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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

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Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method.

Shangyang Jin¹, Fuxing Chen², Jie Bai³

¹College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China.

|July 23, 2025

Summary

This summary is machine-generated.

This study introduces a new DGN method combining Design of Experiments (DOE), Genetic Algorithm (GA), and NLPQL for optimizing metamaterial acoustic properties. The DGN method enhances sound insulation performance and bandwidth effectively.

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Area of Science:

Materials Science
Acoustics
Computational Engineering

Background:

Global optimization algorithms face convergence issues for metamaterial acoustic properties.
Local optimization algorithms are limited by initial data and struggle with parameter influence.
Traditional algorithms lack efficiency in reflecting structural parameter impacts on metamaterial performance.

Purpose of the Study:

To propose a novel combination algorithm optimization strategy for metamaterials.
To address limitations of traditional global and local optimization methods.
To enhance the optimization of acoustic properties considering multiple structural parameters.

Main Methods:

A co-simulation approach integrating Design of Experiments (DOE), Genetic Algorithm (GA), and NLPQL algorithm (DGN method).
DOE to establish relationships between structural parameters and acoustic performance.
Combined global (GA) and local (NLPQL) optimization to improve convergence and quality.

Main Results:

The DGN method achieved a 44.8% optimization effect on peak sound insulation frequency and 116.7% on sound insulation bandwidth.
Compared to NSGA-II, the DGN method improved acoustic isolation bandwidth optimization by 36.8%.
Optimized metamaterial structures exhibit enhanced low-frequency sound insulation.

Conclusions:

The DGN method offers a robust strategy for optimizing metamaterial acoustic properties.
This approach overcomes convergence and data dependency issues in metamaterial design.
Provides a new framework for metamaterial design and performance regulation.