AI-Driven Rheological and Tribological Performance Modeling of Transmission Oil Blended with Castor Oil and Enhanced with CeO2 and MWCNTs Additives for Sustainable Lubrication Systems

This study examines the rheological and tribological behavior of bio-based nano-lubricants enhanced with cerium oxide (CeO2) and multi-walled carbon nanotubes (MWCNTs), alongside the application of artificial intelligence (AI) models for performance prediction. Rheological results confirmed non-Newtonian, shear-thinning behavior across all formulations. CeO2-based lubricants exhibited significantly higher viscosities at 40 °C (up to ~3700 mPa·s at low shear), which decreased sharply with shear, indicating strong particle interactions. In contrast, MWCNT-based lubricants maintained moderate viscosities (90–365 mPa·s at 40 °C) with improved flowability due to nanotube alignment. At 100 °C, both systems showed viscosity reduction, stabilizing between 8 and 18 mPa·s, which favors pumpability in high-temperature applications. Tribological testing revealed distinct performance characteristics. CeO2 lubricants showed slightly higher coefficients of friction (0.144–0.169) but excellent wear resistance, achieving the lowest wear rate of 1.66 × 10−6 mm3/N-m. MWCNT-based lubricants offered stable and lower CoF values (0.116–0.148) while also providing very low wear rates, with MCO6 achieving 1.62 × 10−6 mm3/N-m. However, ternary blends (C20T80 and M20T80) displayed moderate CoF but significantly higher wear rates (up to 2.92 × 10−5 mm3/N-m), suggesting that blending improves dispersion but weakens tribo-film stability. To complement the experimental findings, support vector regression (SVR), artificial neural networks (ANN), and AdaBoost algorithms were employed to predict key performance parameters based on compositional and thermal input data. The models demonstrated high prediction accuracy, validating the feasibility of AI-driven formulation screening. These results highlight the complementary potential of CeO2 and MWCNT additives for high-performance bio-lubricant development and emphasize the role of machine learning in accelerating material optimization for sustainable lubrication systems.