Analysis of high-speed CNC milling of Ti-6Al-4V by multi-objective crow optimisation and multi-objective PSO

Ti-6Al-4V is extensively utilised as a functionally advanced material in different fields. Poor mechanisation and absence of machining innovation are major issues in the application of Ti-6Al-4V. In this research, analysis of variance (ANOVA) and regression analysis have been used to make input-output relationships. The optimisation method was applied to get the maximum material removal rate (MRR) and minimum surface roughness (SR). These responses were optimised simultaneously and composed as a multi-objective optimisation issue. In multi-objective optimisation, the effects of both responses were taken by the grey correlation analysis (GRA) method. This multi-objective optimisation issue has been solved applying two metaheuristic algorithms, namely, the crow optimisation algorithm, and the particle swarm optimisation (PSO). The optimisation results show great concurrence with the response surface methodology (RSM) results. Atomic force microscopy (AFM) was also employed to visualise the impact of procedure parameters on the surface topography.