High-throughput characterization of snap-through stability boundaries of bistable beams in a programmable rotating platform

TL;DR

This paper presents a high-throughput experimental platform for systematically studying the stability boundaries of bistable beams under dynamic conditions, enabling large-scale data collection for nonlinear mechanics analysis.

Contribution

The authors develop a scalable, parallel testing platform that explores the phase space of bistable beams under programmable rotation, providing detailed stability maps and parameterization.

Findings

Constructed stability boundaries as parabolic functions.

Tilt angle effectively tunes the curvature parameter.

Beam thickness and pre-compression influence vertical offset.

Abstract

We introduce a high-throughput platform that enables simultaneous, parallel testing of six bistable beams via programmable motion of a rotating disk. By prescribing harmonic angular dynamics, the platform explores the phase space of angular velocity and acceleration $(\Omega,\,\dot{\Omega})$, producing continuously varying centrifugal and Euler force fields that act as tunable body forces in our specimens. Image processing extracts beam kinematics with sub-pixel accuracy, enabling precise identification of snap-through events. By testing six beams in parallel, the platform allows systematic variation of beam thickness, pre-compression, tilt angle, and clamp orientations across 65 distinct configurations, generating 23,400 individual experiments. We construct stability boundaries and quantitatively parameterize them as parabolic functions, characterized by a vertical offset and a curvature parameter. Tilt angle provides the most robust mechanism for tuning the curvature parameter, while beam thickness and pre-compression modulate vertical offset. Modal decomposition analysis reveals that antisymmetric clamp configurations can trigger mode switching, in which competing geometric and inertial effects drive transitions through different deformation pathways. Our work establishes a scalable experimental framework for high-throughput characterization of dynamic nonlinear instabilities in mechanics. The complete experimental dataset is made publicly available to support data-driven design and machine learning models for nonlinear mechanics with applications to bistability-based metamaterials, mechanical memory, and electronics-free sensing systems.