Preparation of PPA based composite reinforcing with glass beads and clays: Investigation of sound absorbing

Abstract

The research investigates the sound absorption properties of potassium polyacrylate (PPA) composites, particularly those augmented with clay and porous hollow glass beads within a hydrogel template. This unique material combination shows promise for efficient sound absorption, relevant in industries requiring effective noise control like acoustic engineering and construction. Experimental assessment focused on measuring the sound absorption coefficient, crucial for quantifying a material’s ability to absorb sound energy across various frequencies. Incorporating clay and porous hollow glass beads introduces complexities, emphasizing the need for precise acoustic performance prediction. Collected data from sound absorption coefficient measurements formed the basis for training an Artificial Neural Network (ANN) model. Leveraging the ANN’s pattern recognition capabilities, the model learned from diverse composite compositions, enabling accurate prediction of sound absorption coefficients for varying material compositions. This predictive model streamlines material design, offering a systematic approach to tailor composite acoustic characteristics. Integration of machine learning, particularly ANNs, enhances accuracy and expedites material design and optimization, contributing to innovative and customizable sound-absorbing materials for diverse industrial applications.