Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

Alexey Beskopylny , Alexandr Lyapin , Hubert Jerzy Anysz , Besarion Meskhi , Andrey Veremeenko , Andrey Mozgovoy

Abstract

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.
Author Alexey Beskopylny
Alexey Beskopylny,,
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, Alexandr Lyapin
Alexandr Lyapin,,
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, Hubert Jerzy Anysz (FCE / ICE)
Hubert Jerzy Anysz,,
- The Institute of Civil Engineering
, Besarion Meskhi
Besarion Meskhi,,
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, Andrey Veremeenko
Andrey Veremeenko,,
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, Andrey Mozgovoy
Andrey Mozgovoy,,
-
Journal seriesMaterials, ISSN 1996-1944
Issue year2020
Vol13
No11
Pages1-34
Publication size in sheets122.25
Article number2445
Keywords in Englishnon-destructive test; machine learning; clustering; steel; cone indentation; impact;artificial neural networks
ASJC Classification2500 General Materials Science
DOIDOI:10.3390/ma13112445
Languageen angielski
LicenseJournal (articles only); author's original; Uznanie Autorstwa (CC-BY); after publication
File
WUT57189746292145318c80797a13de5be1.pdf 4.74 MB
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 09-07-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.285; WoS Impact Factor: 2018 = 2.972 (2) - 2018=3.532 (5)
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