Automated Discovery of Metainterfaces with Tailored Friction Laws
Li Fu (LTDS), Djibril Gabriel Kashala (LTDS), D. Dalmas (LTDS), J Scheibert (LTDS)

TL;DR
This paper presents an automated inverse design framework using numerical optimization to discover metainterfaces with tailored friction laws, expanding the range of achievable friction behaviors and validating some cases experimentally.
Contribution
The authors introduce a novel automated framework for designing metainterfaces that can realize specified friction laws, surpassing prior manual or intuition-based methods.
Findings
Expanded the range of achievable friction coefficients at constant material pairs.
Achieved power-law friction laws with arbitrary exponents between 2/3 and 1.35.
Validated relevant cases experimentally.
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…
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