LAPSE:2023.17335
Published Article
LAPSE:2023.17335
Application of Artificial Intelligence Technologies to Assess the Quality of Structures
Anton Zhilenkov, Sergei Chernyi, Vitalii Emelianov
March 6, 2023
The timeliness of the complex automated diagnostics of the metal condition for all characteristics has been substantiated. An algorithm for the automation of metallographic quality control of metals is proposed and described. It is based on the use of neural networks for recognizing images of metal microstructures and a precedent method for determining the metal grade. An approach to preliminarily process the images of metal microstructures is described. The structure of a neural network has been developed to determine the quantitative characteristics of metals. The results of the functioning of neural networks for determining the quantitative characteristics of metals are presented. The high accuracy of determining the characteristics of metals using neural networks is shown. Software has been developed for the automated recognition of images of metal microstructures, and for the determination of the metal grade. Comparative results of carrying out metallographic analysis with the developed tools are demonstrated. As a result, there is a significant reduction in the time required for analyzing metallographic images, as well as an increase in the accuracy of determining the quantitative characteristics of metals. The study of this problem is important not only in the metallurgical industry, but also in production, the maritime industry, and other engineering fields.
Keywords
intelligent system, metallographic analysis, neural networks, precedents method, software
Suggested Citation
Zhilenkov A, Chernyi S, Emelianov V. Application of Artificial Intelligence Technologies to Assess the Quality of Structures. (2023). LAPSE:2023.17335
Author Affiliations
Zhilenkov A: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, Russia [ORCID]
Chernyi S: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, Russia; Complex Information Security Department, Admiral Makarov State University of Maritime and Inland Shipping, 198035 Saint-Petersburg, Ru [ORCID]
Emelianov V: Financial University under the Government of the Russian Federation, 49 Leningradsky Prospekt, 125993 Moscow, Russia
Journal Name
Energies
Volume
14
Issue
23
First Page
8040
Year
2021
Publication Date
2021-12-01
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14238040, Publication Type: Journal Article
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doi:10.3390/en14238040
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