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BackgroundMachining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality.MethodsIn this work, the influence of important common machining process variables like feed, cutting speed and axial depth of cut on the output parameters such as surface roughness and amplitude of tool vibration levels in Al-6061 workpieces has been studied. With the use of experimental result analysis and mathematical modelling, correlations between the cutting process conditions and process outputs are studied in detail. The cutting experiments are planned with response surface methodology (RSM) using Box-Behnken design (BBD).ResultsThis work proposes a multi-objective optimization approach based on genetic algorithms using experimental data so as to simultaneously minimize the tool vibration amplitudes and work-piece surface roughness. The optimum combination of process variable is further verified by the radial basis neural network model.ConclusionsFinally, based on the multi-objective optimization approach and neural network models an interactive platform is developed to obtain the correct combination of process parameters.

Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach