Optimization of Wear Behavior Analysis in MWCNT-Reinforced GFRP Composites Using RSM and ETLBO

The present study aims to optimize the fabrication processing of Multi-Walled Carbon Nanotube (MWCNT)-filled glass fiber-reinforced polymer (GFRP) composites to enhance their tribological performance during the hand layup process. A structured experimental approach based on the Box-Behnken design within the Response Surface Methodology (RSM) framework was used to evaluate the influence of wt% MWCNT loading, applied load, and sliding velocity. The fabrication was performed, ensuring a uniform MWCNT dispersion via mechanical stirring and probe sonication. The performance of the composites was assessed in terms of their specific wear rate (SWR) and wear coefficients (WC). Statistical analysis, including ANOVA and Pareto charts, revealed that the load and sliding velocity significantly affected both the SWR and WC, whereas interactions such as the MWCNT content and sliding velocity also played a crucial role. Mathematical models were developed for SWR and WC and optimized using Elitist Teaching-Learning-Based Optimization (ETLBO). The optimal fabrication settings were found to be 1wt% MWCNT loading, 30 N load, and 2 m/s sliding velocity, yielding improved wear characteristics. Confirmatory tests validated the model predictions and showed minimal errors. Scanning electron microscopy further confirmed that optimal nanotube dispersion led to a more uniform and durable composite structure, supporting its potential for high-performance engineering applications. The proposed integrated approach demonstrates the effectiveness of combining RSM and ETLBO to develop high-performance MWCNT-GFRP composites with superior wear resistance. The findings of this study provide practical guidelines for fabricating durable, wear-resistant composite structures for automotive brake systems, aerospace joints, and industrial machinery parts, where reliability under continuous sliding conditions is essential.