Thermal Error Measurement And Research On Compensation Modeling For The Spindle Of Machining Center

Thermal error is the main source of error in the processing of precise machining centers. Thermal error measurement and modeling can eliminate or reduce the harmful effect on machining center caused by thermal error to improve the machining precision. This study backgrounded with the National Science and Technology Major Project "High-level NC Machine Tools and Basic Manufacturing Equipment", aiming at the spindle thermal error of the three-axis linkage vertical machining center, take the three-axis machining center researched and developed by Dalian Machine Tool as the research object. Thermal error measurement and compensation modeling for the spindle of the precise machining center were researched. The paper consists of the following parts:(1) The source and background of the research are stated. Thermal error compensation technique is generally analyzed. The status of development of thermal error compensation techniques for machine tools at home and abroad is elaborated. The research thinking and focuses of this paper are determined.(2) The structure and thermal distribution of the research object three-axis vertical machining center are analyzed. Allowing for the actual work situations of the machining center, the spatially represented manners of thermal error are described from the following angles:thermal drift, thermal extension and thermal tilt. On the basis of the spatial decomposition results of the spindle thermal error, the hardware components of the thermal error measurement system designed for vertical machining centers are introduced.(3) The thermal error measurement experiment for the machining center is described. Thermal error for the spindle of machining center is detected through five point method assisted by the check bar. Contact measurement combined with noncontact measurement strategy is used to determine the arrangement of the temperature sensors. The typical working conditions are designed in the measurement experiments and the experimental data is analyzed.(4) The optimization of temperature measuring points in the spindle temperature field is researched. SOM neural network based on random probability and fuzzy cluster method based on correlation analysis are adopted to optimize the temperature measuring points respectively. Optimization schemes are gained considering the different measuring points combinations in the end. (5) RBF and BP neural network modeling are introduced and used to establish the compensation models corresponding the temperature measuring point optimization schemes. The final compensation model is determined after the analysis comparison.