Investigation of the effects of different chip breaker forms on the cutting forces using artificial neural networks

Abstract

This paper presents a new approach based on artificial neural networks (ANNs) to determine the effects of different chip breaker forms on cutting forces such as principal cutting force, feed force and passive force, in the machining of AISI 1050. The backpropagation learning algorithm and fermi transfer function were used in the network. The best fitting training data set was obtained with nine neurons in the hidden layer, which made it possible to predict cutting forces with an accuracy which is at least as good as that of the experimental error, over the whole experimental range. After training, it was found that the R² values are 0.9829, 0.9667 and 0.9492 for F_C, F_f and F_p, respectively. The average error is %0.145. As seen from the results of mathematical modeling, the calculated cutting forces are obviously within acceptable uncertainties.

 

                Keywords: Cutting forces, Chip breaker form, Artificial neural

               networks

Keywords

• Cutting forces, Chip breaker form, Artificial neural networks

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