Prediction and optimization approaches for modeling and selection of optimum machining parameters in CNC down milling operation

Abstract

In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK- (Ø10*20C*10D*75L) -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro-Fuzzy Inference System (PSOANFIS) is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness. © Maxwell Scientific Organization, 2014.