Optics and Precision Engineering, Volume. 26, Issue 11, 2684(2018)
Measurement and identification of geometric errors for turntable-tilting head type five-axis machine tools
In order to reduce the influence of the geometrical error of rotary axis on the accuracy of turntable-tilting head type five-axis machine tools, a Position-Independent Geometric Error(PIGE) measurement and identification method based on the measurement of the Double Ball Bar(DBB) is proposed. Firstly, based on multi-body system theory and homogeneous coordinate transformation method, the position-independent geometric error model of turntable-tilting head type five-axis machine tool position was established. Four measurement model based on circular path measurement were established according to the influence factors of geometric errors under different motion states of rotary axes, and 10 position-independent geometric errors were identified. Secondly, the numerical simulation was carried out by using the established geometric error model to quantify the influence of 10 geometric errors on the measurement trajectory of the rotary axes. Finally, the geometric error compensation was conducted to validate the validity of the proposed measurement and identification methods, and the measurement trajectories before and after the position-independent geometric error compensation were compared. The average compensation rate of the position-independent geometric errors of the 10 positions is 70.4%, and the maximum compensation rate is 88.4%. The experimental results show that the proposed modeling and identification method can be used to detect the accuracy of rotary axis, at the same time which can be used for the machine tool accuracy evaluation and provide the guidance for improving the accuracy.
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GUO Shi-jie, JIANG Ge-dong, MEI Xue-song, TAO Tao. Measurement and identification of geometric errors for turntable-tilting head type five-axis machine tools[J]. Optics and Precision Engineering, 2018, 26(11): 2684
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Received: Jan. 16, 2018
Accepted: --
Published Online: Jan. 10, 2019
The Author Email: Shi-jie GUO (zijianguoxjtu2015@163.com)