通过逆向设计实现非线性摩擦调控，突破传统接触极限。
Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit
- 用可微分接触力学模型结合神经网络求解摩擦逆问题
- 仅需少量微结构即实现复杂摩擦规律，仿真验证可靠
- 适合软体机器人、精密抓取等需要精准摩擦控制的场景
编程机械界面的静摩擦对软体机器人、触觉反馈和精密抓取至关重要。静摩擦由实际接触面积决定，传统粗糙表面遵循经典的阿查德与格林伍德-威廉森模型中的线性面积-载荷关系，严重限制其功能范围。本文提出一种用于可编程接触行为的摩擦超界面逆向设计框架。通过使用通用轴对称凸起结构，实现了标准赫兹接触无法达到的非线性宏观响应。为解决逆问题，将全可微分接触力学引擎嵌入神经网络与二次优化器中，利用正则化物理梯度自动发现能再现复杂目标摩擦定律的非标准拓扑结构，每个单元内仅需少数凸起。预测设计经高保真边界元法（BEM）仿真严格验证。该框架融合数据驱动优化与严谨物理机制，为功能性摩擦表面的发现提供了尺度无关的路径。
原文摘要 · Abstract (English)
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.
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