with rigid spherical inclusions). The use of such a base makes it possible to significantly improve the speed and quality of interpretation of the results of nanoindentation for structurally inhomogeneous materials. In this case, information becomes available not only about what is done on the surface of the sample but also in the near-surface layer inside it. An algorithm has been developed with the help of which an artificial neural network was built and "trained," designed to obtain indentation curves depending on the size of the filler particles and its localization in the near-surface layer of the composite (depth and horizontal distance from the top of the AFM probe). It is shown that the speed of constructing indentation curves increases by several orders of magnitude compared to conventional approaches based on the numerical solution of the corresponding boundary value problems for each specific case. Accordingly, computer costs are also significantly reduced, that is, in the presence of an already built and "trained" neural network, powerful and high-speed computers are not needed. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanics of Advanced Materials & Structures is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 180993275 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of artificial neural networks in atomic force microscopy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sokolov%2C+A%2E+K%2E%22">Sokolov, A. K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aleksandr_sokol@mail.ru</i><br /><searchLink fieldCode="AR" term="%22Garishin%2C+O%2E+K%2E%22">Garishin, O. K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Svistkov%2C+A%2E+L%2E%22">Svistkov, A. L.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mechanics+of+Advanced+Materials+%26+Structures%22">Mechanics of Advanced Materials & Structures</searchLink>. 2024, Vol. 31 Issue 26, p8388-8396. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+solutions+to+boundary+value+problems%22">Numerical solutions to boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Atomic+force+microscopy%22">Atomic force microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Nanomechanics%22">Nanomechanics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper describes a method of artificial neural network application to build a computer database for numerical simulation of the process of indentation of an atomic force microscope probe into an elastomeric composite with a granular filler (nonlinear elastic medium with rigid spherical inclusions). The use of such a base makes it possible to significantly improve the speed and quality of interpretation of the results of nanoindentation for structurally inhomogeneous materials. In this case, information becomes available not only about what is done on the surface of the sample but also in the near-surface layer inside it. An algorithm has been developed with the help of which an artificial neural network was built and "trained," designed to obtain indentation curves depending on the size of the filler particles and its localization in the near-surface layer of the composite (depth and horizontal distance from the top of the AFM probe). It is shown that the speed of constructing indentation curves increases by several orders of magnitude compared to conventional approaches based on the numerical solution of the corresponding boundary value problems for each specific case. Accordingly, computer costs are also significantly reduced, that is, in the presence of an already built and "trained" neural network, powerful and high-speed computers are not needed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mechanics of Advanced Materials & Structures is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15376494.2023.2258516 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 8388 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Numerical solutions to boundary value problems Type: general – SubjectFull: Databases Type: general – SubjectFull: Atomic force microscopy Type: general – SubjectFull: Finite element method Type: general – SubjectFull: Nanomechanics Type: general Titles: – TitleFull: Application of artificial neural networks in atomic force microscopy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sokolov, A. K. – PersonEntity: Name: NameFull: Garishin, O. K. – PersonEntity: Name: NameFull: Svistkov, A. L. IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 12 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15376494 Numbering: – Type: volume Value: 31 – Type: issue Value: 26 Titles: – TitleFull: Mechanics of Advanced Materials & Structures Type: main |
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