Abstract

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 optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and nonlinear. In this work, machining process was carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness was measured using Surface Roughness Tester. To predict the surface roughness, an artificial neural network (ANN) model was designed through 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 ANN was used confidently. The results obtained, conclude that ANN is reliable and accurate for solving the cutting parameter optimization. 

Key words: CNC turning process, non-ferrous material, surface roughness, artificial neural network (ANN), optimization.