# Distribution and Prediction of Incremental Cutter Flank Wear in High-Efficiency Milling
## 1 Introduction
The results show that the degree of tool wear increases with the increase in surface inclination angle, and, in the early cutting process, tool wear occurs mainly due to the diffusion wear of the flank; in the later cutting process, tool wear is mainly caused by adhesive wear and microchipping. The above research is based on a large number of experiments, but the identification results are greatly affected by uncertain interference without the support of cutting force model and wear model. The wear of the cutter tooth causes a change in the geometric structure of the cutting edge and the flank, which changes the instantaneous contact stiffness and its distribution within the flank, resulting in the uneven distribution of friction and wear degree over the flank, making the evolution of the friction and wear of the milling cutter difficult to predict, with the service life of the cutter decreasing. In a study of the stiffness of the machining system in the cutting process, [PERSON] et al. [19] proposed a method to identify the contact behavior of the cutter handle joint surface and concluded that the contact stiffness and total contact force decreased significantly at high speed. [PERSON] et al. [20] established a unified damping model for milling systems with different stiffnesses, revealing the relationship between stiffness changes and shear force coefficients. [PERSON] et al. [21] proposed a dynamic stiffness model to consider the static and dynamic effects, revealing the influential factors of forced tool vibration on the cutting parameters and stiffness. [PERSON] et al. [22] simulated the influence of cutting force on the stiffness deflection of a tool spindle and proposed a data-monitoring method for the state of a machine tool, studying the initial inclination angle of the tool and the stiffness of the spindle system. [PERSON] et al. [23] considered the change in instantaneous cutting force, the change in contact stiffness between the workpiece and the additional tool support, and the change in workpiece thickness during the milling process and proposed a prediction model for the influence of additional tooltip support on the machining surface error in the peripheral milling of flexible workpieces. [PERSON] et al. [24] proposed a finite element modeling method to determine the stiffness and damping behavior between a tool and the -fxture in the heat shrink-fit connection. [PERSON] et al. [25] proposed a design method for the interface between the spindle and the tool holder by using fractal theory and a multiscale contact mechanic model. The effects of cutting force, average roughness, material type, and other factors were analyzed. The effects of performance, preload, and temperature change on contact stiffness were discussed. For a long time, many scholars have been concerned about the real-time monitoring of tool wear and breakage during machining. At present, it can be divided into direct monitoring and indirect monitoring. The direct monitoring method is used to quantify tool wear and damage status through image vision and infrared measurement [26]. However, these methods need to interrupt the processing process and affect processing efficiency; at the same time, the direct monitoring method is expensive and limited by the operating environment due to the continuous contact between the tool and the workpiece, as well as the harsh processing environment (cutting fluid and chips). The application of the direct monitoring method in the actual machining process has great limitations [27]. The indirect monitoring method is used to identify the state of tool wear in real time via generated data by analyzing the signal received by the sensor generated by the milling cutter during the machining process. The indirect method measures tool wear by extracting cutting force, vibration rate, and current [28]. The main advantage of these methods is that the monitoring process does not need to stop, making up for the lack of direct monitoring methods.