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  4. Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys
 
research article

Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys

Hu, Yi  
•
Curtin, W. A.  
September 15, 2022
Acta Materialia

Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condi-tion where the strength-limiting processes of precipitate shearing and Orowan looping are thought to be comparable. Qualitative models have long captured the basic mechanisms but realistic predictions have been challenging due to both the lack of accurate material parameters and an inability to quantitatively validate the models. Here, dislocation/precipitate interaction mechanisms are studied in Al-6xxx Al-Mg- Si alloys using atomistic simulations in tandem with a near-chemically-accurate Al-Mg-Si neural network interatomic potentials. Results show that a given precipitate can exhibit shearing or looping depending on the relative orientation of the precipitate and dislocation, as influenced by the matrix and precipitate coherency stresses, direction-dependence of precipitate shearing energies, and dislocation line tension. Analytic models for shearing and calibrated discrete dislocation models of looping accurately capture the trends and magnitudes of strengthening in most cases. Good agreement with experiments is then ap-proached by using the theories together with the more-accurate first-principles material properties. The combination of theories and simulations demonstrated here constitutes a path for understanding and pre-dicting the role of chemistry and microstructure on alloy strength that can be applied in many different alloys. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

  • Details
  • Metrics
Type
research article
DOI
10.1016/j.actamat.2022.118144
Web of Science ID

WOS:000830315300006

Author(s)
Hu, Yi  
Curtin, W. A.  
Date Issued

2022-09-15

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Published in
Acta Materialia
Volume

237

Article Number

118144

Subjects

Materials Science, Multidisciplinary

•

Metallurgy & Metallurgical Engineering

•

Materials Science

•

al-mg-si alloy

•

neural network potential

•

precipitation strengthening

•

orowan mechanism

•

precipitate shearing mechanism

•

dislocation precipitate interaction

•

discrete dislocation dynamics

•

hardening behavior

•

dislocation

•

phase

•

simulation

•

mechanism

•

dynamics

•

zones

•

slip

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LAMMM  
Available on Infoscience
August 15, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/189960
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