Dynamic neural network approach for tool cutting force modelling of end milling operations

This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. Neural network (NN) algorithms are developed for use as a direct modelling method, to predict forces for ball-end milling operations. Prediction of cutting forces in ball-end milling is often needed in order to establish automation or optimization of the machining processes. Supervised NNs are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and NN approaches is compared. NN predictions for three cutting force components were predicted with 4% error by comparing with the experimental measurements. Exhaustive experimentation is conduced to develop the model and to validate it. By means of the developed method, it is possible to forecast the development of events that will take place during the milling process without executing the tests. The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system. It can be used also in the combination for monitoring and optimizing of the machining process-cutting parameters.