Multi-objective optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano coated tool

Abstract In the present day world, coated tools are used instead of uncoated tools for machining process, as they improve certain machining characteristics. Surface roughness (Ra) is one of the specified customer requirements in a machining process. It is essential to consider economy in addition to quality in any machining operations. Material removal rate (MRR), productivity and cost effectiveness are the desired requirements. The higher value of MRR reduces the machining time and finally productivity of the system increases. Thus in the present study, multi-objective optimization of R a and MRR is considered during turning of stainless steel 304 with uncoated and TiAlN (Titanium Aluminium Nitride) nano coated carbide tools under dry conditions. The Physical Vapour Deposition (PVD) method is used for coating carbide tool insert by TiAlN nano coating. The cutting speed, feed rate and depth of cut of turning operation are considered as the variable parameters. The machining process is done as per the experiments designed under Taguchi orthogonal array. Then on the basis of experimental results for R a and for MRR, the second-order regression equations have been developed in terms of machining parameters used. Regarding the effect of machining parameters, an upward trend is observed in R a with respect to increasing feed rate. Also as the cutting speed increases, R a value increased slightly due to chatter and vibrations. It is found that the feed rate and depth of cut are the dominant parameters with respect to the MRR. Then to test the adequacy of regression equations of response variables, additional experiments were conducted. The predicted R a and MRR values of uncoated and coated tools are found to be a close match of their corresponding experimental values. After ascertaining that the average % errors are lying within acceptable limits, the Ra and MRR equations of uncoated and coated tools are set as the objectives of multi-objective optimization problems (MOOPS) and are solved by using elitist non dominated sorting genetic algorithm (NSGA II). Finally a single best compromise solution is determined from the Pareto optimal solutions obtained by NSGA II