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Deep learning powered optical microscopy for steel research
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SYSNO ASEP 0568817 Document Type A - Abstract R&D Document Type The record was not marked in the RIV Title Deep learning powered optical microscopy for steel research Author(s) Mikmeková, Šárka (UPT-D) RID, SAI, ORCID
Čermák, Jan (UPT-D)
Ambrož, Ondřej (UPT-D) ORCID, RID, SAI
Jozefovič, Patrik (UPT-D)
Zouhar, Martin (UPT-D) ORCID, RID, SAI
Materna Mikmeková, Eliška (UPT-D) ORCID, RID, SAISource Title 16th Multinational Congress on Microscopy, 16MCM, 04-09 September 2022, Brno, Czech Republic. Book of abstracts. - Brno : Czechoslovak Microscopy Society, 2022 / Krzyžánek V. ; Hrubanová K. ; Hozák P. ; Müllerová I. ; Šlouf M. - ISBN 978-80-11-02253-2
S. 347-348Number of pages 2 s. Publication form Online - E Action Multinational Congress on Microscopy /16./ Event date 04.09.2022 - 09.09.2022 VEvent location Brno Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Institutional support UPT-D - RVO:68081731 Annotation Steel is by far the world´s most important, multi-functional, and most adaptable material. The excellent mechanical properties of steels are determined by their microstructure. The
microstructure of advanced steels, such as advanced high strength steels (AHSS), is a combination of different phases or constituents with complex substructures and its classification is extremely challenging. Traditionally used imaging techniques, such as light optical microscopy (LOM) or confocal microscopy (CM), turn out to be insufficient for precise structural characterization of the AHSS, and high-resolution imaging of their surface by state-of-the-art scanning electron microscopes (SEM) is required. However, common metallographic laboratories are equipped only with the LOM or the CM at best. As a result, they are not able to sufficiently characterize the structure of modern steels. In this work, we employ artificial intelligence techniques - namely deep learning - to enhance the OM micrographs of the AHSS in order to raise phase separation. Each training data-point consists of a trinity of corresponding micrographs obtained by the OM, the CM, and the ultra-high resolution SEM. We will demonstrate that the deep learning methods have the potential to enhance the contrast among
fine features in the OM micrographs and enable accurate secondary phase identification in the AHSS using LOM.Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2023
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