Machine Learning Based Optimal Design of Fibrillar Adhesives

TL;DR

This paper introduces a machine learning tool that optimizes fibril compliance distributions to enhance the adhesive strength of fibrillar adhesives, advancing design capabilities for complex geometries.

Contribution

It develops a novel ML-based framework with two deep neural networks for fibrillar adhesive optimization, including for complex configurations, improving efficiency and design accuracy.

Findings

The ML tool accurately predicts adhesive strength from compliance distributions.

It successfully recovers known simple geometry designs and finds new solutions for complex geometries.

The method reduces testing errors and speeds up the optimization process.

Abstract

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).