Home > Publications database > Data-mining of in-situ TEM experiments: On the dynamics of dislocations in CoCrFeMnNi alloys > print

001     910448
005     20230517203031.0
024 7 _ |a 10.1016/j.actamat.2022.118394
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024 7 _ |a 1873-2453
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024 7 _ |a 2128/33777
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037 _ _ |a FZJ-2022-03837
082 _ _ |a 670
100 1 _ |a Zhang, Chen
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245 _ _ |a Data-mining of in-situ TEM experiments: On the dynamics of dislocations in CoCrFeMnNi alloys
260 _ _ |a Amsterdam [u.a.]
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520 _ _ |a High entropy alloys are a class of materials with many significant improvements in terms of mechanical properties as compared to “classical” alloys. The corresponding structure-property relations are not yet entirely clear, but it is commonly believed that the good mechanical performance is strongly related to dislocation interactions with the complex energy landscape formed due to alloying. Although in-situ Transmission Electron Microscopy (TEM) allows high-resolution studies of the structure and dynamics of moving dislocations and makes the local obstacle/energy “landscape” directly visible in the geometry of dislocations; such observation, however, are merely qualitative, and detailed three-dimensional analyses of the interaction between dislocations and the energy landscape is still missing. In this work, we utilized dislocations as “probes” for the local energy maxima which play the role of pinning points for the dislocation movement. To this end, we developed a unique data-mining approach that can perform coarse-grained spatio-temporal analysis, making ensemble averaging of a considerable number of snapshots possible. We investigate the effect of pinning points on the dislocation gliding behavior of CoCrFeMnNi alloy during in-situ TEM straining and find that (i) the pinning point strength changes when dislocations glide through and (ii) the pinning point moves along the direction close to the Burgers vector direction. Our data-mining method can be applied to dislocation motion in general, making it a useful tool for dislocation research.
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536 _ _ |a MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419)
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700 1 _ |a Song, Hengxu
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700 1 _ |a Oliveros, Daniela
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700 1 _ |a Fraczkiewicz, Anna
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700 1 _ |a Legros, Marc
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700 1 _ |a Sandfeld, Stefan
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773 _ _ |a 10.1016/j.actamat.2022.118394
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856 4 _ |u https://juser.fz-juelich.de/record/910448/files/1-s2.0-S1359645422007728-main.pdf
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