Modelling 3-D cutting dynamics with neural networks

Abstract Identification of 3-D cutting dynamics requires an expensive experimental set-up and complicated analysis. Recently, time series methods were used to model cutting dynamics. This approach allows a simpler experimental set-uup and estimates the discrete transfer functions used for simulation and/or calculation of frequency domain characteristics of the system. In this paper, the use of neural networks is proposed to model the 3-D cutting dynamics. Neural networks can be trained using the same experimental set-up used for the time series methods. However, several time series models (for different cutting speeds) can be represented with a single neural network, and cutting forces can be studied for varying cutting speed conditions. Also, four neural networks were used to store the frequency domain characteristics of the thrust direction cutting force. In this study, the estimation errors for the neural networks were less than 7% of the defined range (the difference between the maximum and minimum of the data).