Identification method for the dynamic distribution characteristics of machining errors in high energy efficiency milling

The distribution characteristics of machining errors in high energy efficiency milling are an important index to evaluate the surface geometry parameters, cutting stability, and dynamic cutting efficiency. The existing methods for machining errors focus on the overall level of the parameters of a machined surface and the degree of deviation from the index and ignore the influence of the instantaneous cutting behavior of a milling cutter and its teeth on the dynamic formation process of a machined surface. The dynamic distribution of machining errors in high energy efficiency milling needs to be revealed. According to the dynamic characteristics of the machined surface formation process with the effects of the cutter tooth error and milling vibration, a method for solving relative position vector of each point on the machined surface was proposed, and the calculation model of the dynamic distribution of the machining errors was constructed to unveil its formation mechanism in high energy efficiency milling. Using the time-frequency analysis method of the milling vibration and machining errors, the dynamic distribution of the machining errors on the machined surface was characterized, and the variety of the geometric error variation of the machined surface was described. The effects of the milling cutter design pose, cutting parameters, cutter tooth error, and milling vibration on the dynamic distribution of the machining errors were revealed, with the proposed identification method of its influencing factors. The response of the dynamic distribution of machining errors was studied, and a method for its identification was proposed and verified with experiments. The results showed that there was a high similarity between the calculated and measured results of the dynamic distribution of the machining errors. The influence mechanism of the key process variables on the dynamic distribution of the machining errors could be identified using the above model and method.