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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/13/11/2445 |
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| author | Alexey Beskopylny Alexandr Lyapin Hubert Anysz Besarion Meskhi Andrey Veremeenko Andrey Mozgovoy |
| author_facet | Alexey Beskopylny Alexandr Lyapin Hubert Anysz Besarion Meskhi Andrey Veremeenko Andrey Mozgovoy |
| author_sort | Alexey Beskopylny |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T19:33:58Z |
| format | Article |
| id | doaj.art-b82e57398442413c87cfdb83e03bcdb3 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T19:33:58Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-b82e57398442413c87cfdb83e03bcdb32023-11-20T01:53:16ZengMDPI AGMaterials1996-19442020-05-011311244510.3390/ma13112445Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive TestsAlexey Beskopylny0Alexandr Lyapin1Hubert Anysz2Besarion Meskhi3Andrey Veremeenko4Andrey Mozgovoy5Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, RussiaDepartment of Information Systems in Construction, Faculty of IT-systems and Technologies, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, RussiaFaculty of Civil Engineering, Warsaw University of Technology, Al. Armii Ludowej 16, 00-637 Warsaw, PolandDepartment of Life Safety and Environmental Protection, Faculty of Life Safety and Environmental Engineering, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, RussiaDepartment of Motor Roads, Faculty of Roads and Transport Systems, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, RussiaDepartment of Life Safety and Environmental Protection, Faculty of Life Safety and Environmental Engineering, Don State Technical University, Gagarin, 1, 344000 Rostov-on-Don, RussiaAssessment 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.https://www.mdpi.com/1996-1944/13/11/2445non-destructive testmachine learningclusteringsteelcone indentationimpact |
| spellingShingle | Alexey Beskopylny Alexandr Lyapin Hubert Anysz Besarion Meskhi Andrey Veremeenko Andrey Mozgovoy Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests Materials non-destructive test machine learning clustering steel cone indentation impact |
| 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 | non-destructive test machine learning clustering steel cone indentation impact |
| url | https://www.mdpi.com/1996-1944/13/11/2445 |
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