Shojaeifard Mohammad, Ferraresso Matteo, Lucantonio Alessandro, Bacca Mattia
Mechanical Engineering Department, University of British Columbia, Vancouver, BC V6T1Z4, Canada.
Department of Mechanical and Production Engineering, Aarhus University, Aarhus, Denmark.
J R Soc Interface. 2025 Feb;22(223):20240636. doi: 10.1098/rsif.2024.0636. Epub 2025 Feb 26.
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 neural networks (NNs), recovers previous design results for simple geometries and introduces novel solutions for complex configurations. The predictor NN estimates adhesive strength based on random compliance distributions, while the designer NN optimizes compliance distribution to achieve maximum strength using gradient-based optimization. This 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.
在甲虫、蜘蛛和壁虎等动物身上观察到的纤维状粘附，依靠纳米级或微观级的纤维，通过“接触分裂”来增强表面粘附力。这一概念启发了机器人技术、交通运输和医学等领域的工程应用。最近的研究表明，纤维特性的功能分级可以提高粘附力，但这是一个复杂的设计挑战，目前仅在简化几何形状中进行过探索。虽然机器学习（ML）在粘合剂设计中已受到关注，但此前尚未有针对纤维阵列尺度优化的尝试。在本研究中，我们提出了一种基于机器学习的工具，该工具可优化纤维柔顺性分布，以实现粘附力最大化。我们的工具由两个神经网络（NN）组成，它能重现简单几何形状的先前设计结果，并为复杂结构引入新颖的解决方案。预测神经网络基于随机柔顺性分布估计粘附力，而设计神经网络使用基于梯度的优化方法来优化柔顺性分布，以实现最大强度。该方法显著降低了测试误差，加速了优化过程，通过实现均等的载荷分担，为设计纤维状粘合剂和旨在抗断裂的微结构材料提供了一种高性能解决方案。
