AISI 1040 steel is extensively used in structural and automotive applications, where surface integrity plays a significant role in service performance and coating adhesion. Furthermore, the selected cutting fluids are expected to effectively reduce surface roughness and tool wear by improving lubrication at the tool and workpiece interface. This study investigates the influence of SiO2 nanoparticle-assisted Pongamia pinnata oil on surface roughness and tool wear during the machining of AISI 1040 steel using an uncoated tungsten carbide tool by varying nanoparticle concentration (Vol.%), cutting speed (m/min), depth of cut (mm), and feed rate (mm/rev). The incorporation of 0.5 (Vol.%) SiO2 nanoparticles significantly enhances machining performance by improving surface finish and reducing tool wear. Further, a minimum surface roughness value of 1.95 microns and tool wear value of 0.047 mm were achieved at a cutting speed of 101 m/min, feed rate of 0.11 mm/rev, depth of cut of 0.25 mm and 0.5 (Vol.%) SiO2 nanoparticle concentration. ANOVA results indicate that nanoparticle concentration is the most dominant parameter affecting both surface roughness and tool wear, contributing 85.35% to the variation in surface roughness and 82.2% to the total variation in tool wear. Cutting speed is the second most influential factor, accounting for 11.63% of surface roughness variation and 11.07% of tool wear variation, while feed rate and depth of cut exhibit minimal influence in both cases. A second-order RSM model was developed to predict surface roughness and tool wear, showing excellent agreement with experimental results. The model predicted surface roughness with an average error below 2.43%, while the second-order model for tool wear exhibited an average prediction error of 4.95%, confirming its statistical significance and predictive reliability. Desirability Function Method (DFM) analysis yielded a desirability value of 1.000, confirming the optimal combination of machining parameters at 0.5354 (Vol.%) nanoparticle concentration, a cutting speed of 45 m/min, a depth of cut of 0.50 mm, and a feed rate of 0.1298 mm/rev. Overall, this study demonstrates that 0.5 (Vol.%) SiO2 nanoparticle-incorporated Pongamia pinnata oil is an effective and sustainable cutting fluid, significantly improving surface integrity and machining performance of AISI 1040 steel during machining. Under these settings, the predicted tool wear was 0.0614 mm, accompanied by a high composite desirability value of 0.92786, indicating excellent overall performance. Moreover, the close agreement between experimental, response surface model and BP-ANN-predicted tool wear and surface roughness confirms that the ANN model reliably and robustly captures the complex, nonlinear effects of machining parameters with minimal systematic error.
P et al. (Sat,) studied this question.