Počet záznamů: 1
High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning
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SYSNO ASEP 0574897 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning Tvůrce(i) Mikmeková, Šárka (UPT-D) RID, SAI, ORCID
Man, Jiří (UFM-A) RID, ORCID
Ambrož, Ondřej (UPT-D) ORCID, RID, SAI
Jozefovič, Patrik (UPT-D)
Čermák, Jan (UPT-D)
Järvenpää, A. (FI)
Jaskari, M. (FI)
Materna, J. (CZ)
Kruml, Tomáš (UFM-A) RID, ORCIDCelkový počet autorů 9 Číslo článku 1039 Zdroj.dok. Metals. - : MDPI
Roč. 13, č. 6 (2023)Poč.str. 13 s. Forma vydání Online - E Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova scanning electron microscopy (SEM) ; austenitic stainless steel ; low cycle fatigue ; deformation induced martensite ; deep learning Vědní obor RIV JG - Hutnictví, kovové materiály Obor OECD Materials engineering Vědní obor RIV – spolupráce Ústav fyziky materiálu - Hutnictví, kovové materiály Způsob publikování Open access Institucionální podpora UPT-D - RVO:68081731 ; UFM-A - RVO:68081723 UT WOS 001015296500001 EID SCOPUS 85163816053 DOI 10.3390/met13061039 Anotace This paper aims to demonstrate a novel technique enabling the accurate visualization and fast mapping of deformation-induced α′-martensite produced during cyclic straining of a metastable austenitic stainless steel, refined by reversion annealing to different grain sizes. The technique is based on energy and angular separation of the signal electrons in a scanning electron microscope (SEM). Collection of the inelastic backscattered electrons emitted under high take-off angles from a sample surface results in the acquisition of micrographs with high sensitivity to structural defects, such as dislocations, grain boundaries, and other imperfections. The areas with a high density of lattice imperfections reduce the penetration depth of the primary electrons, and simultaneously affect the signal electrons leaving the specimen. This results in an increase in the inelastic backscattered electrons yielded from the vicinity of α′-martensite, and a bright halo surrounds this phase. The α′-martensite phase can thus be separated from the austenitic matrix in SEM micrographs. In this work, we propose a deep learning method for a precise α′-martensite mapping within a large area. Various deep learning-based methods have been tested, and the best result measured by both Dice loss and IoU scores has been achieved using the U-Net architecture extended by the ResNet encoder. Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2024 Elektronická adresa https://www.mdpi.com/2075-4701/13/6/1039
Počet záznamů: 1