Research And Application Of Thermal Error In Precision Machine Tools Based On Dynamic Bayesian Network

Thermal error is a major error source that affects machining accuracy. For general CNC machine tools, thermal error can be accounted for about 35% of the machining error, especially for precision or ultra-precision machining. Thermal error may affect machining accuracy as high as 40%-70%. Thus, thermal error grows up to be a principal factor that affects machining accuracy of precision CNC machine tools. Therefore, it is important to research thermal error model to improve machining accuracy of machine tools.This paper presents a research and application of thermal error in precision machine tools based on Dynamic Bayesian Network(DBN) model. In the same time, the data can be measured and the temperature sensor is used to measure the data under the actual condition of the machine tools, and the time series algorithm is used to estimate and forecast the data, and using the fuzzy clustering method in gray system, for the data filter optimization. Then the relative accuracy of the measurement data and the structure of dynamic Bayesian network are obtained. The network nodes between thermal error elements and the thermal deformation of the precision multi-axis milling machine tool and the machining center are explicitly established.The main research contents are as follows:(1) In order to realize dynamic measurement of the precision numerical control machine tools quickly and efficiently, a dynamic tracking measurement scheme is proposed, which is based on the precision measuring instrument-DBB and digital temperature sensors.(2) Using fuzzy clustering analysis method in gray system to optimize the key temperature measuring points, then carry out the temperature acquisition experiment of thermal error. Combined with the characteristics of temperature and thermal error on the precision multi-axis milling machine tools and machining center. Temperature and thermal deformation of grouping, and then choose the typical values in each group according to the correlation, the measurement data are modified.(3) For the complex changes of precision CNC machine tools, such as the processing conditions and environmental factors and other complex changes, this paper uses the time series algorithm to predict the data which the sensors can not get a complete data. And establish a representative sample of the mathematical model of time dependent relationship, based on the model of relationship between movements, estimate projections for the data.(4) Grey System theory and Dynamic Bayesian network structure to construct the thermal error model, and the MATLAB software is used to construct the model simulation. According to the comparison of the results of the output shows which modeling method is more superior. Then, illustrate applications of thermal error modeling according to the reality.