Surface texture topography evaluation and classification by considering the tool posture changes in five-axis milling

Surface texture topography affects the performance of industrial parts, such as precision and service life. Different tool postures may lead to various texture topographies in five-axis milling. In this paper, surface texture topography quality and texture trend evaluation indexes are proposed considering the influence of tool posture in five-axis milling. Based on the evaluation, the ranges of tool posture angles corresponding to surface texture topographies of different characteristic are obtained through surface texture topographies classification. The result can provide reference for singularity avoidance in path planning and machining error compensation in five-axis milling. The influence of tool posture change on the surface texture quality could be avoided while adjusting the tool posture angle in the determined range. Firstly, the general tool cutting edge point expression with the typical structure parameters of the end mills is proposed. The general digital model of surface texture topography is established with the improvement of the unit time calculation and computing vertex judgment in simulation. Secondly, several evaluation indexes are proposed to represent the quality and the overall trend of the surface texture topography with digital extraction of indexes realized on the surface digital model. On this basis, the classification of the surface texture topographies and the determination of the range of tool posture angle for each class of textures are proposed. Thirdly, the evaluation and classification of the ball-end milling plane texture topographies are carried out, which divide the texture topographies into zero-inclination angle textures and non-zero inclination angle textures. The surface textures are further divided to detailed five categories with the tool posture angle range determined. Different tool posture angles of the same class are used to generate a texture topography for the comparison to verify the evaluation indexes and the classification result. Finally, the plane cutting orthogonal experiments are carried out. The general surface digital model is verified by the comparison in surface texture characteristic of different types of mills between digital model and machined surface. Based on the ball-end milling plane classification result, the machined surface textures and extracted indexes are compared to verify the effectiveness of the proposed evaluation indexes and the classification result. The fillet-end milling surface textures are further classified and verified based on the method above. In addition, the spherical surface textures of ball-end milling are carried out for further verification.