Speaker
Description
This paper presents a novel indirect methodology for the characterization of fibrous sound-absorbing materials based on in-situ acoustic impedance measurements. The proposed approach relies on the combined measurement of sound pressure and particle velocity to estimate the surface acoustic impedance of the sample under test, enabling material characterization directly in realistic measurement conditions. An inverse identification framework based on the Johnson–Champoux–Allard–Lafarge (JCAL) model is employed to optimize the material parameters that exhibit the strongest influence on the acoustic response, namely airflow resistivity, effective sample thickness, and viscous characteristic length. Other parameters are kept fixed, as a sensitivity analysis showed within a physical feasible range their impact on the measured impedance to be negligible within the investigated frequency range. The optimization procedure is designed to be robust and computationally efficient, making it suitable for automated measurement scenarios. The method is evaluated on a wide variety of fibrous samples, covering different thicknesses and densities, and the extracted parameters are systematically compared against results obtained from standardized laboratory measurement techniques. Experimental results demonstrate good agreement between the proposed indirect approach and reference methods, confirming the validity and reliability of in-situ impedance-based parameter estimation. The study highlights the potential of this technique for fast, non-destructive material characterization and paves the way for its application in industrial and end-of-line quality control environments.