Abstract
Abstract
Over the past two decades, nano additive lubricants have become essential in manufacturing as lubricating agents. Our study examines the impact of three process parameters—carbon nanotube (CNT) (volume concentration,%), sliding velocity (m/s), and applied load (N)—on the tribological performance of polyolester oil blended with carbon nanotubes. By employing the robust Taguchi L9 orthogonal array as the design of experiment, the current study made an attempt to identify the best combination of these three factors parameters to achieve the least coefficient of friction (COF) while the study also conducted ANOVA and multivariate linear regression to determine the significant factor that determines the least COF. For this study, POE oil and varying concentrations of CNTs (such as 0.05, 0.075 and 0.1 volume concentration%) were used. For this study, the characterization of the CNTs was performed using TEM, SEM and XRD methods while its stability was validated through Zeta potential value i.e., 0.075 volume concentration% CNT concentration achieved 35 mV zeta potential value. The Taguchi L9 orthogonal array outcomes found the least COF i.e., 0.0359 was achieved from 0.075 volume concentration % of CNT with a sliding speed of 3.6 m s−1 at 50 N load. The ANOVA outcomes confirmed the major contribution (91%) of the CNT concentration towards influencing the COF outcomes. The contour plots confirmed that optimal COF can be achieved when using 0.075 volume concentration% CNT with load ranged from 75 N to 125 N and sliding velocities between 1.2 m s−1 and 3.0 m s−1. The outcomes establish that when POE oil is supplemented with CNTs, it can achieve superior performance as the nanolubricant mitigates the coefficient of friction (COF), eventually enhancing the tribological performance. Future researchers can focus on employing Taguch-grey relational analysis, artificial intelligence and machine learning models to find the optimal process parameters for other lubricants and nanoadditives.