Improvements on the characterization of heterogeneities in grain size by network analysis
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2023-01-05
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MOREIRA, V. C.
TSCHIPTSCHIN, A. P.
Júlio Cesar Dutra
TSCHIPTSCHIN, A. P.
Júlio Cesar Dutra
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Materials Characterization
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MOREIRA, V. C.; TSCHIPTSCHIN, A. P.; DUTRA, J. C. Improvements on the characterization of heterogeneities in grain size by network analysis. Materials Characterization, v. 195, jan. 2023.
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© 2022Some microstructural heterogeneities, like those observed during abnormal grain growth or right after recrystallization of metals and alloys, may be challenging to evaluate in metric or topological terms, as the variations in both grain size and number of sides distributions are subtle. This work proposes a new methodology for microstructural heterogeneities characterization based on network analysis. The method involves coupling the eigenvector centrality and three newly conceived microstructural network centralities. These microstructural centralities take advantage of existing topological constraints during normal grain growth to overcome the dependency of traditional network centralities on the number of grains in micrographs. The proposed methodology was successfully tested for the characterization of abnormal grain growth and a pre-self-similar state during normal grain growth. This procedure has some advantages over grain size distribution and topological evaluations, and it can be automated for both industrial and research applications, paving the way for the characterization of other heterogeneities in materials microstructures.