Designing directional adhesive pillars using deep learning-based optimization, 3D printing, and testing

Abstract

Nature-inspired fibrillar adhesives have versatile applications, such as wall-climbing robots and grippers, which require residue-free and repeatable adhesion. To meet the requirements of excellent adhesion strength combined with controllability of adhesion and detachment, directional adhesive pillars with anisotropic adhesion properties have been extensively investigated. However, existing designs that simply mimic nature suffer from relatively weak adhesion and insufficient directionality, because most of them were designed without considering a sufficiently large design space. In this study, we rigorously defined adhesive directionality and systematically investigated the optimal pillar shape to obtain an excellent combination of adhesion strength and directionality using deep-learning-based optimization. A data-driven model based on artificial neural networks was trained using finite element analysis data obtained from 199,466 adhesive pillar shapes, which can predict the directionality and adhesion strength of given pillar shapes. Optimization was performed using the trained neural network to obtain the optimal pillar shape under the geometric constraints necessary to secure the reliability of the pillar. Finally, we suggest adhesive pillar shapes having severe directionality and high adhesive strength at the same time. For validation of our optimization model and optimized result, the proposed pillar shapes were fabricated using a 3D polyjet printer, and their adhesion strength and directionality were tested. We noticed that optimized pillar shapes we obtained have superior directionality and adhesion strength in real.