多晶材料建模中基于交互的取向感知深度材料网络
Orientation-aware interaction-based deep material network in polycrystalline materials modeling
AI总结：
本文提出基于交互的取向感知深度材料网络ODMN，通过取向感知机制和Hill-Mandel交互机制，仅用线弹性数据训练即可准确预测多晶材料在复杂塑性变形下的力学响应与织构演化，扩展了深度材料网络的应用范围。
摘要：
多尺度模拟对于将微观结构特征与多晶材料的宏观行为联系起来不可或缺，但其高昂的计算需求限制了其实用性。深度材料网络（DMNs）已被提出作为高效的代理模型，然而它们在捕捉织构演化方面仍存在不足。为解决这一局限，我们提出了基于交互的取向感知深度材料网络（ODMN），该网络融合了取向感知机制和基于Hill-Mandel原理的交互机制。取向感知机制学习晶体学织构，而交互机制捕捉代表性体积单元（RVE）子区域之间的应力平衡方向，从而提供对内部微观结构力学的深入认识。值得注意的是，ODMN仅需线弹性数据进行训练，却能有效泛化到复杂的非线性和各向异性响应。我们的结果表明，ODMN能够准确预测复杂塑性变形下的力学响应和织构演化，从而将DMNs的适用范围扩展到多晶材料。通过在计算效率与预测保真度之间取得平衡，ODMN为多晶材料的多尺度模拟提供了一个稳健的框架。
英文摘要：
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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