Abstract
Surface roughness prediction models using artificial neural network (ANN) are developed to investigate the effects of cutting conditions during turning of free machining steel, 9SMnPb28k(DIN). The ANN model of surface roughness parameters ( R a and R t) is developed with the cutting conditions such as feed rate, cutting speed and depth of cut as the affecting process parameters. The experiments are planned as per L 27 orthogonal array with three levels defined for each of the factors in order to develop the knowledge base for ANN training using error back-propagation training algorithm (EBPTA). 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on surface roughness parameters. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the depth of cut has the least effect. The details of experimentation, ANN training and validation are presented in the paper.
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