Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: An aluminium alloy 5083 case study

Abstract

A mastering of surface quality issues during machining helps avoiding failure, enhances component integrity and reduces overall costs. Surface roughness significantly affects the quality performance of finished components. A number of parameters, both material and process oriented, influence at a different extend the surface quality of the finished product. Aluminium alloy 5083 component surface quality, achieved in side end milling, constitutes the subject of the present case study. The design of experiment method is employed: that is, 18 carbide two-flute end mill cutters – manufactured by a five-axis grinding machine – have been assigned to mill 18 pockets in finishing conditions – having different combinations of geometry and cutting parameters values, according to the L18 (21× 37) standard orthogonal array. Process performance is estimated using the statistical surface texture parameters Rα, Ry and Rz– measured during three different passes on the side surface of the pockets. The results indicate that process parameters – such as the cutting speed, the peripheral second relief angle and the core diameter – mostly influence surface texture. The experimental values are used to train a feed forward back-propagation artificial neural network for the prediction of the yield surface roughness magnitude.