A study on optimisation of cutting parameters and prediction of surface roughness in end milling of aluminium under MQL machining

Abstract

The aim of this study is to find the influence of cutting parameters on surface roughness and optimum conditions for better surface quality in end milling of aluminium 6063 under minimum quantity lubrication (MQL). The most important parameters like cutting speed, depth of cut and feed rate are considered. Taguchi experimental design method is applied to conduct the experiments. This study also attempts to develop models to predict surface roughness. Multiple regression and artificial neural network (ANN) techniques are applied to predict the surface roughness. The results of the prediction models are quite close with experiment values. ANOVA is carried out and the influence of cutting parameters on surface roughness is found. The feed rate is the most dominant factor in influencing surface roughness. The results also show that the highest cutting speed, medium feed rate and medium depth of cut produces lowest surface roughness. This study provides the optimum cutting conditions for end milling of aluminium 6063 under minimum quantity lubrication machining.