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Title

Analysis of the Possibility of Using Selected Artificial Intelligence Algorithms for the Assessment of the Microstructure of Vermicular Cast Iron

Journal title

Archives of Foundry Engineering

Yearbook

2025

Volume

vol. 25

Issue

No 2

Authors

Janiszewska, U. ; Marcjan, Ł. ; Gajoch, S. ; Jaśkowiec, K. ; Bitka, A. ; Małysza, M. ; Wilk-Kołodziejczyk, D.

Affiliation

Janiszewska, U. : AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, Poland ; Marcjan, Ł. : AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, Poland ; Gajoch, S. : AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, Poland ; Jaśkowiec, K. : Łukasiewicz Research Network-Krakow Institute of Technology, Poland ; Bitka, A. : Łukasiewicz Research Network-Krakow Institute of Technology, Poland ; Małysza, M. : AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, Poland ; Wilk-Kołodziejczyk, D. : AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, Poland ; Wilk-Kołodziejczyk, D. : Łukasiewicz Research Network-Krakow Institute of Technology, Poland

Keywords

Microstructure images ; Vermicular cast iron ; Artificial intelligence HOG algorithm

Divisions of PAS

Nauki Techniczne

Coverage

173-182

Publisher

The Katowice Branch of the Polish Academy of Sciences

Bibliography

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  • Fotos, G., Campbell, A., Murray, P. & Yakushina, E. (2023). Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentation. Journal of Materials Science. 58(36), 14390-14410. https://doi.org/10.1007/s10853-023-08901-w.
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  • Zhu, C. & Zhang, A. (2007). Experimental study on fracture toughness of vermicular cast iron. Journal of Mechanical Strength. 29(2), 310-314.
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Date

27.06.2025

Type

Article

Identifier

DOI: 10.24425/afe.2025.153807
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