Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach

The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys. Here, we discuss an application of response surface methodology with Box–Cox transformation to determine the optimal cutting parameters for three surface roughness values, i.e., R_a, R_q, and R_z, in turning of titanium alloy under the NFMQL condition. The surface roughness prediction model has been established based on the selected input parameters such as cutting speed, feed rate, approach angle, and different nano-fluids used. Then the multiple regression technique is used to find the relationship between the given responses and input parameter. Further, the experimental data were optimized through the desirability function approach. The findings from the current investigation showed that feed rate is the most effective parameter followed by cutting speed, different nano-fluids, and approach angle on R_a and R_q values, whereas cutting speed is more effective in the case of R_z under NFMQL conditions. Moreover, the predicted results are comparatively near to the experimental values and hence, the established models of RSM using Box-Cox transformation can be used for prediction satisfactorily.