利用机器学习建立动态加载下铝铜固溶体结构相变的理论模型

Dynamics Pub Date : 2024-07-12 DOI:10.3390/dynamics4030028
Natalya A. Grachyova, E.V. Fomin, Alexander Mayer
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引用次数: 0

摘要

要从理论上描述材料的复杂动态加载,亟待解决的问题是建立能反映多种塑性机制相互作用的动态塑性模型。在此,我们以 Al-Cu 固溶体为模型材料,以单轴压缩为模型载荷,通过位错和相变的联合作用来考虑动态塑性松弛。我们提出了一个结合分子动力学(MD)数据、理论框架和机器学习(ML)方法的简单而稳健的理论模型。对 Al、Cu 和 Al-Cu 固溶体进行单轴压缩的 MD 模拟显示,在 Cu 浓度为 30-80% 的范围内,位错塑性和相变共同作用导致剪切应力松弛,位错活动被完全抑制。特别是,纯铝在约 36 GPa 的压力下几乎完全实现了从面心立方结构到体心立方结构的相变,而纯铜至少在 110 GPa 的压力下才实现相变。考虑到位错活动和相变,我们建立了一个应力松弛理论模型,并将其应用于描述铝铜固溶体的 MD 结果。采用阿伦尼乌斯方程来描述相变速率。应用贝叶斯方法确定模型参数,并以 MD 结果为参考数据进行拟合。使用两个由单轴压缩和拉伸的 MD 数据训练的前向传播人工神经网络(ANN)来逼近构成关系中的单值函数,如状态方程(EOS)、弹性(剪切和体积)模量和描述位错成核的成核应变距离函数。所开发的机器学习理论模型可进一步用于模拟易陨落铝铜固溶体中的冲击波结构,所开发的方法还可应用于其他金属体系,包括高熵合金。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theoretical Model of Structural Phase Transitions in Al-Cu Solid Solutions under Dynamic Loading Using Machine Learning
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.
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