Surface Corrosion Detection for Ferrous-metal Parts: Application of Artificial Intelligence, Python and Microscopic Images.
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| Title: | Surface Corrosion Detection for Ferrous-metal Parts: Application of Artificial Intelligence, Python and Microscopic Images. |
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| Authors: | WDOWIK, Roman, BEŁZO, Artur, BENDIKIENE, Regita |
| Source: | Materials Science / Medziagotyra; 2025, Vol. 31 Issue 3, p431-438, 8p |
| Subject Terms: | ARTIFICIAL intelligence, PYTHON programming language, CORROSION engineering, PRODUCT quality, MICROSCOPY, STEEL corrosion, IRON metallurgy, CONDITION-based maintenance |
| Abstract: | This paper presents a novel method for the identification of surface damage, in particular corrosion, in ferrous metals based on generative artificial intelligence (GenAI), showing how to automate damage identification and corrosion recognition. The methodology involved using optical microscopy to capture electrochemical corrosion patterns, followed by image preprocessing and classification using AI algorithms implemented in Python. High-quality microscopic images have been recorded, based on selected ferrous metals. Python code lines were generated using ChatGPTTM based on queries created by the authors, and this method was applied to the corrosion analysis. Quantitative evaluation confirmed Python code parameters-dependent detection accuracy and repeatability, demonstrating the robustness of the proposed technique. The results were discussed in terms of possible industrial applications. In addition, the limitations of the results obtained, which sometimes fall short of the claims inspector's expectations, were discussed. Compared to traditional corrosion detection methods such as visual inspection and non-destructive testing, AI-based methods are a faster and more cost-effective solution that can process large volumes of images in real time and produce consistent results. Further research directions are also suggested, including the analysis of other types of damage and improving the accuracy of the model. In addition to technical efficiencies, the broader impact of these studies is that they can contribute to predictive maintenance, reduce downtime and improve safety in industries with high ferrous metal use. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials Science / Medziagotyra is the property of Kaunas University of Technology, represented by Prof. Rymantas Jonas Kazys and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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