Optimizing dynamic measurement accuracy for machine tools and industrial robots with unscented Kalman filter and particle swarm optimization methods

Abstract

The telescoping ballbar is widely utilized for diagnosing accuracy and identifying faults in machine tools and industrial robots. Currently, there are no established standards for determining the optimal feeding speed for ballbar tests. This lack of clear guidelines results in time inefficiency in measurements and inconsistencies in dynamic measurements, which complicates the comparison of ballbar test results under various conditions or across different machine platforms. To mitigate dynamic variations in ballbar results, an updated ballbar data processing method that integrates the unscented Kalman filter (UKF) and particle swarm optimization (PSO) was developed and validated using real ballbar data measured at multiple feeding speeds and simulated data with varying vibration magnitudes generated through the Renishaw ballbar simulator. Experimental results revealed that the dynamic components extracted from the ballbar results were observed to increase in correlation with the vibration measured at different feeding speeds and from the simulations. Moreover, the variations in the results measured at different feeding speeds after PSO-UKF processing were significantly reduced. The findings confirm the effectiveness of the proposed method in minimizing the dynamics of the ballbar results. Ultimately, this approach enhances the efficiency and accuracy of ballbar testing and offers a general method for improved diagnostics.