Using data-driven techniques to provide feedback during material characterisation

Abstract

The aim of the work is to study the feasibility of using machine learning techniques to design a decision helper to assist the characterisation of acoustic materials (porous media for instance). The tool is intended to alert the human operator about specific physical phenomena occurring during the measurements or common mistakes in handling the characterization rig or its parameters. Examples of classical issues include leakage around the samples, unintentional compression during the sample mounting, errors in input parameters such as the static pressure or temperature, etc. The proposed helper relies on a physical analysis and a k-nearest neighbours classifier using the Fréchet distance to score the measurements. This approach allows to measure the similarity between curves, independently from sampling. The training phase is performed on a labelled dataset created from actual impedance tube measurements and possibly some computer generated results to bridge gaps. The inputs are frequency-dependent quantities including normal sound absorption curves, surface impedance, dynamic mass density and dynamic bulk modulus.