Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit
- URL: http://arxiv.org/abs/2605.11012v2
- Date: Tue, 19 May 2026 14:58:10 GMT
- Title: Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit
- Abstract summary: Static friction is governed by the real contact area.<n>Standard rough surfaces exhibit a linear area-load scaling inherent to classical Archard and Greenwood-Williamson models.<n>We propose a framework for the inverse design of tribological metainterfaces engineered for programmable contact behaviors.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Push, Press, Slide: Mode-Aware Planar Contact Manipulation via Reduced-Order Models [4.600772894287281]
We propose a mode-aware framework for planar manipulation with one or two robotic arms.<n>Our core insight is that complex wrench-twist limit surface mechanics can be abstracted into a discrete library of physically intuitive models.<n>We demonstrate our fast, optimization-free approach in simulation across diverse single-arm and bimanual manipulation tasks.
arXiv Detail & Related papers (2026-03-12T19:21:19Z) - ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control [7.095015036485825]
We present a GPU-parallelized analytical contact physics engine built on complementarity-free contact modeling.<n>ComFree-Sim computes contact impulses in closed form via an impedance-style prediction--correction update in the dual cone of Coulomb friction.
arXiv Detail & Related papers (2026-03-12T17:14:45Z) - PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
arXiv Detail & Related papers (2026-01-19T07:57:52Z) - Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces [0.0]
We introduce a generative modeling framework to infer surface topographies from target friction laws.<n> Trained on a synthetic dataset composed of 200 million samples constructed from a parameterized contact mechanics model, the proposed method enables efficient, simulation-free generation of candidate topographies.<n>This approach paves the way for near-real-time control of frictional behavior through tailored surface topographies.
arXiv Detail & Related papers (2025-10-27T13:00:48Z) - Physics-informed neural network solves minimal surfaces in curved spacetime [0.0]
We develop a framework based on physics-informed neural networks (PINNs) for solving boundary value problems involving minimal surfaces in curved spacetimes.<n>Our approach enables robust and accurate solutions to both ordinary and partial differential equations with complex boundary conditions.
arXiv Detail & Related papers (2025-09-13T15:41:13Z) - Learning Transferable Friction Models and LuGre Identification via Physics Informed Neural Networks [4.432363122731454]
We present a physics-informed friction estimation framework to integrate well-established friction models with learnable components.<n>Our approach enforces physical consistency yet retains the flexibility to adapt to real-world complexities.<n>We show that our approach enables the learned models to be transferable to systems they are not trained on.
arXiv Detail & Related papers (2025-04-16T19:15:48Z) - Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization.
Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information.
Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
arXiv Detail & Related papers (2024-05-23T14:39:52Z) - Equivariant Graph Mechanics Networks with Constraints [83.38709956935095]
We propose Graph Mechanics Network (GMN) which is efficient, equivariant and constraint-aware.
GMN represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.
Extensive experiments support the advantages of GMN compared to the state-of-the-art GNNs in terms of prediction accuracy, constraint satisfaction and data efficiency.
arXiv Detail & Related papers (2022-03-12T14:22:14Z) - Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit
Constraints [49.66841118264278]
We introduce a series of challenging chaotic and extended-body systems to push the limits of current approaches.
Our experiments show that Cartesian coordinates with explicit constraints lead to a 100x improvement in accuracy and data efficiency.
arXiv Detail & Related papers (2020-10-26T13:35:16Z) - Scalable Differentiable Physics for Learning and Control [99.4302215142673]
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions.
arXiv Detail & Related papers (2020-07-04T19:07:51Z) - First Steps: Latent-Space Control with Semantic Constraints for
Quadruped Locomotion [73.37945453998134]
Traditional approaches to quadruped control employ simplified, hand-derived models.
This significantly reduces the capability of the robot since its effective kinematic range is curtailed.
In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space.
A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators.
We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
arXiv Detail & Related papers (2020-07-03T07:04:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.