Quantitative analysis of predictors of acoustic materials for noise reduction as sustainable strategies for materials in the automotive industry

Abstract This study proposes a qualitative analysis to identify the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data on materials from six different classes and employs a multidisciplinary approach, including Mann‒Whitney U tests, Kruskal‒Wallis analysis with Dunn's post hoc multiple comparisons, and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class, and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase.