Sustainable Composites from Sugarcane Bagasse Fibers and Bio-Based Epoxy with Insights into Wear Performance, Thermal Stability, and Machine Learning Predictive Modeling
Mahima Samanth, Pavan Hiremath, G. Divya Deepak, Nithesh Naik, H S Arunkumar, Srinivas Shenoy Heckadka, R. C. Shivamurthy
Abstract
The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear testing, thermal and structural characterization, and machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited the lowest wear rate (0.245 mg/m) and the highest thermal stability (T20% = 260 °C, char yield = 1.3 mg), highlighting the role of optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated fibers achieved the highest crystallinity index of 62%, underscoring the importance of structural alignment in fiber-matrix bonding. Machine learning insights using a Random Forest model identified fiber treatment as the most significant parameter influencing wear performance, with accurate predictions validated through experimental results. This work demonstrates the transformative potential of sugarcane bagasse fibers in sustainable polymer composites, offering a pathway for environmentally friendly, lightweight, and durable material solutions. These findings integrate experimental rigor with computational insights, paving the way for advancements in natural fiber-based composite technologies.