Predicting sound transmission loss in particle-reinforced polymer composites using machine learning: A comparative study with experimental and theoretical results

Abstract

In this study, the use of machine learning (ML) models to predict sound transmission loss (STL) in particle-reinforced polymer composites has been examined. The work included extensive literature research and data extraction for training various ML models, focusing on their effectiveness in accurately predicting STL, which is crucial for evaluating acoustic performance. The method involves processing data and applying it to different ML algorithms, with the models calibrated and tested for reliability. A key aspect is comparing these models' predictions with actual experimental results and theoretical models based on the mass law in acoustics. The findings reveal the potential and limitations of ML in materials science, showing their accuracy in predicting STL and comparing them with traditional theories. This research advances the use of data-driven methods in developing and assessing acoustic materials, significantly impacting materials science and machine learning.