Study on the cutting force prediction of supercritical material milling

In this paper, the technology of the artificial neural network (ANN) is applied in the study on cutting force prediction of supercritical material milling. Base on the orthogonal milling experiments, three signals of the cutting force have been collected. The basis of this approach is to train and test the cutting force model. The inputs to the model consist of cutting velocity v_c, feed rate f_z and depth of cut a_p, while the outputs are composed of thrust force Fx, radial force Fy and main cutting force Fz. Two-dimensional Gaussian surfaces of the cutting force and three cutting elements have been established fitting through JMP software. Base on the factors portray, the rules of cutting forces variation are forecasted. During the lack of empirical formula of cutting force in CNC milling process, prediction of cutting force is achieved by describing the factors. The prediction results are in good agreement with the experimental results.