Journal Article

FZJ-2022-04283

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Data-mining of in-situ TEM experiments: Towards understanding nanoscale fracture

Steinberger, D. ; Issa, I. ; Strobl, R.FZJ* ; Imrich, P. J. ; Kiener, D. (Corresponding author) ; Sandfeld, S. (Corresponding author)FZJ*

2023
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33773  doi:10.1016/j.commatsci.2022.111830

Abstract: The lifetime and performance of any engineering component, from nanoscale sensors to macroscopic structures, are strongly influenced by fracture processes. Fracture itself is a highly localized event; originating at the atomic scale by bond breaking between individual atoms close to the crack tip. These processes, however, interact with defects such as dislocations or grain boundaries and influence phenomena on much larger length scales, ultimately giving rise to macroscopic behavior and engineering-scale fracture properties. This complex interplay is the fundamental reason why identifying the atomistic structural and energetic processes occurring at a crack tip remains a longstanding and still unsolved challenge.We develop a new analysis approach for combining quantitative in-situ observations of nanoscale deformation processes at a crack tip with three-dimensional reconstruction of the dislocation structure and advanced computational analysis to address plasticity and fracture initiation in a ductile metal. Our combinatorial approach reveals details of dislocation nucleation, their interaction process, and the local internal stress state, all of which were previously inaccessible to experiments. This enables us to describe fracture processes based on local crack driving forces on a dislocation level with a high fidelity that paves the way towards a better understanding and control of local failure processes in materials.

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Contributing Institute(s):

1. Materials Data Science and Informatics (IAS-9)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419) (759419)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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