Reduction of temperature measurement points for NC machine based on PCA and SVC

Thermal error is the key error source which affects the stability of machining accuracy and temperature measurement point optimization is the premise and difficult to achieve accurate thermal error compensation. In order to reduce the temperature measurement point and improve the efficiency of temperature data acquisition, The method is proposed based on principal component analysis (PCA) and Single Variable Contribution (SVC). This method is mainly to reduce the temperature measurement points of the machine tool, so as to facilitate the later thermal error modelling. firstly, PCA is introduced to extract the principal components of temperature data samples. Then, the contribution of each temperature variable attribute in the main component space is recorded as a single variable contribution degree, and based on which the key measuring points is screened out. Thirdly, multivariable linear regression model is used as the thermal error model of the spindle in z-axix. Finally, three axis CNC machine tool μ1000/3ν is taken as the research object and the results show that the proposed method can reduce the temperature measuring point from 24 to 6, and minimum residual error is only 0.5 μm. This fully shows that the method proposed in this paper is effective, and has a certain guiding role to improve the precision prediction of machine tool.