Open Access

Article

Article ID: 3908

Inverse engineering of micro-perforated plates for targeted acoustic characteristics

by Binxia Yuan, Xiangyang Li, Tianqi You, Tianzhong Chen, Rui Zhu

Sound & Vibration, Vol.6, No.3, 2026;

The inverse design of micro-perforated panels (MPPs) for target sound absorption remains challenging due to the complex nonlinear relationship between structural parameters and acoustic performance. This study proposes a tandem neural network (TNN) framework to achieve efficient inverse design of single-layer MPPs. A forward multi-layer perceptron (MLP) is first trained to accurately predict the absorption coefficient curve from three key structural parameters: perforation diameter, panel thickness, and cavity depth. The forward model achieves superior accuracy compared to GA-SVR, GridSearch-SVR, and random forest models, with an R² of 0.999 and MAE of 0.007. Subsequently, an inverse design network is connected in series with the frozen forward network, taking a target absorption curve as input and outputting the corresponding structural parameters. The activation function of the output layer constrains the parameters within physically feasible ranges. The framework is validated by designing an MPP with a distinct absorption peak in the 300–600 Hz range. The predicted parameters (diameter 0.93 mm, thickness 0.9 mm, cavity depth 9.9 mm) yield an absorption curve that matches the target with an R² of 0.997. This work demonstrates that deep learning can effectively automate the inverse design of MPPs, offering a flexible and efficient alternative to traditional trial-and-error methods.