期刊
MATERIALS TODAY COMMUNICATIONS
卷 32, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.mtcomm.2022.103901
关键词
Anechoic coating; Topology optimization; Variational autoencoder; Neural network
资金
- National Natural Science Foundation of China [51765008]
- High-level Innovative Talents Project of Guizhou Province [20164033]
- Science and Technology Project of Guizhou Province [2020-1Z048]
This paper investigates a VAE model-based topology optimization method for optimizing the cavity structure of anechoic coatings. The finite-element method is used to calculate the sound absorption coefficient, and the VAE model is trained to learn the key features of an anechoic coating. The method efficiently generates new anechoic coatings with specific sound absorption properties.
An anechoic coating is an artificial heterogeneous composite material composed of periodic cells with cavities. Using the local resonance of cavities to reduce their sound absorption frequencies and widen their frequency bands has been a research hotspot in recent years. One of the main challenges involves optimizing the cavity structure of an anechoic coating to obtain low-frequency, broadband, and strong sound absorption properties. In this paper, a variational autoencoder (VAE) model-based topology optimization method was investigated. First, the finite-element method (FEM) was used to calculate the sound absorption coefficient, and a dataset was constructed with samples whose average sound absorption coefficients ranged from 200 to 6000 Hz and were greater than 0.75. Then, the VAE model was trained to learn the key features of an anechoic coating. Finally, the data were reconstructed with Gaussian distributions. The decoder network of the trained VAE model was used to design a new anechoic coating. It took only approximately 3 s to generate 100 new topologies, and the average absorption coefficients were all greater than 0.754. This efficient neural network-based method can be further generalized to optimize the designs of various mechanical structural materials with specific functions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据