通过自组织神经细胞自动机实现无序超材料的一次性生成设计

中文摘要 AI 辅助

无序超材料具有内在随机性和不规则性的微观结构，能实现更广泛的性能覆盖和卓越性能。然而，设计无序微观结构比设计规则结构困难得多，目前的设计方法存在局限性。为此，我们提出基于神经细胞自动机的生成设计框架，受自然材料自组织过程启发，通过学习局部交互规则动态生长复杂微观结构。该框架仅需单个训练模板，能适应多种无序微观结构、不规则域和任意离散化。通过操纵局部规则，可生成训练中未见的微观结构，控制方向、各向异性和方向厚度。我们在多尺度机械隐身设计中验证了这一点，该设计无需复杂后处理和现有方法中常见的不兼容组装，就能实现优异的隐身性能。这种数据高效、可推广的方法为生物医学植入物和软机器人等领域提供了以前难以处理的无序材料。

英文摘要

Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable in their regular counterparts. Despite their promise, designing disordered microstructures is substantially harder than designing regular ones. Their design remains trapped between manual parameterizations with limited expressiveness, and generative AI that is data-hungry and struggles to generalize. To address these limitations, we propose a generative design framework based on Neural Cellular Automata that dynamically grows complex microstructures through learned local interaction rules, inspired by the self-organizing processes in natural materials. This framework requires only a single training template, yet accommodates diverse disordered microstructures and adapts to irregular domains and arbitrary discretizations. By manipulating the learned local rules, we can steer the growth process to generate microstructures unseen during training, providing control over orientation, anisotropy, and directional thickness without retraining. As a dynamic, local growth process, it naturally produces spatially varying microstructures that transition smoothly to enable location-specific mechanical properties. We demonstrate this in a multiscale mechanical cloaking design, where microstructures vary across the space to meet an optimized heterogeneous property distribution. Our design enables excellent cloaking performance without complicated post-processing and incompatible assembly common in existing methods. This data-efficient, generalizable approach opens access to previously intractable disordered materials for biomedical implants and soft robotics.

发表机构

• Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。