Experimental evaluation and ANN-based predictive modelling of rheological behavior of SiO₂ and ZnO based nanolubricants

Nanolubricants based on metal oxide nanoparticles have currently emerged as effective solutions for enhancing the rheological and tribological performance of engine oils. This study presents an experimental and artificial neural network (ANN) based investigation of rheological and lubricity behavior of 20W40 engine oil blended with silicon dioxide (SiO₂) and zinc oxide (ZnO) nanoparticles at weight concentrations of 0.05 wt%, 0.1 wt%, 0.15 wt%, and 0.2 wt%, over a temperature range of −10 °C to 70 °C. The nanolubricant samples were prepared by uniformly dispersing the nanoparticles into the base oil. Rheological experiments were conducted using an Anton Paar MCR-92 rheometer to evaluate viscosity, shear stress, and flow behavior under varying shear rates and temperatures. All nanolubricant samples exhibited non-Newtonian shear-thinning behaviour, analysed using the Herschel–Bulkley model. For SiO₂ nanolubricants, a maximum viscosity increase of 7.17% was recorded at 0.05 wt% and −10 °C, while 0.1 wt% NP provided highest viscosity reduction of 15.29% relative to the base oil. For ZnO nanolubricants, 0.05 wt% reduced viscosity by 8.5% at −10 °C, while 0.15 wt% and 0.2 wt% caused viscosity reductions of 12.9% and 13.9% respectively. Tribological performance was also assessed using an OFFITE block-on-ring tribometer, where 0.15 wt% SiO₂ and 0.2 wt% ZnO demonstrated the most favorable friction reduction. The optimal ANN configuration employing the tansig activation function with 19 neurons achieved the minimum mean square error, with R² values confirming excellent agreement with experimental data. These findings confirm that nanoparticle type, concentration, and temperature collectively govern the rheological and tribological response of 20W40 nanolubricants.