Distribution and Prediction of Incremental Cutter Flank Wear in High-Efficiency Milling

Abstract

In the milling of titanium alloy workpieces, tool wear seriously affects the surface quality of a workpiece and its tool life. It is of great significance to study the influence of instantaneous contact stiffness on instantaneous friction variables and incremental wear, which is of great significance for the realization of control over the degree of flank wear and improving the service life of cutter teeth. In this paper, an experiment to monitor cutting with a Ti6Al4V workpiece with a high-feed milling cutter was carried out; according to the experimental results, the wear area of the flank face of the cutter tooth was determined. The feature points of the flank were selected, and an instantaneous contact stiffness calculation method for the flank was proposed. The infinitesimal method was used to characterize the distribution of the contact stiffness of the flank, and the evolution characteristics of instantaneous contact stiffness distribution under the influence of vibration were obtained. According to the calculation results, the instantaneous distribution of flank wear depth was calculated. A grey correlation degree was used to reveal the correlation between the instantaneous contact stiffness of the flank face and wear depth, and a positionable wear-prediction model based on the instantaneous contact stiffness of the flank was proposed. Based on a BPNN (back propagation neural network), a prediction model for flank wear was established. The results showed that the above model and method could accurately predict the instantaneous wear of the tool flank.