Prediction of mechanical properties of <i>Newboulia laevis</i> particulate composite material using artificial neural network (ANN)

Abstract

Abstract The growing interest in using plant-based materials as substitutes for synthetic ones in the polymer composites industry is primarily driven by the negative health and environmental impacts of synthetic materials. However, the effectiveness and application of these composites are often hindered by poor material design. This research focused on extracting and processing stem fibers from the Newbouldia laevis plant into particles, followed by the creation of composites with varying particulate weight contents (wts). Mechanical testing showed that the composites had a maximum tensile strength of 31.3496 N mm −2 for those containing 10 wt.%, a maximum compression strength of 59.8716 N mm −2 for those with 40 wt.%, and a maximum flexural strength of 36.5808 N mm −2 for those with 10 wt.% particle content. The experimental results were analyzed using artificial neural networks (ANNs) to create a precise predictive model aimed at improving material design. The reliability of the ANN models was assessed using various performance metrics such as mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), normalized root MSE (NRMSE), and the coefficient of determination ( R 2 ). The results showed low error rates for MSE, MAE, MAPE, and NRMSE, and a high R 2 value exceeding 0.9, indicating that the model predictions are highly accurate. These results demonstrate that ANN can be an effective mathematical tool for modeling and predicting the mechanical properties of reinforced polymer composites, ultimately saving both time and resources in material design research.