Machine Learning Based Prediction of Mechanical Properties of Unsaturated Polyester Resin Nanocomposites

Abstract

The use of polymer nanocomposites has become key material in membrane applications, especially unsaturated polyester resin (USPER) based nanocomposites because of their cost effectiveness and tune-able characteristics. Experimental characterization of mechanical properties over a range of reinforcement concentrations is both time-consuming and resource intensive. This paper introduces a detailed machine learning (ML) model in predicting the mechanical properties of egg shell biomaterial-reinforced and nano sand filler-reinforced USPER nanocomposites. At five weight loadings that were expressed as a percentage, four important mechanical properties were studied. Various regression models were tested in a systematic manner to determine the best predictive models of each property. The developed models demonstrated strong fitting performance within the experimentally investigated range, with R2 values above 0.99 for all properties. Ridge Regression showed consistent predictive capability across all investigated properties while providing the advantages of implementation simplicity and regularization. The developed models enabled exploratory trend-based predictions beyond the experimentally investigated reinforcement range. The proposed ML-assisted methodology may help reduce excessive experimental trials and support preliminary material design optimization for membrane-related applications, while additional experimental validation is required for higher reinforcement concentrations.