用机器学习优化纤维粘附剂的软硬分布，提升粘合力。
Machine Learning Based Optimal Design of Fibrillar Adhesives
- 构建双深度神经网络，通过梯度优化实现纤维柔性的智能分配。
- 在复杂结构中实现粘附力显著提升，且测试误差大幅降低。
- 适合微结构材料设计、仿生粘附与抗断裂材料研发人员使用。
纤维粘附现象见于甲虫、蜘蛛和壁虎等动物，其通过纳米或微米级纤维实现'接触分裂'以增强表面粘附力，已广泛应用于机器人、交通和医疗领域。研究表明，对纤维属性进行功能梯度化可提升粘附性能，但此类设计挑战复杂，此前仅限于简化几何形态。尽管机器学习已在粘附设计中崭露头角，却从未用于纤维阵列尺度的优化。本研究提出一种基于机器学习的工具，通过优化纤维柔度分布以最大化粘附强度。该工具包含两个深度神经网络：预测器（Predictor DNN）根据随机柔度分布估计粘附强度，设计师（Designer DNN）则利用梯度优化策略寻找最大强度的柔度配置。方法不仅复现了简单几何下的已有设计结果，更提出了复杂构型的新解，显著降低测试误差并加速优化过程，为实现等载荷分担（ELS）的高韧性微结构材料提供高效解决方案。
原文摘要 · Abstract (English)
Fibrillar adhesion, observed in animals like beetles, spiders, and geckos, relies on nanoscopic or microscopic fibrils to enhance surface adhesion via 'contact splitting.' This concept has inspired engineering applications across robotics, transportation, and medicine. Recent studies suggest that functional grading of fibril properties can improve adhesion, but this is a complex design challenge that has only been explored in simplified geometries. While machine learning (ML) has gained traction in adhesive design, no previous attempts have targeted fibril-array scale optimization. In this study, we propose an ML-based tool that optimizes the distribution of fibril compliance to maximize adhesive strength. Our tool, featuring two deep neural networks (DNNs), recovers previous design results for simple geometries and introduces novel solutions for complex configurations. The Predictor DNN estimates adhesive strength based on random compliance distributions, while the Designer DNN optimizes compliance for maximum strength using gradient-based optimization. Our method significantly reduces test error and accelerates the optimization process, offering a high-performance solution for designing fibrillar adhesives and micro-architected materials aimed at fracture resistance by achieving equal load sharing (ELS).
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