Influence of tool wear on geometric surface modeling for TC4 titanium alloy milling

Addressing the insufficient accuracy of milling surface roughness prediction model, the formation causes of different roughness were analyzed, and a surface roughness prediction model which takes tool wear into account was established based on an improved Z-MAP algorithm. Combining tool geometry and tool wear morphology, a mathematical model of surface profile was built; And the motion trajectory equations of the milling cutter teeth were established via machining kinematics. The servo rectangular encirclement and Newton iterative method were adopted to improve the Z-MAP algorithm. Combining the Z-MAP algorithm with the insert motion trajectory equations, the values at different height coordinates of the workpiece grid nodes were obtained, allowing numerical simulation of workpiece surface geometry and analysis of the effect of cutting trajectory overlap on the machined surface morphology. TC4 titanium alloy milling experiment was conducted to validate the simulation model. The simulated surface roughness Sa exhibited significant deviation from experimental values before tool wear, with a maximum error of 53.08 %. Nevertheless, the simulated values of Sa were closer to experimental values after tool wear, with a maximum error of 15.45 % and a minimum of only 3.07 %. Additionally, the predicted results for the bearing length ratio Rmr(c) were consistent with experimental values. The mathematical model for surface roughness taking tool wear into account effectively predicted the shape profile and surface roughness of the machined surface, providing theoretical guidance and technical support for practical production.