Deep Learning-Based Decision Support System for Automatic Detection and Grading of Surface Corrosion on Galvanized Steel Sheets

• Published in: Electronics (Basel) Vol. 13; no. 20; p. 3998
• Main Authors: Erkınay Özdemir, Merve, Karakuş, Fuat
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Artificial neural networks; Automation; Corrosion; Corrosion prevention; Corrosion products; Corrosion rate; Corrosion tests; Damage detection; Datasets; Decision making; Decision support systems; Deep learning; Factories; Galvanized steel; Galvanized steels; Galvanizing; Iron and steel plants; Iron industry; Machine learning; Measuring instruments; Metal sheets; Metals; Network reliability; Neural networks; Plant layout; Production costs; Sheet-metal; Steel; Steel industry; Steel, Galvanized; United States > US
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13203998

Corrosion in the sheets produced leads to significant material losses, including the loss of resources, capital, labor, energy and knowledge. Corrosion control is significant for sheets produced and sent to customers in iron and steel factories. Surface corrosion testing of produced sheets and the accurate detection of corrosion levels are of great importance. The corrosion detection process for sheets in steel factories is performed visually with the naked eye. This is a subjective and time-consuming method. Identifying corrosion damage by visual detection and accurately determining the type and extent of corrosion requires expertise. Wrong decisions at this stage lead to losses during the production phase. Therefore, there is a need for systems that can automate this process and make it human-independent. In this study, a decision support system was designed to automatically detect the level of corrosion in galvanized sheets using convolutional neural networks. The average accuracy of the system is 97.5%, the average precision is 0.98, the average recall is 1 and the average F1 score is 0.99. The results we obtained indicate that a successful system has been developed for the detection and determination of corrosion levels. The high performance of the convolutional neural network models used for corrosion detection supports the practical applicability of the developed system. This system will increase the reliability and efficiency of industrial processes by enabling the accurate and automatic classification of corrosion. This system, which meets a significant need in this area for industrial organizations, reduces production costs and also makes the corrosion detection process more consistent and faster.