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Lookup NU author(s): Dr Xinwei LiORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.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.
Author(s): Hu Z, Ding J, Ding S, Ma WWS, Chua JW, Li X, Zhai W, Song X
Publication type: Article
Publication status: Published
Journal: Virtual and Physical Prototyping
Year: 2024
Volume: 19
Issue: 1
Online publication date: 10/10/2024
Acceptance date: 02/04/2018
Date deposited: 28/10/2024
ISSN (print): 1745-2759
ISSN (electronic): 1745-2767
Publisher: Taylor and Francis Ltd.
URL: https://doi.org/10.1080/17452759.2024.2412198
DOI: 10.1080/17452759.2024.2412198
Data Access Statement: The data that support the findings of this study are available from the corresponding author, Dr. Xu Song, upon reasonable request.
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