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Biosilica and clove oil-modified banana fiber epoxy composites: mechanical, thermal, wear, and drilling performance with machine learning and RSM optimization

Lavanya Dhanapalan, Dhavamani Chinnathambi, V. Subramanian, Ravikumar Natarajan

2025Composite Interfaces5 citationsDOI

Abstract

This research reports the fabrication and characterization of a sustainable banana fiber – reinforced epoxy composite modified with clove oil and biosilica derived from corn cob waste. Alkali-treated banana fibers were embedded in an epoxy matrix toughened with clove oil and reinforced with biosilica, yielding eco-friendly laminates with superior multifunctional performance. Among the tested compositions, the B5 laminate (25% fiber, 3% biosilica, 15% clove oil) exhibited the best balance of properties, achieving tensile strength of 132.51 MPa, flexural strength of 186.4 MPa, and impact toughness of 5.71 J. Thermogravimetric analysis confirmed enhanced thermal stability, while biosilica incorporation reduced water absorption and improved flame resistance, achieving a UL-94 V-0 rating. Tribological evaluation revealed a low wear rate and a reduced coefficient of friction. The drilling behavior of B5 was optimized using Response Surface Methodology (RSM) and validated through XGBoost machine learning, both demonstrating high predictive accuracy (R2 > 0.96). The optimized parameters (4 mm drill, 1024.59 rpm, 0.100 mm/rev) minimized thrust force (18.02 N), torque (0.95 Nm), and delamination (0.79 mm). Overall, the study highlights the synergistic role of biosilica and clove oil in enhancing natural fiber composites and demonstrates the effectiveness of AI-driven optimization for sustainable machining applications.

Topics & Concepts

Materials scienceEpoxyComposite materialResponse surface methodologyDrillingFiberMachine learningMachiningNatural fiberComposite numberPulp and paper industryNatural Fiber Reinforced CompositesTribology and Wear AnalysisSurface Treatment and Coatings
Biosilica and clove oil-modified banana fiber epoxy composites: mechanical, thermal, wear, and drilling performance with machine learning and RSM optimization | Litcius