Thermal Effects Analysis And Compensation Strategy Research For CNC Machine Tool

Along with our country go from a manufacturing country to a powerful manufacturing country, the machining accuracy of NC machine tools has an increasingly demand in China. But with the continuous improvement of the machining precision of NC machine tools, thermal error has become the key factors in machining accuracy of CNC machine tool. It’s up to 70% in the total error of machine tool. So the effective compensation for thermal errors of machine tool has become a problem need to be solved in our country and the world. In this paper the compensation for thermal error of CNC machine tools as a main line, then conduct a further research from finite element analysis for thermal deformation of NC machine tools, optimizat ion of temperature measuring and thermal error modeling.Firstly, the thermal deformation finite element analysis of CNC machine tools. Presents the calculation method of heat source and the heat transfer coefficient of the CNC machine tools from the theor y, based on finite element analysis and experimental verification, the wheel groove milling machine spindle box is carried on the steady state and transient thermal-structure coupling analysis,and the wheel groove milling machine spindle box of temperature rise and thermal deformation is obtained. At the same time, the environment temperature on the thermal deformation of wheel groove milling machine spindle box effect is analyzed.Secondly, the temperature measurement optimization of NC machine tool. By using the method of transient thermal analysis, the temperature rise and thermal deformation data of vertical milling machine are obtained, and the pre-arrangement temperature measuring is optimized by using FCM clustering algorithm. According to the defects of FCM clustering algrithm, the algorithm is improved and the adaptive clustering algorithm is presented. This method not only can give the optimal number of clusters, but also can opitimize the measuring point. The temperature measuring points is reduced from 13 to 6 through the improved algorithm, and the points classification are more close to actual situation.On the basis of this, the adaptive clustering algorithm is applied to the wheel g roove milling machine spindle box’s temperature measuring optimization, also the key temperature measuring points on the spindle box is obtained.The final is the thermal error modeling of NC machine tools. Building experiment platform, measuring key point’s temperature rise and thermal deformation of machine tool, the thermal error modeling is established by RBF neural network and multiple linear regressions with two kinds of different temperature measuring point optimization, and the modeling accuracy and robustness are analyzed. The results showed that: all the thermal error models have a better predict for the machine tool’s thermal error, but the RBF neural network with adaptive temperature measuring point optimization algorithm has the highest precision and good robustness.