High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning

0574897 - ÚPT 2024 RIV CH eng J - Článek v odborném periodiku
Mikmeková, Šárka - Man, Jiří - Ambrož, Ondřej - Jozefovič, Patrik - Čermák, Jan - Järvenpää, A. - Jaskari, M. - Materna, J. - Kruml, Tomáš
High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning.
Metals. Roč. 13, č. 6 (2023), č. článku 1039. E-ISSN 2075-4701
Grant ostatní: AV ČR(CZ) StrategieAV21/5
Program: StrategieAV
Institucionální podpora: RVO:68081731 ; RVO:68081723
Klíčová slova: scanning electron microscopy (SEM) * austenitic stainless steel * low cycle fatigue * deformation induced martensite * deep learning
Obor OECD: Materials engineering; Materials engineering (UFM-A)
Impakt faktor: 2.9, rok: 2022
Způsob publikování: Open access
https://www.mdpi.com/2075-4701/13/6/1039

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.
Trvalý link: https://hdl.handle.net/11104/0344823