Thermal Error Modeling Of The Mechanical Biaxial Rotary Milling Head Based On Hybrid Algorithm

As a key part of the five-axis linkage CNC machine, the high-torque biaxial rotary milling head is mainly used to realize the function of the A-axis swing and C-axis rotation for the five-axis machine tool and make it easy to realize the high efficiency and high precision machining for complex parts. In the machining process, the double biaxial rotary milling head bear large loads, so under the effect of these loads, its internal bearing and gears will produce large amount of heat because of the friction. In the influence of the internal heat source, the double biaxial rotary milling head produce thermal deformation, which reduce its machining precision. In order to improve the machining precison of the biaxial rotary milling head, it need to carry on precision compensation. On the basis of analyzing the characteristic of different thermal error compensation, the thermal error modeling method adopting the hybrid algorithm is present in this paper, by establishing the model between the temperatures and errors as the input model of the error compensation to realize the improvement of the machining accuracy of the double rotary milling head. In this paper, the research is as following:(1) The variation discipline of temperature with time of high-torque biaxial rotary milling head parts under the standard condition is studied. And on the basis of these, the linear thermal drift error of X, Y, Z and the angle of thermal drift error of A/C axis and the variation discipline of the thermal error with the position and orientation under the influence of the temperature state are studied.(2) Coupling of the thermal key point and low correlation between the temperature and the thermal error are the two important problems in the selection of the thermal key point, and the traditional modeling method didn’t accommodate to the modeling problem with many error terms, so the main factors strategy combining with arithmetic mean method is put forward to compute the correlation coefficient between the temperatures and the different thermal errors under different angle. Then through the correlation degree, the thermal error key points are preliminary filtered. On the basis of that process, the thermal error key points are clarified into different groups. Finally the optimal thermal key points are selected considering the correlation degree and arrangement realizability of the temperature sensors from each partition.(3) The application of multiple linear regression in the thermal error modeling is analyzed. In this paper, the thermal error models under different rotary angles are built using the temperatures of the key point at the number of 2, 4, 6 to determine the the optimal number of thermal error points. In the meantime, two models are established using the key points selected by hybrid algorithm or principal factor to verify the precision prediction performance, it can be founded that the model established using the using the key points selected by hybrid algorithm has higher prediction precision.