Effect of hBN and SDS added vegetable based cutting fluid application on the performance of turning Ti6Al4V alloys: A Comparative analysis with Taguchi and ANN approaches

Abstract

In this study, during the turning process of Ti6Al4V alloy, which is difficult to machine, nanofluid, whose base fluid is vegetable oil (sunflower oil), was applied to the cutting zone with the minimum quantity lubrication (MQL) technique. Nanofluids were prepared by adding hexagonal boron nitride (hBN) nanoparticles (65–75 nm) with different concentration ratios (0.5 and 1 %) and surfactant (Sodium dodecyl sulfate, SDS) into vegetable oil. Thermal conductivity coefficients and dynamic viscosities of the prepared nanofluids and pure sunflower oil were measured at four different temperatures. Additionally, the stability of the prepared nanofluids was observed for 15 months. In turning experiments, four different cutting speeds (50, 75, 100 and 125 m/min), four different feed rates (0.05, 0.10, 0.15 and 0.20 mm/rev) and four different cooling conditions (dry, PureMQL, 0.5 % NanoMQL and 1 % NanoMQL) were used. The effect of the change in these parameters on surface roughness (Ra), temperature in the cutting zone (T) and flank wear on the tools (Vb) was investigated experimentally and analytically. As a result of the research, the experimental results were evaluated using variance analysis, signal/noise (S/N) analysis and artificial neural networks (ANN) methods. As a result of the measurements and observations, it was determined that the nanofluid with 0.5 % concentration had the best stability. In the main experiments, the MQL condition using this nanofluid (0.5 % NanoMQL) exhibited the best machining performance. In the wear experiments, the lowest tool wear was detected under the 1 % NanoMQL condition. According to the ANOVA and S/N analysis results showed that Ra was most affected by the feed rate (95 %), T was most affected by the cooling condition (70 %), and Vb was most affected by the cutting speed (45 %). The prediction performances of ANN and Taguchi approaches were examined and the prediction success of these approaches was more than 98.5 % and 80.8 %, respectively. Especially for Vb values that have very close data, more accurate predictions were made with the ANN approach compared to Taguchi.