A method for predicting ball-end cutter milling force and its probabilistic characteristics

Abstract

Ball-end milling is widely used in industrial applications, such as mold manufacturing and aerospace industries. One of many difficulties in milling research is the milling force, which is often crucial to the component’s processing efficiency and quality. In this paper, a method for predicting ball-end cutter milling force and its probabilistic characteristics is presented. First, a model of milling force prediction is established by integrating the differential milling forces. Then, a method of identifying milling force coefficients is suggested based on the proposed model, which considered both the mean value and the amplitude of the milling force. Based on the proposed milling force prediction model, milling force probabilistic analysis while considering randomness of cutting parameters is performed. To improve computational efficiency, the function relationship between cutting parameters and the milling force is reconstructed by adaptive Kriging model. Furthermore, trained Kriging model is employed to analyze the probabilistic characteristics of the milling force. Both experimental and computational results indicate that the method proposed in this paper has high accuracy and efficiency.