Variational autoencoder-based topological optimization of an anechoic coating: An efficient- and neural network-based design

Abstract

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.