Machine learning – enabled inverse design of shell-based lattice metamaterials with optimal sound and energy absorption
| Autor: | Zongxin Hu, Junhao Ding, Shuwei Ding, Winston Wai Shing Ma, Jun Wei Chua, Xinwei Li, Wei Zhai, Xu Song |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Virtual and Physical Prototyping, Vol 19, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 17452759 1745-2767 1745-2759 |
| DOI: | 10.1080/17452759.2024.2412198 |
| Popis: | Currently, the development in shell-based lattice, is increasingly focused on multifunctionality, with growing interest in combining sound and energy absorption. However, few studies have explored the multi-objective inverse design process. Herein, we propose a new approach using machine learning (ML) to optimise both the mechanical and acoustic performances of shell-based lattices. Firstly, the K-Nearest Neighbour and Artificial Neural Network are employed to predict the properties of different configurations. Then the non-dominated sorting genetic algorithm is employed to generate the desired structures. Finally, the lightweight metamaterials generated achieve optimal multifunctional performances (an energy absorption capacity of 50% higher than typical Gyroid structure and a sound absorption coefficient near 1 at specific frequency band). Besides, the potential trade-off phenomenon of mechanical and acoustic properties is also presented by our work. Overall, this work presents a new concept to use ML and genetic algorithm for multi-functional inverse design for shell lattice metamaterials. |
| Databáze: | Directory of Open Access Journals |
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