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    β'' needle-shape precipitate formation in Al-Mg-Si alloy: Phase field simulation and experimental verification

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    Type
    Article
    Authors
    Mao, Hong
    Kong, Yi cc
    Cai, Dan
    Yang, Mingjun
    Peng, Yingbiao
    Zeng, Yinping
    Zhang, Geng
    Shuai, Xiong
    Huang, Qi
    Li, Kai
    Zapolsky, Helena
    Du, Yong cc
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Date
    2020-06-20
    Online Publication Date
    2020-06-20
    Print Publication Date
    2020-11
    Embargo End Date
    2022-06-20
    Submitted Date
    2020-02-23
    Permanent link to this record
    http://hdl.handle.net/10754/663849
    
    Metadata
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    Abstract
    β'' needle-shape precipitate structures significantly affect the mechanical properties of 6xxx aluminum alloys. In this study, a modified multi-phase field (MPF) method combined with the thermodynamic and kinetic databases provided with the CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) method has been employed to investigate the morphological evolution of the monoclinic β'' precipitates in Al-Mg-Si alloy. Emphasis has been placed on understanding the influences of interfacial energy anisotropy and elastic interaction on the shape of β'' precipitates. It is shown that the high anisotropic strain alone cannot explain the needle shape of β'' precipitates. In the case of considering the contributions from both anisotropic interfacial energy and elastic interaction simultaneously, the simulations lead to the formation of unique micro-needle-like structures in age strengthening 6xxx Al alloy. The simulation results are compared with plate-shaped θ'(Al2Cu) precipitate in Al-Cu system, and verified with high-resolution transmission electron microscopy (HRTEM) images of needle-like β'' precipitates in Al-Mg-Si system.
    Citation
    Mao, H., Kong, Y., Cai, D., Yang, M., Peng, Y., Zeng, Y., … Du, Y. (2020). β’’ needle-shape precipitate formation in Al-Mg-Si alloy: Phase field simulation and experimental verification. Computational Materials Science, 184, 109878. doi:10.1016/j.commatsci.2020.109878
    Sponsors
    This study was funded by the National Natural Science Foundation of China (Grant Numbers: 51531009, 51771234) and National Key R&D Program of China (2018YFB0704003). Thanks to Guangxi Natural Science Foundation under Grant No.AD19245052&AD19110008. Hong Mao also thanks the project of the CSU Special Scholarship for Study Abroad to attend the exchange program. The data that support the findings of this study are available from the authors upon reasonable request.
    Publisher
    Elsevier BV
    Journal
    Computational Materials Science
    DOI
    10.1016/j.commatsci.2020.109878
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0927025620303694
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.commatsci.2020.109878
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division

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