用于多晶微结构性能引导逆设计的条件晶粒图扩散方法
Conditional grain-graph diffusion for property-guided inverse design of polycrystalline microstructures
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中文总结 AI 辅助
提出条件晶粒图扩散框架，结合增强晶粒图神经网络，高效实现双相Ti-6Al-4V微结构的性能引导逆设计，生成候选性能接近预设且晶体学一致性高，运行速度远快于随机搜索与进化优化。
中文摘要 AI 辅助
图表示能在保留晶粒拓扑和晶界信息的同时紧凑编码多晶微结构。我们提出一种条件图扩散框架，用于双相Ti-6Al-4V微结构的性能引导逆设计。带有晶界边特征、可学习节点与边嵌入以及多统计池化的增强晶粒图神经网络（GNN），作为应力预测与候选评估的前向代理。该条件扩散模型在预设的α相体积分数、弹性模量和屈服应力代理目标下，通过反向扩散生成候选结构。在四个目标 regime 及独立种子起始集上，生成的候选结构始终接近预设性能，包括超出现有微结构性能包络的目标。生成后通过与伯格斯取向关系（BOR）的偏差评估局部晶体学一致性，考虑BOR的排序使包络内、外目标的平均BOR一致性分别提升达44.9%和56.4%，同时保持性能匹配。对每个主要设计案例的5个最佳候选进行有限元验证，其平均性能的最大绝对相对误差为1.0%。在代表性基准测试中，扩散方法对每个输入图需32次候选评估，而随机搜索和进化优化约需40000次，在测试实现中运行时间减少约两个数量级。这些结果表明，条件晶粒图扩散是用于性能引导多晶微结构设计的高效框架。
英文摘要
Graph representations compactly encode polycrystalline microstructures while retaining grain topology and grain boundary information. We present a conditional graph diffusion framework for property-guided inverse design of dual-phase Ti-6Al-4V microstructures. An enhanced grain graph neural network (GNN) with grain boundary edge features, learnable node and edge embeddings, and multi-statistic pooling serves as a forward surrogate for stress prediction and candidate evaluation. The conditional diffusion model generates candidates through reverse diffusion under prescribed α-phase volume fraction, elastic modulus, and yield-stress proxy targets. Across four target regimes and independently seeded starting sets, generated candidates consistently approach the prescribed properties, including a target outside the property envelope of the existing microstructures. Local crystallographic consistency is evaluated post-generation from deviations from the Burgers orientation relationship (BOR). BOR-aware ranking increases mean BOR consistency by up to 44.9% and 56.4% for the in- and out-of-envelope targets, respectively, while maintaining property alignment. Finite element validation of the five best candidates in each primary design case yields a maximum absolute relative error of 1.0% in their mean properties. In a representative benchmark, diffusion requires 32 candidate evaluations per input graph, compared with approximately 40,000 for random search and evolutionary optimization, and reduces runtime by approximately two orders of magnitude in the tested implementations. These results establish conditional grain-graph diffusion as an efficient framework for property-guided polycrystalline microstructure design.
