Temperature measurement point optimization and experimental research for bi-rotary milling head of five-axis CNC machine tool

Thermal deformation is the main factor affecting the machining accuracy of the bi-rotary milling head. To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis method with improved connection weights to optimize the temperature measurement points. The analysis results are subjected to randomized mean value processing to reduce the randomness of the initialization of the prediction model. The number of temperature measurement points is reduced from 15 to 4. Taking the 5AS01 direct-drive bi-rotary milling head as an example, a thermal-structural coupling model is established to analyze its thermal characteristics. The capillary copper tube cooling suppression experiment is arranged according to the position of the temperature-sensitive points. The experimental results show that cooling the temperature-sensitive points can simultaneously reduce the thermal error in X- and Z-directions by about 58%, providing a basis for the bi-rotary milling head to improve machining accuracy.