Abstract
The measurement and compensation of volumetric positioning errors can be used to significantly improve the accuracy of machine tools. In this paper, a sequential step diagonal measurement is introduced to measure nine volumetric positioning errors in a short time. Measurements under various thermal conditions are preformed to understand the relationship between the volumetric positioning errors and the machine temperature field and variations. A radial basis function neural network is used to predict the volumetric positioning errors at all positions based on the temperature distribution of the machine. Compensation experiment is carried out to validate the performance of the measurement and the prediction method. The experimental results show that the volumetric accuracy of the machine tool is significantly improved by the error compensation.
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