A generalized neural network model of ball-end milling force system

The focus of this paper is to develop a reliable method to predict 3D cutting forces during ball-end milling process. This paper uses the artificial neural networks (ANNs) approach to evolve an generalized model for prediction of cutting forces, based on a set of input cutting conditions. A set of ten input milling parameters that have a major impact on the cutting forces was chosen to represent the machining conditions. The training of the networks is performed with experimental machining data. This approach greatly reduces the time-consuming mathematical work normally required for obtaining the cutting force expressions. The estimation performance of the network is evaluated through a detailed simulation study. The accuracy of an analytical model, which is a feasible alternative to the network, is compared to that of the network. With similar system parameter estimates for both methods, the network is found to be considerably more accurate than the analytical model. The results of model validation experiments on machining Ck45 are also reported. Experimental results demonstrate that this method can accurately estimate feed cutting force within an error of 4%. The results also indicate that when the combination of sigmoidal and gaussian transfer function were applied, the prediction accuracy of neural network is as high as 98%.