| Popis: |
Cellular soundproofing materials have been critical for lowering incidence pressure waves, which have led to their wide application in studios, auditoria, automotive and aerospace applications, among others. This work, therefore, premiers the utilisation of machine learning backpropagation algorithm of artificial neural networks (ANN) to develop and train a series of datasets of sound absorption properties (noise reduction coefficient, sound absorption average, and area under the sound absorption spectrum) obtained via numerical modelling and simulations. ANN models were established to analyze these sound absorption properties in relation to several non–acoustical parameters associated with cellular materials, including their thickness. Modelling and simulation revealed the relative importance of non–acoustic parameters on sound absorption spectra for these structures. ANN models predicted output signals that almost overlapped with their respective “real” signals with correlation coefficients over 95 %. Model confidence was proposed to analytically predict sound absorption phenomena accurately for cellular materials characterized by permeability ranging between 0.5–3.0 × 10−09 m2 or viscous characteristic lengths ranging between 80 and 300 μm. Analytical models based on optimised synaptic weights, biases, non–acoustic parameters, and material thickness could assist manufacturers design soundproofing materials for noise reduction and reverberation control. |
