Experimental Study and Regression Modeling of Sound Absorption Coefficient for Wood Panels

Area of Science:

• Acoustics
• Materials Science
• Wood Engineering

Background:

• Wood panels are increasingly used in architectural acoustics.
• Optimizing their sound absorption requires understanding key physical parameters.
• Existing models may not fully capture the behavior of perforated wood panels.

Purpose of the Study:

• To develop a predictive model for sound absorption coefficient of wooden panels.
• To investigate the influence of panel thickness and perforation ratio on acoustic performance.
• To establish regression functions for designing custom wooden acoustic absorbers.

Main Methods:

• Experimental measurements of normal-incidence sound absorption coefficient using an impedance tube (ISO 10534-2).
• Testing of four wood species (fir, pine, pedunculate oak, sessile oak) with varying thicknesses (11-25 mm) and perforation ratios (0-20%).
• Development of first-order regression models based on experimental data.

Main Results:

• Increased panel thickness and perforation ratio significantly enhance sound absorption.
• A strong correlation was found between absorption coefficient and material density, thickness, and perforation percentage.
• The developed regression functions accurately predict sound absorption based on physical parameters.

Conclusions:

• The predictive model provides a practical tool for acoustic design of wooden panels.
• The data-driven approach complements analytical models for acoustic optimization.
• This research facilitates the creation of wooden absorbers with tailored acoustic performance.