Use of Neural Networks in Prediction and Simulation of Steel Surface Roughness

Researches on machined surface roughness prediction in the face milling process of steel are presented in the paper. The data for modelling by the application of neural networks have been collected by the central composite design of experiment. Input variables are the parameters of machining (number of revolutions – cutting speed, feed and depth of cut) and the way of cooling, while the machined surface roughness is output variable. In the modelling process the algorithms Back-Propagation Neural Network, Modular Neural Network and Radial Basis Function Neural Network have been used. Various architectures of neural networks have been investigated on a data sample and they have generated the prediction results which are at the RMS (Root Mean Square) error level of 5.24 % in the learning phase (8.53 % in the validation phase) for the Radial Basis Function Neural Network, 6.02 % in the learning phase (8.87 % in the validation phase) for the Modular Neural Network and for the Back-Propagation Neural Network 6.46 % in the learning phase (7.75 % in the validation phase). (Received in September 2012, accepted in June 2013. This paper was with the authors 3 months for 2 revisions.)