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Surface Roughness Modelling and Optimization in CNC End Milling using Taguchi Design and Neural Networks

A Neural Network modelling approach is presented for the prediction of surface texture parameters during end milling of aluminium alloy 5083. Eighteen carbide end mill cutters were manufactured by a five axis grinding machine and assigned to mill eighteen pockets having different combinations of geometry parameters and cutting parameter values, according to the L18 (2x3) standard orthogonal array. A feedforward back-propagation NN was developed using data obtained from experimental work conducted on a CNC milling machine center according to the principles of Taguchi’s design of experiments method. It was found that NN approach can be applied easily on designed experiments and predictions can be achieved, fast and quite accurately.