Performance Improvement of Nanofluid Minimum Quantity Lubrication (Nanofluid MQL) Technique in Surface Grinding by Optimization Using Jaya Algorithm

Abstract

The recent industries are more concise about clean, green, and sustainable machining process for better quality and productivity. The conventional cutting fluid is gradually replaced by nanofluid due to heat transfer and the lubricating properties of nanoparticles. The effect of material hardness on grinding performance in terms of surface roughness is determined under different cooling environments such as conventional flooded, MQL, and Nanofluid MQL. The result shows that surface finish of hard material is obtained better at 0.30 vol.% concentration of nanofluid compared to conventional flooded, MQL and 0.15 vol.% concentrations of Nanofluid MQL process. In present work, the modeling and optimization of process parameters for EN 31 soft material are carried out using Jaya algorithm to improve the process performance. The process parameters such as table speed, depth of cut, dressing depth, coolant flow rate, and nanofluid concentration are considered the input parameters and surface roughness as the response parameter for model development. The optimal values obtained by Jaya algorithm for soft steel material in Nanofluid MQL process are table speed (7000 mm/min), depth of cut (20 µm), dressing depth (10 µm), coolant flow rate (750 ml/hr), and nanofluid concentration (0.22 vol.%). The result shows that Nanofluid MQL process significantly reduced the surface roughness by 14% over the conventional technique. The confirmation experiments are conducted to validate the regression model by comparing the experimental results with predicted values obtained from Jaya algorithm at optimal setting.