Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin, L. Catherine Brinson
TL;DR
This work tackles the challenge of designing hierarchical phononic materials with tunable multi-band gaps by extending the interpretable unit-cell template method to hierarchical templates. A two-level coarse-to-fine design enables scale separation, allowing coarse-scale bandgap objectives to be preserved while refining fine-scale features to realize additional high-frequency bandgaps, all within a much larger, not previously explored design space. The method demonstrates robust scale-separation: coarse objectives remain intact as fine features are added, with quantified robustness via fine-scale transfer precision and substantial computational savings from sequential design. Numerical and experimental validations—featuring FE dispersion analysis and a 3D-validated, 8×8 steel lattice with LDV measurements—confirm that the hierarchical templates can realize prescribed bandgaps in real structures, offering a flexible route to multidimensional wave control in metamaterials.
Abstract
Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ``hierarchical unit-cell template method,'' is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.
