Modelling of Effects of Various Chip Breaker Forms on Surface Roughness in Turning Operations by Utilizing Artificial Neural Networks

In this study, the effects of different chip breaker forms and cutting parameters on the surface roughness on machined surfaces were investigated experimentally in turning of aisi 1050 steel; and values of surface roughness obtained from experiments were determined with empirical equations using artificial neural networks. the utilizing of ann was offered to determine the surface roughness depending on chip breaker forms and cutting parameters of aisi 1050 steel. the back propagation learning algorithm and fermi transfer function were used in artificial neural network. experimental measurements data were employed as training and test data in order to train the neural network created. the best fitting training data set was attained with ten neurons in two hidden layers 6 of which were at first hidden layer and 4 of which were at second hidden layer, making it possible to predict surface roughness with precision at least as good as that of the experimental error over the entire experimental range. after network training, r^2 value was found as 0.978, and average error as 0.018%. when the results of mathematical modelling are examined, the computed surface roughness is observed to be apparently within acceptable values.