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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Key Engineering Materials. Volume 1034, Advanced Research, Technologies and Development in Materials Science

ISBN

978-3-0364-0954-2

Počet stran výsledku

5

Strana od-do

"Roč. 1034 (2025)"

Počet stran knihy

144

Název nakladatele

Trans Tech Publications

Místo vydání

Baech

Kód UT WoS kapitoly

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