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
AbstractAssessment 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.
|Journal series||Materials, ISSN 1996-1944|
|Publication size in sheets||122.25|
|Keywords in English||non-destructive test; machine learning; clustering; steel; cone indentation; impact;artificial neural networks|
|License||Journal (articles only); author's original; ; after publication|
|Score||= 140.0, 09-07-2020, ArticleFromJournal|
|Publication indicators||: 2017 = 1.285; : 2018 = 2.972 (2) - 2018=3.532 (5)|
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