The metal cutting process is accompanied by complex stress field, strain field, temperature field. The comprehensive effects of process parameters on chip morphology, cutting force, tool wear and residual stress are complex and inter-connected. Finite element method (FEM) is considered as an effective method to predict process variables and reveal microscopic physical phenomena in the cutting process. Therefore, the finite element (FE) simulation is used to research the conventional and micro scale cutting process, and the differences in the establishment of process variable FE simulation models are distinguished, thereby improving the accuracy of FE simulation. The reliability and effectiveness of FE simulation model largely depend on the accuracy of the simulation method, constitutive model, friction model, damage model in describing mesh element, the dynamic mechanical behavior of materials, the tool-chip-workpiece contact process and the chip formation mechanism. In this paper, the FE models of conventional and micro process variables are comprehensively and up-to-date reviewed for different materials and machining methods. The purpose is to establish a FE model that is more in line with the real cutting conditions, and to provide the possibility for optimizing the cutting process variables. The development direction of FE simulation of metal cutting process is discussed, which provides guidance for future cutting process modeling.

Easy cutting vibration of Titanium alloy thin-walled structural components in processing process directly influences the quality of part machining surface. So, the chatter prediction has become a research hotspot. The milling process of Ti-6Al-4V framework parts for hard alloy cutter is researched and chatter prediction methods are proposed to solve the chatter problem generated in milling process. The signals in milling process are comprehensively considered to work out the stability boundary and the chatter prediction model based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM). The stability lobe diagram is utilized to select experiment parameter for experiment, in which the 1/3-2/3 position of framework parts chatters easily in processing. The model training in experiment aims to monitor the time of chatter, with the recognition precision of 97.50%.