Method of constructing artifi cial neural networks for the identifi cation of special purpose structures by classifi cation and country of origin based on electron microscopic analysis of their fragments
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- Abstract
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The article describes a methodology that employs visual analysis of metal microstructures based on AI for their efficient classification. This approach is grounded on a dataset composed of microscopic images depicting various metal classes, each showcasing distinct visual features such as grain size, shape, phase distribution, inclusions, and signs of mechanical processing. The use of convolutional neural networks (CNNs) for analyzing these features has significantly improved the identification and classification process, demonstrating the potential of AI in materials science.
Experimental results have confirmed the effectiveness of using CNNs to accurately distinguish between two and three metal classes. One notable observation was the positive impact of increasing image sizes on the classification process. This fact suggests that higher-quality images can mitigate the limitations of a small dataset size, thereby enriching the analysis.
In addition to convolutional neural networks, the paper describes the features of involving the large language model GPT-4v in the classification of metals and demonstrates its effectiveness.
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- 2024-03-31
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Copyright (c) 2024 Ігор Чепков,Вадим Слюсар,Андрій Кучинський

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