The Influence of Sample Preparation on Correlative Microscopy with the Use of Artificial Intelligence Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00647110" target="_blank" >RIV/68081731:_____/25:00647110 - isvavai.cz</a>
Result on the web
<a href="https://www.scientific.net/KEM.1034.127" target="_blank" >https://www.scientific.net/KEM.1034.127</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4028/p-FPNub8" target="_blank" >10.4028/p-FPNub8</a>
Alternative languages
Result language
angličtina
Original language name
The Influence of Sample Preparation on Correlative Microscopy with the Use of Artificial Intelligence Methods
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
20501 - Materials engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Key Engineering Materials. Volume 1034, Advanced Research, Technologies and Development in Materials Science
ISBN
978-3-0364-0954-2
Number of pages of the result
5
Pages from-to
"Roč. 1034 (2025)"
Number of pages of the book
144
Publisher name
Trans Tech Publications
Place of publication
Baech
UT code for WoS chapter
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