AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation

2024-12-03

AI Summary

To address the engineering need for on-demand vibration suppression in high-precision manufacturing, this work proposes an AI-driven inverse design methodology for non-periodic interlocked mechanical metamaterials, enabling precise frequency-response inversion and localized resonance bandgap customization. We innovatively develop a forward model based on finite-element-informed multi-head spatial attention (FSA) and an inverse framework leveraging multi-scale Gaussian self-attention (MGSA), incorporating 1D Gaussian spectral positional encoding to overcome conventional periodicity constraints. Experimental validation on nine additively manufactured metamaterials demonstrates a bandgap matching error of less than 3.2% within target frequency ranges and achieves an R² of 0.987 for vibration transmission prediction. This study marks the first successful high-fidelity frequency-response inverse generation for non-periodic interlocked architectures.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• AI-driven inverse design of complex metastructures for vibration mitigation.
• Development of a novel forward analysis model using multi-head FEM-inspired spatial attention.
• Creation of a multiscale Gaussian self-attention model for inverse design of metastructures.

Innovation

• AI-driven inverse design for vibration mitigation
• Multi-head FEM-inspired spatial attention model
• Multiscale Gaussian self-attention for inverse design