Mikmeková Š, Man J, Ambrož O, Jozefovič P, Čermák J, Järvenpää A, Jaskari M, Materna J, Kruml T. High-Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning. Metals. 2023; 13(6):1039. https://doi.org/10.3390/met13061039
High-resolution characterization of deformation induced martensite in large areas of fatigued austenitic stainless steel using deep learning
|Author:||Mikmeková, Šárka1; Man, Jiří2; Ambrož, Ondřej1;|
1Institute of Scientific Instruments, Czech Academy of Sciences, Královopolská 147, 612 00 Brno, Czech Republic
2Institute of Physics of Materials, Czech Academy of Sciences, Žižkova 22, 616 62 Brno, Czech Republic
3Kerttu Saalasti Institute, University of Oulu, 85500 Nivala, Finland
4Machine Learning College, Chrlická 787/56, 620 00 Brno, Czech Republic
|Online Access:||PDF Full Text (PDF, 10.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230920133943
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2023-09-20
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
222 Other engineering and technologies
This research was funded by the Czech Academy of Sciences, Strategy AV21: research program “New Materials Based on Metals, Ceramics and Composites”.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).