Research on the Sound Absorption Performance of Metal Rubber Material Based on BP Neural Network

Abstract

In this paper, in order to study on the sound absorption performance of metal rubber material, the BP neural network method is used to fulfilling the requirements of data processing. The sound absorption performance of Metal Rubber material was studied theoretically and experimentally. It is feasible to model the sound absorption performance of metal rubber with uniform isotropic porosity. It provides a simple way to study the structural performance of metal rubber accurately. The structure constant is stable for metal rubber material with the same mean cavity diameters. It decreases with increase of the frequency and finally tends to a constant. The frequency factor in the structure constant is varied from 1 to 4/3, while the structure factor decreases in an exponentially decayed way with increase of mean cavity diameter. In the general frequency range, the compressive modules of the sound absorber samples is approximately constant when metal rubber material has the same structure parameters. The ratio of the real part to the imaginary part of the modules increases linearly with increase of mean cavity diameters.