Improving machining accuracy of complex precision turning-milling machine tools

To improve stability and accuracy during machining, the structural performance of machine tools must be predicted in advance. This study aims to improve the performance of a large-scale turning-milling complex machine tool by analyzing its structural characteristics and health status. Modal and spatial accuracies of an actual machine tool were analyzed using the finite element method based on the working environment and material properties. Static analysis was used to determine spatial processing position deformation errors and an appropriate compensation value to improve accuracy. The modal analysis determined the characteristic parameters of the main machine structure in a fair frequency range, and the modal analysis software verified the machine frequency using modal shape curve fitting. Modal error percentages between the virtual and machine models determined the validation of the model. The prediction diagnosis performance system monitored the spindle vibration signal to evaluate its health status. However, the use and maintenance of each piece of equipment differed. Abnormal symptoms of the spindle are observed in specific frequency bands or vibration characteristics. The feature modeling can establish a health diagnosis model and use the principal component analysis method to observe vibration characteristics and identify machine health.