Enhancing tribological performance of hybrid fiber-reinforced composites through machine learning and response surface methodology

Abstract

This study delves into the significant effects of sodium hydroxide (NaOH) treatment on the tribological properties of hybrid fiber-reinforced composites, specifically focusing on the combination of paddy straw (PS) and pineapple leaf (PALF) in a polyester matrix. By leveraging Artificial Neural Networks (ANNs) to predict the Specific Wear Rate (SWR) and Coefficient of Friction (COF), the research employs a grid search approach for hyperparameter optimization. This optimization process results in an optimal ANN architecture with impressive accuracy, showcasing low mean absolute error and high R-squared values of 0.991 and 0.986 for SWR and COF predictions, respectively. Utilizing the Design of Experiments (DOE), the study systematically analyzes the intricate interplay of disc speed, wear duration, and NaOH treatment percentage, with a specific focus on SWR and COF as pivotal tribological metrics. The Analysis of Variance (ANOVA) results underscore the substantial impact of duration and treatment percentage on wear characteristics. Additionally, quadratic regression models reveal nuanced correlations, highlighting the sensitivity of SWR to NaOH percentage and the influence of disc speed, duration, and treatment percentage on COF. This outcome emphasizes the efficacy of these parameters in achieving superior tribological performance in hybrid composites. Beyond contributing to a profound understanding of wear characteristics, this work introduces an innovative dimension through optimized ANN modeling, ensuring a more accurate and fine-tuned predictive model.