Theoretical Model of Structural Phase Transitions in Al-Cu Solid Solutions under Dynamic Loading Using Machine Learning

被引:1
作者
Grachyova, Natalya [1 ]
Fomin, Eugenii [1 ]
Mayer, Alexander [1 ]
机构
[1] Chelyabinsk State Univ, Dept Gen & Theoret Phys, Chelyabinsk 454001, Russia
来源
DYNAMICS | 2024年 / 4卷 / 03期
基金
俄罗斯科学基金会;
关键词
dynamic loading; Al-Cu solid solution; dislocation plasticity; phase transformation; molecular dynamics; plasticity model; artificial neural network; Bayesian calibration; equation of state; MECHANICAL THRESHOLD STRESS; TEMPERATURE ELASTIC-MODULI; CONSTITUTIVE MODEL; CRYSTAL PLASTICITY; NEURAL-NETWORKS; STRAIN RATES; DEFORMATION; DISLOCATION; METALS; CONSTANTS;
D O I
10.3390/dynamics4030028
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The development of dynamic plasticity models with accounting of interplay between several plasticity mechanisms is an urgent problem for the theoretical description of the complex dynamic loading of materials. Here, we consider dynamic plastic relaxation by means of the combined action of dislocations and phase transitions using Al-Cu solid solutions as the model materials and uniaxial compression as the model loading. We propose a simple and robust theoretical model combining molecular dynamics (MD) data, theoretical framework and machine learning (ML) methods. MD simulations of uniaxial compression of Al, Cu and Al-Cu solid solutions reveal a relaxation of shear stresses due to a combination of dislocation plasticity and phase transformations with a complete suppression of the dislocation activity for Cu concentrations in the range of 30-80%. In particular, pure Al reveals an almost complete phase transition from the FCC (face-centered cubic) to the BCC (body-centered cubic) structure at a pressure of about 36 GPa, while pure copper does not reveal it at least till 110 GPa. A theoretical model of stress relaxation is developed, taking into account the dislocation activity and phase transformations, and is applied for the description of the MD results of an Al-Cu solid solution. Arrhenius-type equations are employed to describe the rates of phase transformation. The Bayesian method is applied to identify the model parameters with fitting to MD results as the reference data. Two forward-propagation artificial neural networks (ANNs) trained by MD data for uniaxial compression and tension are used to approximate the single-valued functions being parts of constitutive relation, such as the equation of state (EOS), elastic (shear and bulk) moduli and the nucleation strain distance function describing dislocation nucleation. The developed theoretical model with machine learning can be further used for the simulation of a shock-wave structure in metastable Al-Cu solid solutions, and the developed method can be applied to other metallic systems, including high-entropy alloys.
引用
收藏
页码:526 / 553
页数:28
相关论文
共 6 条
  • [1] Dynamics and kinetics of dislocations in Al and Al-Cu alloy under dynamic loading
    Yanilkin, A. V.
    Krasnikov, V. S.
    Kuksin, A. Yu
    Mayer, A. E.
    INTERNATIONAL JOURNAL OF PLASTICITY, 2014, 55 : 94 - 107
  • [2] MOLECULAR DYNAMIC MODELING OF PLASTICITY OF Al AND Al-Cu ALLOYS UNDER DYNAMIC LOADING
    Stegailov, Vladimir V.
    Kuksin, Alexey Yu.
    Norman, Genri E.
    Yanilkin, Alexey V.
    SHOCK COMPRESSION OF CONDENSED MATTER - 2009, PTS 1 AND 2, 2009, 1195 : 781 - 784
  • [3] Study of large strain deformation of dilute solid solutions of Al-Cu using channel-die compression
    Deschamps, A
    Brechet, Y
    Necker, CJ
    Saimoto, S
    Embury, JD
    MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 1996, 207 (02): : 143 - 152
  • [4] Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations
    Erhard, Linus C.
    Otzen, Christoph
    Rohrer, Jochen
    Prescher, Clemens
    Albe, Karsten
    NPJ COMPUTATIONAL MATERIALS, 2025, 11 (01)
  • [5] Comparative Analysis of Slope Stability for Kalimpong Region under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers
    Bansal, Vaishnavi
    Sarkar, Raju
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING, 2024, 48 (4) : 2785 - 2807
  • [6] Investigating the fracture behaviors of bulk metallic glasses under different dynamic loading rates using phase-field model
    Zhang, Hongying
    Zhou, Yexin
    Zhong, Zheng
    MATERIALS TODAY COMMUNICATIONS, 2022, 31