Experimental and Computational Acoustic Analysis of Recycled Automobile Dashboard Composites

This study presents the development and optimisation of sustainable composite materials derived from automotive polymeric waste (dashboard crumbs). The influence of key formulation parameters on material performance was investigated using experimental analysis combined with model-based prediction. Acoustic behaviour was evaluated using an impedance tube method, while predictive modelling was performed using the Johnson–Champoux–Allard (JCA) model and a Padé approximation for efficient computation. Model performance was assessed using quantitative metrics, including root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2), demonstrating good agreement with experimental data across the investigated frequency range. The results show that catalyst concentration is a critical parameter, with an optimal value of 5 wt% yielding near-unity absorption within the mid-frequency range (1200–1800 Hz). Further increase in catalyst content resulted in reduced performance due to changes in pore structure and reaction kinetics. In contrast, particle size variation exhibited a limited effect on overall performance. The proposed modelling framework enables efficient prediction of material behaviour and supports optimisation of formulation parameters. This study highlights the potential of recycled polymeric materials for sustainable engineering applications and provides a practical approach for performance-driven material design.