基于点阵数据库增强和GCNN的生长启发图生成与机械晶格逆向设计
Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN
查看机构详情
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
受生物生长启发，提出基于点阵增强和GCNN的机械晶格生成与逆向设计框架，实现刚度预测及目标性能设计。
中文摘要 AI 辅助
天然承重和运输网络并非一步组装而成，而是通过生长、分支、强化和环路形成的时序过程逐步涌现。受此发育逻辑启发，本工作引入了一种用于机械晶格的形态发生图生成框架，其中离散点阵提供潜在节点，最终架构通过跨层和层内顺序生长创建。该规则在二维中可视化为叶脉状发育序列，并在包含27个候选节点的3x3x3节点矩阵上于三维中实现。通过基于梁的有限元分析评估了一个包含不同三维晶格的数据集，并直接表示为图。具有三个图卷积层和双全局池化的图卷积神经网络（GCNN）学习拓扑-属性映射，并预测有效压缩刚度。将GCNN代理与快速结构采样相结合，实现逆向设计：对于1000 MPa的目标刚度，所选设计预测值为1042.43 MPa，并通过有限元分析验证为1027.49 MPa。除直杆外，该框架还扩展到由非线性材料制成的参数化马蹄形弯曲梁，实现面向规定变形形状的拓扑-几何设计。我们的工作为机械超材料数据库增强提供了范例，所得到的视角将生物形态发生、图学习和非线性形状编程统一在架构材料的生成设计框架中。
英文摘要
Natural load-bearing and transport networks are not assembled in a single step; they emerge through a temporally ordered process of growth, branching, reinforcement, and loop formation. Inspired by this developmental logic, this work introduces a morphogenetic graph-generation framework for mechanical lattices in which a discrete dot matrix provides potential nodes and the final architecture is created by sequential cross-layer and intra-layer growth. The same rule is visualized in two dimensions as a leaf-vein-like developmental sequence and implemented in three dimensions on a 3x3x3 nodal matrix containing 27 candidate nodes. A dataset of distinct three-dimensional lattices was evaluated by beam-based finite element analysis and represented directly as graphs. A graph convolutional neural network (GCNN) with three graph-convolution layers and dual global pooling learns the topology-property mapping and predicts effective compressive stiffness. Coupling the GCNN surrogate with rapid structural sampling enables inverse design: for a target stiffness of 1000 MPa, the selected design was predicted at 1042.43 MPa and validated by finite element analysis at 1027.49 MPa. Beyond straight members, the framework has also been extended to parameterized horseshoe-shaped curved beams made of nonlinear materials, enabling topology-geometry design toward prescribed deformation shapes. Our work provides a paradigm for augmenting the database of mechanical metamaterials, and the resulting perspective links biological morphogenesis, graph learning, and nonlinear shape programming in a unified generative design framework for architected materials.
