The Paton Welding Journal, 2024, №08



2024 №08 (04)

DOI of Article 10.37434/tpwj2024.08.05

2024 №08 (06)

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The Paton Welding Journal, 2024, #8, 36-44 pages

Methods for recognizing surface defects on thin-sheet materials for visual testing automation (Review)

A.S. Novodranov

E.O. Paton Electric Welding Institute of the NASU. 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: artur19940731@gmail.com

Abstract
The use of methods for recognizing surface defects in order to automate the process of visual non-destructive testing in production of rolled thin-sheet materials is becoming an increasingly urgent task. The use of automated systems for recognizing surface defects leads to early detection of damage and determination of their class and level of danger. After classifying a defect, the system makes a decision on further actions without the operator participation. The presence of such systems prevents the equipment downtime and reduces the impact of the human factor on production. The classifier performance rates were determined and analysis of the current techniques for determining surface defects was performed. The advantages and disadvantages of the methods are determined. The feasibility of using a method was analyzed depending on the type of surface and geometric characteristics of a defect. The expediency of using several methods to ensure more accurate recognition of surface defects is determined. Significant prospects for the application of machine learning methods based on neural networks are noted. The prospect of using neural networks in the systems for automated recognition of surface defects is predetermined by the possibility of automatic selection of features from the image, as well as processing of complex structures.
Keywords:surface defects, defect detection methods, sheet materials, automated monitoring, defect recognition, image processing

Received: 26.03.2024
Received in revised form: 30.05.2024
Accepted: 22.08.2024

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Suggested Citation

A.S. Novodranov (2024) Methods for recognizing surface defects on thin-sheet materials for visual testing automation (Review). The Paton Welding J., 08, 36-44.

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