TY - JOUR

T1 - Overview of application of automated SEM/EDS measurements for inclusion characterization in steelmaking

AU - Ramesh Babu, Shashank

AU - Michelic, Susanne Katharina

PY - 2024/4/10

Y1 - 2024/4/10

N2 - The scanning electron microscope equipped with an energy dispersive spectroscopy (SEM/EDS) is considered as a state-of-the-art characterization tool to determine the morphological features and the chemical composition of non-metallic inclusions in steel. Such a characterization is pivotal to assessing the steel quality, which influences the properties of end products. This paper offers a comprehensive review of the SEM/EDS system, tracing its historical developments and methodological advancements by various research groups which have contributed to non-metallic inclusion analysis. Then the discussions transition to developments that have matured the SEM/EDS platform. The paper highlights selected examples utilizing the SEM/EDS to examine inclusions across various steel grades and at different stages of the metallurgical process. Finally, latest advancements in integrating machine learning techniques to expedite the analysis process were discussed.

AB - The scanning electron microscope equipped with an energy dispersive spectroscopy (SEM/EDS) is considered as a state-of-the-art characterization tool to determine the morphological features and the chemical composition of non-metallic inclusions in steel. Such a characterization is pivotal to assessing the steel quality, which influences the properties of end products. This paper offers a comprehensive review of the SEM/EDS system, tracing its historical developments and methodological advancements by various research groups which have contributed to non-metallic inclusion analysis. Then the discussions transition to developments that have matured the SEM/EDS platform. The paper highlights selected examples utilizing the SEM/EDS to examine inclusions across various steel grades and at different stages of the metallurgical process. Finally, latest advancements in integrating machine learning techniques to expedite the analysis process were discussed.

KW - SEM

KW - non-metallic inclusions

KW - steel

KW - review

U2 - 10.1002/metm.18

DO - 10.1002/metm.18

M3 - Article

VL - 2024

JO - MetalMat (e-only)

JF - MetalMat (e-only)

SN - 2940-1402

IS - e18

M1 - e18

ER -