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The Influence of Sample Preparation on Correlative Microscopy with the Use of Artificial Intelligence Methods

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Abstract:

The preparation of metallographic samples remains a crucial aspect of microstructural analysis, especially with the continuous development of advanced materials and imaging techniques. Despite its significance, sample preparation is often underestimated, yet achieving a surface with minimal structural distortion is essential for accurate microstructure evaluation and data interpretation. This study aimed to optimize steel sample preparation methods to obtain surfaces suitable for correlative imaging using multiple microscopic techniques, including modern scanning electron microscopy (SEM) with sample bias and electron backscatter diffraction (EBSD). The results demonstrate that specific contrast features observed in SEM can, in some cases, be qualitatively verified using EBSD. Furthermore, variations in SEM settings, such as lower landing energy, influence information depth, which in turn affects the accuracy of phase quantification, particularly when utilizing artificial intelligence-based methods.

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Periodical:

Key Engineering Materials (Volume 1034)

Pages:

127-131

DOI:

https://doi.org/10.4028/p-FPNub8

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Cite this paper

Online since:

December 2025

Authors:

Ondřej Ambrož*, Jan Čermák, Šárka Mikmeková

Keywords:

Artificial Intelligence, Correlative Microscopy, Metallography, Sample Preparation

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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