Data Fusion Approach for Extracting Spindle Key Performance Features Under Different Workloads

Abstract

The spindle is the core component of CNC machine tools, and its performance features are the main factors affecting the overall accuracy and efficiency of machine tools. This study proposes a method for extracting key performance features of machine tool spindles under different workloads. First, it develops a spindle performance test device, which can obtain the performance features of the spindle at different speeds and radial and axial forces. Then, it proposes a spatial vector model for predicting radial runout at any axial position. Test results show that the maximum error between the test and the prediction model is 2.85% at the first prediction position. At the second prediction location, the maximum error is 2.25%. Lastly, this study proposes a data fusion method to calculate the bearing stiffness by combining the measured data, mechanical theory, and finite element analysis. Test results show that the bearing stiffness feature can be simplified to a constant value in the milling process. In the typical working load range, the bearing stiffness fluctuates within 20% of the axial force. The method is applicable and instructive for engineering applications.