新模型能精准预测多晶材料变形时的力学与织构变化。
Orientation-aware interaction-based deep material network in polycrystalline materials modeling
- 引入取向感知与交互机制，捕捉晶体织构和应力平衡方向。
- 仅需线弹性数据训练，即可准确预测非线性各向异性响应。
- 适合需要高效高精度模拟的材料设计与仿真研究者。
多尺度模拟对连接多晶材料微观结构与宏观行为至关重要，但计算成本过高限制了实际应用。深度材料网络（DMNs）作为高效代理模型被提出，但难以捕捉织构演化。为此，本文提出取向感知交互式深度材料网络（ODMN），结合基于Hill-Mandel原理的取向感知机制与交互机制。取向感知机制学习晶体学织构，交互机制捕捉代表性体积元（RVE）子区域间的应力平衡方向，揭示内部微结构力学特征。值得注意的是，ODMN仅需线弹性数据训练，即可有效泛化至复杂非线性与各向异性响应。结果表明，ODMN能准确预测复杂塑性变形下的力学响应与织构演化，显著拓展了DMNs在多晶材料中的适用性。通过兼顾计算效率与预测精度，ODMN为多晶材料的多尺度模拟提供了稳健框架。
原文摘要 · Abstract (English)
Multiscale simulations are indispensable for connecting microstructural features to the macroscopic behavior of polycrystalline materials, but their high computational demands limit their practicality. Deep material networks (DMNs) have been proposed as efficient surrogate models, yet they fall short of capturing texture evolution. To address this limitation, we propose the orientation-aware interaction-based deep material network (ODMN), which incorporates an orientation-aware mechanism and an interaction mechanism grounded in the Hill-Mandel principle. The orientation-aware mechanism learns the crystallographic textures, while the interaction mechanism captures stress-equilibrium directions among representative volume element (RVE) subregions, offering insight into internal microstructural mechanics. Notably, ODMN requires only linear elastic data for training yet generalizes effectively to complex nonlinear and anisotropic responses. Our results show that ODMN accurately predicts both mechanical responses and texture evolution under complex plastic deformation, thus expanding the applicability of DMNs to polycrystalline materials. By balancing computational efficiency with predictive fidelity, ODMN provides a robust framework for multiscale simulations of polycrystalline materials.
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