Title: A study on optimisation of cutting parameters and prediction of surface roughness in end milling of aluminium under MQL machining
Authors: K. Sundara Murthy, I. Rajendran
Addresses: Department of Mechanical Engineering, Jayam College of Engineering and Technology, Dharmapuri, Tamil Nadu, India. ' Department of Mechanical Engineering, Dr. Mahalingam College of Engineering and Technology, Udumalai Road, Pollachi, Tamilnadu, India
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.
Keywords: optimisation; cutting parameters; surface roughness; ANOVA; artificial neural networks; ANNs; multiple regression; end milling; Taguchi methods; minimum quantity lubrication; MQL; aluminium machining; cutting speed; depth of cut; feed rate; experimental design.
International Journal of Machining and Machinability of Materials, 2010 Vol.7 No.1/2, pp.112 - 128
Published online: 02 Dec 2009 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article