Problems of Surface Defectoscopy of Metals using Machine Learning and Ways for Their Solutions
- 作者: Rybakov K.M.1, Khamitov R.M.1
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隶属关系:
- Kazan State Power Engineering University
- 期: 卷 14, 编号 1 (2024)
- 页面: 196-204
- 栏目: Articles
- ##submission.datePublished##: 31.03.2024
- URL: https://journals.rcsi.science/2328-1391/article/view/299572
- DOI: https://doi.org/10.12731/2227-930X-2024-14-1-289
- EDN: https://elibrary.ru/HGYQAY
- ID: 299572
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详细
Rejection of metal products is an important stage of the production process aimed at ensuring the best quality of the final product. Traditional rejection methods, based on visual inspection or the use of simple automated systems, have their limitations and disadvantages, such as low speed and accuracy of defect classification. The paper examines the possibility of using various machine learning methods to classify defects in metal products. A comparative analysis of these algorithms, as well as their effectiveness, is carried out in order to determine the most suitable approach to the automatic rejection of metal products.
作者简介
Kirill Rybakov
Kazan State Power Engineering University
编辑信件的主要联系方式.
Email: kotya.ribak@mail.ru
ORCID iD: 0009-0005-3781-5259
2nd year master's student of the Department of Information Technologies and Intelligent Systems
俄罗斯联邦, 51, Krasnoselskaya Str., Kazan, 420066, Russian FederationRenat Khamitov
Kazan State Power Engineering University
Email: hamitov@gmail.com
ORCID iD: 0000-0002-9949-4404
Associate Professor of the Department of Information Technologies and Intelligent Systems, Candidate of Technical Sciences
俄罗斯联邦, 51, Krasnoselskaya Str., Kazan, 420066, Russian Federation参考
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