Roja Abraham Raju

This study evaluates the performance of a multi-walled carbon nanotube (MWCNT)-based nanofluid as an alternative cutting fluid in the turning of EN31 steel. Experiments were conducted under dry, conventional coolant, and nanofluid-assisted conditions using a gravity-feed lubrication system with a flow rate of 1 L/h. The results indicate that the nanofluid improves machining performance, with reductions of 5–20% in cutting force, 11–37% in thrust force, 4–33% in feed force, and 2–34% in surface roughness compared to conventional cooling. Response Surface Methodology (RSM) with Central Composite Design was applied to develop predictive models for surface roughness and resultant cutting force. The models showed good statistical significance and prediction accuracy. Multi-objective optimization was performed to minimize surface roughness and cutting force while maximizing material removal rate. The optimal parameters were identified as 1500 rpm cutting speed, 0.10 mm/rev feed rate, and 1 mm depth of cut, resulting in surface roughness of approximately 0.47 μm, cutting force of 165 N, and material removal rate of 7.54 mm³/min. Comparative analysis of optimization algorithms showed that NSGA-II provided superior convergence and stability, achieving better performance than other methods. The findings confirm the effectiveness of nanofluid-assisted machining combined with intelligent optimization for improved machining outcomes.

Nanofluid-assisted machining is a promising sustainable alternative to the conventional cutting fluids; however, the correct prediction and optimization of the latter process is still difficult due to nonlinearities inherent to it and the lack of experimental data. Hence, the current study presents an innovative hybrid generative deep-learning model that synergistically integrates Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Generative Adversarial Network (GAN) to predict intelligently and optimize multi-fold in the nanofluid-assisted turning of EN31 steel. It has been experimentally shown that MWCNT based gravity fed nanofluid decreases the surface roughness by 12.83% and cutting force components by up to 23.4% when compared to traditional coolant. The RSM model has a moderate predictive power (R ² ≈ 0.82–0.88) and ANN model outperforms the RSM model (R ² ≈ 0.95–0.97). Under the limited data condition, the GAN-ANN hybrid has higher predictive robustness with R ² ≈ 0.97–0.99 and lowest mean squared error (1.49 for roughness and 7.89 for cutting forces) than any of the other models tested. Using a hybrid ANN-RSM strategy based on desirability, best machining parameters predict a cutting force of about 205 N, a surface roughness of 0.45 μm and a maximum rate of material removal of 16.6 m ³ /min, which are later confirmed in follow-up experiments with an error of less than 5%. Therefore, the suggested framework will provide a scalable, interpretable and environmentally friendly decision support system to intelligent machining applications.

Machining of titanium alloys generate very high temperature in the cutting zone. This results in rapid tool wear and poor surface properties. Therefore, improvement in cutting performance in machining of titanium alloys is very much dependent on effectiveness of the cooling strategies applied. In the present work, performance of nanofluid using multiwalled carbon nanotubes (MWCNTs) dispersed in distilled water and sodium dodecyl sulfate (SDS) as surfactant is evaluated for turning operation on Ti–6Al–4V workpieces. Turning operations were carried out under three different conditions – dry, with conventional cutting fluid and with nanofluid. Nanofluid application was limited to 1 L/h and it was applied at the tool tip through gravity feed. Various machining responses like cutting force, surface finish and tool wear were analyzed while turning at optimum cutting parameters as 150 m/min, 0.1 mm/rev and 1 mm depth of cut. Later on, machining performance of nanofluid is confirmed at low cutting speed of 90 m/min. Nanofluid outperformed conventional cutting fluid with 34% reduction in tool wear, average 28% drop in cutting forces and 7% decrease in surface roughness at cutting speed of 150 m/min.

This paper evaluates the performance of nanofluid using multi-walled carbon nanotubes (MWCNT) in distilled water and sodium dodecyl sulfate surfactant for turning operation on EN 31 material. Turning was performed without any fluid, with conventional, mineral oil–based cutting fluid, and with nanofluid. The flow rates of both fluids were limited to 1 L/h and these fluids were applied at the tool tip through gravity feed. Cutting forces, wear on tool, and surface finish on workpiece were measured as responses while turning under the three conditions. The responses obtained in three different conditions of turning are then compared. It is found that application of MWCNT-based nanofluid resulted in 49% and 30% lesser tool wear than machining without any fluid and machining with mineral oil–based fluid, respectively. The use of nanofluid also resulted in 5–8% lesser cutting force and 9–22% better surface finish of the workpiece as compared with conventional cutting fluid. Thus, MWCNT-based nanofluid performed better than the conventional, oil-based cutting fluid for turning of EN 31 bars.

This article presents the results of experiments performed to evaluate properties of dispersion of multiwalled carbon nanotubes (MWCNT) in water with sodium dodecyl sulfate (SDS) as a dispersant. Different samples of varying concentrations of MWCNTs were prepared for the analysis and properties including thermal conductivity, pH value, viscosity, wettability, etc., were evaluated. These properties were compared with the properties of conventional cutting fluid, which was taken as a mix of water and mineral oil. It was found that the thermal conductivity of the MWCNT dispersion was higher than the conventional cutting fluid by about 42%. There was a decrease in contact angle by about 70%. Thus, dispersing MWCNTs in water with SDS increases the thermal conductivity and wettability of fluid. The pH value of fluid with 0.2 vol% MWCNTs was found to be 8.4. It thus reduced the corrosive nature of water. Nanoparticles of MWCNTs did not have much influence on the viscosity of the base fluid. Thus, the use of MWCNTs in water with SDS appeared to result in a better cutting fluid for machining than conventional cutting fluid.