Investigation of cutting parameters of surface roughness for brass using artifi cial neural networks in computer numerical control turning

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed and depth of cut) are required. So it is necessary to find a suitable optimisation method that can find optimum values of cutting parameters for minimising surface roughness. The turning process parameter optimisation is highly constrained and non-linear. In this work, the machining process has been carried out on brass C26000 material in a dry cutting condition in a computer numerical control turning machine. Surface roughness has been measured using a surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through a back propagation network using Matlab 7 software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference, and the ANN has been used confidently. The results obtained conclude that the ANN is reliable and accurate for solving the cutting parameter optimisation.