Modelling of Surface Roughness Performance of Coated Cemented Carbide Groove Cutting Tool Via Artificial Neural Networks

The objective of the presented study is to model the effects of cutting speed, feed rate and depth of cut on the surface roughness (roughness average, Ra) in the turning process carried out by the grooving cutting tool by using Artificial Neural Network (ANN). To realize this aim, twenty seven specimens are machined at the cutting speeds of 100, 140 and 180m/min, feed rates of 0.05, 0.15 and 0.25mm/rev, and cutting depth of 0.6, 1.3 and 2mm in wet conditions. Data from these experiments are used in the training of ANN. When we compare the experimental results with the ANN ones, it is observed that proposed method is applied with an error rate of 8.14% successfully.