精密进给系统热误差的协同训练支持向量机回归建模与补偿方法

Considering the problem of high cost and scarcity of thermal error data acquisition for precision feed system, a modeling and compensation strategy for the thermal error of precision feed system based on co-training support vector machine regression is proposed. This strategy establishes thermal error model by integrating labeled data such as temperature error and thermal error and unlabeled temperature data, then compensates the compensation method based on Siemens 840D NC system. Taking X-axes of double-drive ball screw feed system of precision boring machine as the research object, thermal characteristic experiments are carried out. The labeled data at 24 m/min feeding speed and the unlabeled temperature data at 12 m/min feeding speed are obtained. The thermal error model is constructed by integrating all data with COSVR, and only the labeled data are used to construct the control model with support vector machine regression algorithm optimized by genetic algorithm(GA-SVR). The labeled data at 18 m/min feeding speed are obtained for model performance test. Compared with GA-SVR model, the root mean square error of COSVR model is reduced by 34.14%, and the error range at 100 min and 520 min is reduced by 62.62% and 55.85% respectively. The results show that COSVR model has better prediction performance and reduces thermal error more effectively to further improve the accuracy of thermal error modeling of precision feeding system.