THE MODELLING OF SURFACE ROUGHNESS AFTER THE TURNING OF INCONEL 601 BY USING ARTIFICIAL NEURAL NETWORK

Abstract

This research includes longitudinal turning of Inconel 601 in a dry environment with PVD coated cutting inserts. Turning was performed for different levels of cutting speeds, feeds, depth of cuts and corner radius. After turning, the arithmetical mean surface roughness was measured. Mean arithmetic surface roughness values ranging from 0.156 μm to 6.225 μm were obtained. Based on the obtained results, an artificial neural network (ANN) was created. This ANN model was used to predict surface roughness after machining for different variants of input variables. Performance evaluation of the generated model was performed on the basis of additional - confirmation experiments. The mean absolute errors are 0.005 μm and 0.012 μm for the training and confirmation experiments, respectively. The mean percentage errors are 0.894 % and 1.303 % for the training and confirmation experiments, respectively. The obtained results showcase the possibility of practical application of the developed ANN model.