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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformatio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321333/ https://www.ncbi.nlm.nih.gov/pubmed/32471095 http://dx.doi.org/10.3390/ma13112445 |
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author | Beskopylny, Alexey Lyapin, Alexandr Anysz, Hubert Meskhi, Besarion Veremeenko, Andrey Mozgovoy, Andrey |
author_facet | Beskopylny, Alexey Lyapin, Alexandr Anysz, Hubert Meskhi, Besarion Veremeenko, Andrey Mozgovoy, Andrey |
author_sort | Beskopylny, Alexey |
collection | PubMed |
description | Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures—often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy—over 95%—to attribute the results to the corresponding steel grade. |
format | Online Article Text |
id | pubmed-7321333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73213332020-06-29 Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests Beskopylny, Alexey Lyapin, Alexandr Anysz, Hubert Meskhi, Besarion Veremeenko, Andrey Mozgovoy, Andrey Materials (Basel) Article Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures—often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy—over 95%—to attribute the results to the corresponding steel grade. MDPI 2020-05-27 /pmc/articles/PMC7321333/ /pubmed/32471095 http://dx.doi.org/10.3390/ma13112445 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Beskopylny, Alexey Lyapin, Alexandr Anysz, Hubert Meskhi, Besarion Veremeenko, Andrey Mozgovoy, Andrey Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title | Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title_full | Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title_fullStr | Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title_full_unstemmed | Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title_short | Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests |
title_sort | artificial neural networks in classification of steel grades based on non-destructive tests |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321333/ https://www.ncbi.nlm.nih.gov/pubmed/32471095 http://dx.doi.org/10.3390/ma13112445 |
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