Yi Hu^1*, William Curtin^1*

1 Laboratory for Multiscale Mechanics Modeling (LAMMM), École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland

* Corresponding authors emails: y.hu@mpie.de, william.curtin@epfl.ch

DOI10.24435/materialscloud:9m-4n [version v1]

Publication date: Jan 30, 2023

How to cite this record

Yi Hu, William Curtin, Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys, Materials Cloud Archive 2023.18 (2023), doi: 10.24435/materialscloud:9m-4n.

Description

Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condition 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 approached 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 predicting the role of chemistry and microstructure on alloy strength that can be applied in many different alloys.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:7b83e87720e0d196dd36bece0e6e824e	946 Bytes	README
atom.tar.xz MD5md5:7bc55258ebae7c6c88e2016a5692a974	117.6 MiB	atomistic input files and results
dd.tar.xz MD5md5:c1ceee14989364743f730e28db7d5ed2	306.2 KiB	ParaDiS input files and results

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External references

Keywords

Al–Mg–Si alloy Neural network potential Precipitation strengthening Orowan mechanism Precipitate shearing mechanism Dislocation precipitate interaction Discrete dislocation dynamics MARVEL/DD2