Prediction of surface roughness of end milling operation using genetic algorithm

In the present study, the predictive model is developed to observe the effect of radial rake angle on the end milling cutting tool by considering the following machining parameters: spindle speed, feed rate, axial depth of cut, and radial depth of cut. By referring to the real machining case study, the second-order mathematical models have been developed using response surface methodology (RSM). A number of machining experiments based on statistical five-level full factorial design of experiments are carried out in order to collect surface roughness values. The direct and interaction effects of the machining parameter with surface roughness are analyzed using Design Expert software. The optimal surface roughness value can be attained within the specified limits by using RSM. The genetic algorithm (GA) model is trained and tested in MATLAB to find the optimum cutting parameters leading to minimum surface roughness. The GA recommends 0.25 mu m as the best minimum predicted surface roughness value. The confirmatory test shows the predicted values which were found to be in good agreement with observed values.