Optimization of cutting conditions for end milling of Ti6Al4V Alloy by using a Gravitational Search Algorithm (GSA)

Surface roughness is commonly used to indicate the quality of machine parts. Optimizing cutting parameters throughout the machining process is an important aspect for manufacturers, as it allows them to achieve a minimum surface value. During this study, a new optimization technique known as the gravitational search algorithm (GSA) was employed in order to achieve minimum surface roughness when end milling a Ti6Al4V alloy under dry cutting conditions, with both PVD coated and uncoated cutting tools. Regression models have been created based on the results of real experimental data. Through use of SPSS software, it was possible to formulate the objective (fitness) functions which were used in the GSA optimization for each cutting tool. A MATLAB code was then created to instigate the optimization process. The results indicated that high cutting speed and low feed rate and depth of cut could result in a minimum surface roughness value of (0.6255 μm), based on the objective function for the PVD cutting tool. Alternatively, surface roughness of around (0.4165 μm) could be achieved by using an uncoated tool on a lower feed rate, depth of cut and cutting speed. The same GSA technique was used in another case study optimized by Genetic algorithm (GA). The GSA achieved the same results, and proved that it is faster than GA: GSA could reach the optimum solution in the third iteration; GA could only reach it in the 67th.