Automated Discovery of Metainterfaces with Tailored Friction Laws

Abstract

Providing dry solid contacts with on-demand macroscale frictional behaviour remains a formidable challenge in tribology, haptics or robotics. Metainterfaces created from surfaces with engineered asperity-based topographies can achieve such friction control. However, only few friction behaviours were demonstrated because suitable topographies were identified based on human intuition. Here, we introduce a numerical-optimisation-based inverse design framework to automatically discover new metainterfaces satisfying specified relationships between friction and normal forces (friction law). To illustrate the framework's versatility, we first expand the range of achievable friction coefficients at a constant material pair; we next unlock power-law friction laws with arbitrary exponents between 2/3 and 1.35; we then achieve bilinear laws with a smaller slope in the second segment than in the first. We validate relevant cases experimentally. By enabling systematic exploration of large parameter spaces, not limited to topography but potentially incorporating the individual asperities' bulk material or surface physicochemistry, our automated framework offers design solutions for any physically possible friction law. It also provides new insights into the elusive relationship between local interfacial properties and macroscopic friction.