AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation
Journal:
arXiv
Published Date:
Dec 3, 2024
Abstract
On-demand vibration mitigation in a mechanical system needs the suitable
design of multiscale metastructures, involving complex unit cells. In this
study, immersing in the world of patterns and examining the structural details
of some interesting motifs are extracted from the mechanical metastructure
perspective. Nine interlaced metastructures are fabricated using additive
manufacturing, and corresponding vibration characteristics are studied
experimentally and numerically. Further, the band-gap modulation with metallic
inserts in the honeycomb interlaced metastructures is also studied. AI-driven
inverse design of such complex metastructures with a desired vibration
mitigation profile can pave the way for addressing engineering challenges in
high-precision manufacturing. The current inverse design methodologies are
limited to designing simple periodic structures based on limited variants of
unit cells. Therefore, a novel forward analysis model with multi-head
FEM-inspired spatial attention (FSA) is proposed to learn the complex geometry
of the metastructures and predict corresponding transmissibility. Subsequently,
a multiscale Gaussian self-attention (MGSA) based inverse design model with
Gaussian function for 1D spectrum position encoding is developed to produce a
suitable metastructure for the desired vibration transmittance. The proposed AI
framework demonstrated outstanding performance corresponding to the expected
locally resonant bandgaps in a targeted frequency range.
