Parametric analysis and optimization of nanofluid minimum quantity lubrication micro-drilling process for titanium alloy (Ti-6Al-4V) using response surface methodology and desirability function

Abstract

This paper discusses the optimization of nanofluid MQL micro-drilling process of titanium alloy (Ti-6Al-4V) using nanodiamond particles based on a response surface methodology (RSM) and desirability function (DF). In order to obtain regression models of drilling torques, thrust forces and edge radii in terms of process parameters such as drill diameter, feed rate, spindle speed and nanofluid weight concentration, a series of micro-drilling experiments are performed by using a design of experiment (DOE) approach. Then, the multi-objective optimization for minimizing drilling torques, thrust forces and edge radii is carried out by introducing DF, and the optimal values of the process factors are obtained. The micro-drilling experiments with the optimal process factors are conducted, and the experimental results of drilling torque, thrust force and edge radius are similar to calculated ones. Thus, the validity of the regression models of drilling torques, thrust forces and edge radii are demonstrated. The developed regression models can be used to find dominant parameters influencing the drilling performances and to practically guide operators to choose optimal values for the enhanced drilling performances.