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Fig. 109: Zoom into the early stages of recrystallisation via DFXM maps after 190s annealing at 610°C of the embedded GOI. a) Mosaicity and orientation distribution colour key. Some of the recrystallised grains are marked with red arrows in the local pole figure. b) Misorientation map emphasising the boundaries and cells (log scale). Yellow arrows show the nucleation sites and the red arrow marks the bulging of a new recrystallised grain into another deformed grain with a different orientation. c-f) DFXM in-situ monitoring of recrystallisation and grain growth in an embedded GOI during annealing at 610°C.
PRINCIPAL PUBLICATION AND AUTHORS
4D microstructural evolution in a heavily deformed ferritic alloy: a new perspective in recrystallisation studies, C. Yildirim (a), N. Mavrikakis (b, c), P.K. Cook (a), R. Rodriguez-Lamas (a), M. Kutsal (a,d), H.F. Poulsen (d), C. Detlefs (a), Scr. Mater. 214, 114689 (2022); https:/doi.org/10.1016/j.scriptamat.2022.114689 (a) ESRF (b) Aix-Marseille University, Institut Matériaux Microélectronique Nanosciences de Provence-IM2NP, UMR (France) (c) ArcelorMittal Global R&D Gent, Zelzate (Belgium) (d) Department of Physics, Technical University of Denmark, Lyngby (Denmark)
REFERENCES
[1] C. Yildirim et al., MRS Bulletin 45, 4 (2020).
larger intensity and significantly lower orientation spread. Additional recrystallised grains are observed in the close vicinity of the parent grain boundary, while the orientation spread is not directly correlated to the distance from the parent grain.
In summary, by means of DFXM, an extensive 3D movie has been made of the nucleation and relation of the
growth of recrystallised grains with the surrounding matrix in a heavily deformed industrial alloy. DFXM proves a significant tool for capturing both the structural and the dynamic evolution of recrystallisation in-situ and in real-time. This opens up new avenues in understanding that have industrial significance for the control of material microstructure in order to tailor properties, and can also be used as input for computational growth models.
