Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit

TL;DR

This paper introduces a novel inverse design framework for engineering tribological interfaces with programmable static friction, surpassing traditional Hertzian contact limitations through differentiable mechanics and neural optimization.

Contribution

It develops a differentiable contact mechanics model integrated with neural networks to automatically design asperity topographies for complex friction behaviors.

Findings

Successfully designed non-Hertzian asperities with nonlinear contact responses.

Validated designs against high-fidelity Boundary Element Method simulations.

Enabled scale-invariant, data-driven discovery of functional tribological surfaces.

Abstract

Programming the static friction of mechanical interfaces is critical for soft robotics, haptics, and precision gripping. Static friction is governed by the real contact area, and standard rough surfaces exhibit a linear area-load scaling inherent to classical Archard and Greenwood-Williamson models, severely restricting their functional range. Here, we propose a framework for the inverse design of tribological metainterfaces engineered for programmable contact behaviors. By utilizing general axisymmetric asperities, we unlock nonlinear macroscopic responses unattainable by standard Hertzian contacts. To solve the inverse problem, we embed a fully differentiable contact mechanics engine within a neural network and a quadratic optimizer. We leverage regularized physical gradients to automatically discover non-standard topographies that reproduce complex target friction laws, with only a few asperities in unit cells. The predicted designs are strictly validated against high-fidelity Boundary Element Method (BEM) simulations. This framework bridges data-driven optimization and rigorous physics, offering a scale-invariant pathway for discovering functional tribological surfaces.