Prediction of Cutting Forces Using ANNs Approach in Hard Turning of AISI 52100 steel

In this study, artificial neural networks (ANNs) was used to predict cutting forces in the case of machining the hard turning of AISI 52100 bearing steel using cBN cutting tool. Cutting forces evolution is considered as the key factors which affect machining. Predicting cutting forces evolution allows optimizing machining by an adaptation of cutting conditions. In this context, it seems interesting to study the contribution that could have artificial neural networks (ANNs) on the machining forces prediction in both numerical and experiment studies. Feed-forward multi-layer neural networks trained by the error back-propagation (BP) algorithm are used. Levenberg-Marquardt (LM) optimization algorithm was used for finding out weights. The training of the network is carried out with experimental machining data. The input dataset used are cutting speed, feed rate, cutting depth and hardness of the material. The output dataset used are cutting forces (F-t-cutting force, F-a-feed force and F-r-radial force). Results of the neural networks approach, in comparison with experimental data are discussed in last part of this paper.