High speed CNC machining of AISI 304 stainless steel; Optimization of process parameters by MOGA

Abstract

This work is to establish the relationship with the basic parameters to the responses namely Surface roughness (Ra) and Material Removal Rate (MRR). The Taguchi based Box-Behnken RSM (Response Surface Methodology) method is used to develop prediction formula and Multi Objective Genetic Algorithm (MOGA) is used for High speed CNC milling process optimization with higher Spindle speed, Feed rate and Depth of cut for better surface finish and material removal rate. The Ra and MRR is resultant of various controllable process parameters are Spindle speed, Feed rate and Depth of Cut, Hardness of the material, wet or dry machining, type of insert, and Dynamic forces on the job, tool wear rate with Cutter geometry. The need for scientific selection of machining parameters, conditions and the most suitable type of cutting tool has been felt over the years to eliminate the human intervention to reduce the errors and to improve productivity of the system. High speed CNC milling process is scientifically optimized using this method formulating a mathematical model that relates the surface roughness and MRR with cutting parameters in end milling, precisely to the Spindle speed, Feed rate, Depth of cut and insert type, which reduces human intervention on the process. Keywords: CNC milling, optimization, surface finish, DOE, ANOVA, material removal rate, Box-Benkhen method, genetic algorithm, stainless steel