Multi-objective optimization of minimum quantity lubrication in end milling of aluminum alloy AA6061T6

The purpose of this research is to optimize the process of minimum quantity lubrication (MQL) in the end milling of AA6061T6 using multi-objective genetic algorithm approach. Response surface methodology coupled with a central composite design of experiments is used for modeling. Data is collected from a vertical CNC milling center and the input parameters are cutting speed, table feed rate, axial depth of cut and the minimum quantity lubrication flow rate. Analysis of variance at a 95% confidence level is implemented to identify the most significant input variables on the CNC end milling process. Optimization of the responses is done using a multi-objective genetic algorithm. A multi-criteria decision making utility is used to find among the feasible range of optimum designs for the operating parameters and the responses. An iterative multi-criteria decision making algorithm is used to find the best design among those obtained from multiobjective optimization with respect to the given conditions. The best design obtained for the equal weightage case is the design at 5252 rpm, with a feed rate of 311 mm/min, a depth of cut of 3.47 mm and MQL flow rate at 0.44 ml/min.