Thermal error is the main source of error in the machining process of the precision machining center. Through the research on the mechanism of the thermal error of the machining center and its influence on the machining accuracy, the use of thermal error detection and compensation modeling can eliminate or reduce the influence of the thermal error on the precision machining center and improve the machining accuracy. This paper aims at the thermal error of the main shaft of the three-axis linkage precision vertical machining center, with the national science and technology major project “high-end CNC machine tools and basic manufacturing equipment” as the background, and the three-axis linkage vertical machining center VDM55 developed by Dalian Machine Tool Group as the research object. The research on spindle thermal error detection and compensation modeling was carried out. This article mainly conducts research from the following aspects:
- Explain the source and research background of the subject, make an overview analysis of the thermal error compensation technology of CNC machine tools, summarize the research status and current problems of domestic and foreign machine tool thermal error compensation technology, and establish the research technology of this subject Route and key research content.
- Analyze the structure and heat source distribution of the vertical machining center of the research object of the subject, combined with the actual working conditions of the machining center, study the spatial representation of thermal error from three angles of thermal drift, thermal elongation and thermal tilt. Based on the thermal error space decomposition results, a spindle thermal error detection system for vertical machining centers is designed.
- Introduce the thermal error detection test of the machining center. The thermal error of the spindle of the machining center is measured by the five-point measuring method assisted by the test rod, and the layout scheme of the temperature sensor is determined by the strategy of combining contact and non-contact. Combined with the actual working conditions of the machining center, the typical test conditions are designed to analyze the test data.
- Study the optimal layout of temperature measurement points of the spindle temperature field. The SOM neural network classification optimization algorithm based on random probability and the fuzzy clustering method based on correlation analysis are used to optimize the temperature measurement points of the spindle temperature field. Considering the number of different thermal key points, four spindle thermal error measurement points are obtained Optimization.
- Research the RBF and BP neural network modeling theory, and establish the corresponding spindle thermal error compensation model for different temperature measurement point optimization solutions. After analysis and comparison, the final compensation model is determined.