强化学习用于逆向结构设计和快速激光切割 kirigami 原型
Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes
AI总结：
本文提出 RL-Kirigami 框架，结合最优传输条件流匹配与强化学习，生成兼容比场以实现紧凑可重构平行四边形 kirigami 结构。通过 marching 解码器和 GRPO 策略提升生成精度与效率，实现快速原型制造。
摘要：
Kirigami 是一种日益有用的制造方法，用于生产形状可编程的超材料结构。然而，逆向设计仍然困难，因为部署是非线性的，可行的切割布局必须满足离散的兼容性规则，避免重叠，并将目标形状映射到有效设计。我们提出了 RL-Kirigami，一种结合最优传输条件流匹配（OT-CFM）与强化学习的逆向设计框架，用于生成兼容比场以实现紧凑可重构平行四边形 kirigami。一个 marching 解码器强制执行全局几何兼容性，而 Group Relative Policy Optimization（GRPO）对齐生成器与非可微奖励以实现轮廓匹配、可行性以及比场正则性。在程序生成的目标形状实例上，一个来自预训练 OT-CFM 先验的样本达到了 94.2% 的 sIoU，并在减少前向模拟器评估次数至 1 次的同时优于求解器基线。GRPO 将精度提升到 94.91% 的 sIoU，并在包含正则性的情况下将 TV(x) 从 0.95 降至 0.81，同时保持 94.83% 的 sIoU。生成的布局被导出到 DXF 并在 50 μm 聚合物板上激光切割，以在 8.0 ± 1.0 分钟内生成可部署的原型。这些结果支持了在严格的几何可行性约束下，可部署 kirigami 超材料的制造感知逆向设计流程。
英文摘要：
Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult because deployment is nonlinear, and feasible cut layouts must satisfy discrete compatibility rules, avoid overlap, and map one target shape to valid designs. We present RL-Kirigami, an inverse design framework that combines optimal-transport conditional flow matching (OT-CFM) with reinforcement learning to generate compatible ratio fields for compact reconfigurable parallelogram quad kirigami. A marching decoder enforces global geometric compatibility, and Group Relative Policy Optimization (GRPO) aligns the generator with nondifferentiable rewards for silhouette matching, feasibility, and ratio-field regularity. Across procedurally generated target shape instances, a single sample from the pretrained OT-CFM prior reached $94.2\%~\mathrm{sIoU}$ and outperformed solver baselines while reducing forward simulator evaluations from hundreds to $1$. GRPO improved accuracy to $94.91\%~\mathrm{sIoU}$ and, with regularity included, reduced $\mathrm{TV}(\mathbf{x})$ from $0.95$ to $0.81$ while maintaining $94.83\%~\mathrm{sIoU}$. Generated layouts were exported to DXF and laser cut in $50~μ\mathrm{m}$ polymeric sheets to produce deployable prototypes in $8.0\pm 1.0$ minutes per part. These results support a manufacturing-aware inverse design workflow for deployable kirigami metamaterials under geometric feasibility constraints. Code is available at https://github.com/milad1378yz/RL-Kirigami.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
