【摘要】 针对异质结构金属材料（heterostructured metallic materials, HMMs）设计中强度-韧性倒置关系难题，文章提出一种融合深度学习与多目标进化算法的智能设计框架，即基于微观结构参数与力学响应的复杂映射关系，构建了结合双向长短期记忆网络（bidirectional long short-term memory, Bi-LSTM）、注意力机制与物理约束的混合神经网络模型。通过有限元模拟数据集训练验证，该模型对极限抗拉强度（ultimate tensile strength, UTS）和韧性的预测决定系数R~2分别为0.896 6和0.967 2。进一步集成非支配排序遗传算法（non-dominated sorting genetic algorithm Ⅱ, NSGA-Ⅱ），建立了以微观结构参数为变量的多目标优化框架。研究结果表明，该模型可切实捕捉微观结构与性能的映射关系，为异质结构金属材料的智能预测找到了新途径。更多还原

【Abstract】 To address the strength-ductility trade-off dilemma in the design of heterostructured metallic materials(HMMs), an intelligent design framework integrating deep learning and multi-objective evolutionary algorithms was proposed. Leveraging the complex mapping relationship between microstructural parameters and mechanical responses, a hybrid neural network model is constructed by combining bidirectional long short-term memory(Bi-LSTM), attention mechanisms, and physical constraints.Trained and validated on a dataset generated by finite element simulations, the model achieves coefficients of determination(R~2) of 0.896 6 and 0.967 2 for predicting ultimate tensile strength(UTS)and ductility, respectively. Furthermore, a multi-objective optimization framework based on the nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is established, with microstructural parameters as variables. The results demonstrate that the model effectively captures the mapping relationship between microstructure and performance, offering a novel pathway for the intelligent prediction of heterostructured metallic materials.更多还原