Machine Learning Based Optimal Design of Fibrillar Adhesives
This work addresses the problem of designing high-performance fibrillar adhesives for applications in robotics, transportation, and medicine, offering an incremental improvement by applying machine learning to a previously unexplored scale.
The study tackled the complex design challenge of optimizing fibril compliance distribution in fibrillar adhesives to maximize adhesive strength, using a machine learning-based tool with two deep neural networks that recovered previous design results for simple geometries and introduced novel solutions for complex configurations, significantly reducing test error and accelerating the optimization process.
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).