The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

• Authors: Muammer Nalbant;Hasan Gökkaya;İhsan Toktaş;Gökhan Sur
• Venue: Robotics and Computer-Integrated Manufacturing
• Year: 2009

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45mm/rev with the cutting speeds of 100, 200 and 300m/min by keeping depth of cuts constant (i.e. 2mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools-with 200mm/min cutting speed and 0.25mm/rev feed rate-the TiN coated with PVD method has provided 2.16@mm, TiAlN coated with PVD method has provided 2.3@mm, AlTiN coated with PVD method has provided 2.46@mm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100m/min and 0.25mm/rev feed rate has yielded the surface roughness value of 2.45@mm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R^2) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.