Tool edge preparation can improve the tool life, as well as cutting performance and machined surface quality, meeting the requirements of high-speed and high-efficiency cutting. In general, prepared tool edges could be divided into symmetric or asymmetric edges. In the present study, the cemented carbide tools were initially edge prepared through drag finishing. The simulation model of the carbide cemented tool milling steel was established through Deform software. Effects of edge form factor, spindle speed, feed per tooth, axial, and radial cutting depth on the cutting force, the tool wear, the cutting temperature, and the surface quality were investigated through the orthogonal cutting simulation. The simulated cutting force results were compared to the results obtained from the orthogonal milling experiment through the dynamometer Kistler, which verified the simulation model correctness. The obtained results provided a basis for edge preparation effect along with high-speed and high effective cutting machining comprehension.

Introduction: High-speed and high-efficient machining is the inevitable development direction of machining technology. The tool edge preparation can improve the life, cutting performance, and surface quality of a tool and help to achieve high-speed and efficient machining. Therefore, precise modeling and detection of the micron-level contour of a tool edge are crucial for edge preparation. The aim of this study is to provide the model and detect method of the prepared tool edge radius. Methods: The mathematical model of the milling tool trajectory is established through the Matlab. The material removal model by single abrasive particle is established based on the energy conservation principle and energy absorption theory. The material removal model by multiple abrasive grains on the cutting tool edge is constructed using the statistical methods. The mathematical model of the edge radius is established through the geometrical relationship. The milling edge preparation contour detection system is setup based on the machine vision principle through LabVIEW software. Finally, the edge radius at different process parameters is determined by the mathematical model and detection system, and the results are compared with the results of the scanning electron microscopic measurement (SEM). Results: Through the Comparison and analysis of the edge radius measured by the SEM and calculated by the proposed model. The maximum error between the analytical results and SEM measurements is 11.18 μm, while the minimum error is 0.07 μm. Through the comparison and analysis of the edge radius measured by the SEM and the edge detection system. The maximum difference between the two methods is 2.71 μm, and the minimum difference is 0.31 μm. The maximum difference in percentage is 9.2%, and the minimum difference in percentage is 1.2%. Discussions: The edge preparation mechanisms of a single particle and multiple particles on the tool edge are explained. A mathematical model of the edge radius is established, which provides a basis for a deeper understanding of the edge preparation effect. Based on the machine vision principle, the prepared tool micron-level edge detection method is proposed. The histogram specification method, median filtering, multi-threshold segmentation method, and Canny edge detection operator are adopted to obtain the edge contour. The comparison result shows that the mathematical model of the edge radius is accurate, and the proposed tool edge detection method is feasible, which lays the foundation for edge preparation and realization of high-speed and high-efficient machining.