基于误差分布图的五轴在机测量路径优化方法

doi:10.7527/S1000-6893.2023.29261

Abstract

Abstract:

In five-axis on-machine measurement， the measurement path planning determines calibration efficiency， measurement efficiency， and measurement accuracy under the influence of machine positioning errors. In order to obtain a measurement path that balances measurement accuracy and efficiency， this paper proposes an on-machine measurement path optimization method based on the error distribution graph. First， the measurement feasible graph of each to-be-measured point is constructed based on the results of interference checking. Then， a positioning error impact model is used to predict the measurement error distribution within the feasible graph. To improve the on-machine measurement accuracy and efficiency， the measurement error is used as the accuracy optimization target， and the efficiency optimization target is constructed by combining the measurement path length and the number of calibration points， and therefore， the multi-objective optimization model is established. NSGA-Ⅱ is used to solve the optimization model and optimize the measurement path. Finally， the centrifugal impeller is used as an example to verify that this method can not only reduce the ruby touch points and measurement path length to improve efficiency， but also accurately compensate for the pre-travel error and reduce the effect of the introduced machine positioning error to improve the measurement accuracy. The experimental results show that the optimized method can reduce the calibration time by 55.28%， the measurement time by 10.2%， and the average measurement error by 26.7%， indicating that the method has good feasibility.

Key words: on-machine measurement, measurement feasibility graph, measurement error, pre-travel error, path planning

CLC Number:

V260.5

Yanheng GUO, Neng WAN, Qixin ZHUANG, Bo LIU, Xinxin LI, Dao WANG. Optimization method for five⁃axis on⁃machine measurement path based on error distribution graph[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(13): 629261-629261.

Figures/Tables 18

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References 23

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序号	X/mm	Y/mm	Z/mm	序号	X/mm	Y/mm	Z/mm
1	-38.454	-4.825	-9.936	13	-40.559	-13.770	-21.800
2	-38.671	-7.092	-12.760	14	-42.318	-16.164	-24.591
3	-39.222	-9.306	-15.615	15	-44.439	-18.696	-27.277
4	-40.114	-11.516	-18.464	16	-46.899	-21.384	-29.839
5	-41.372	-13.784	-21.287	17	-35.865	-4.9440	-10.394
6	-42.994	-16.164	-24.039	18	-36.275	-7.1230	-13.386
7	-44.97	-18.672	-26.695	19	-37.067	-9.280	-16.400
8	-47.289	-21.324	-29.236	20	-38.219	-11.473	-19.381
9	-37.159	-4.882	-10.167	21	-39.746	-13.752	-22.306
10	-37.475	-7.112	-13.072	22	-41.646	-16.161	-25.136
11	-38.132	-9.293	-16.002	23	-43.911	-18.720	-27.854
12	-39.166	-11.495	-18.927	24	-46.521	-21.435	-30.429

序号	实验1	实验2	序号	实验1	实验2	序号	实验1	实验2
1	（-71.0，324.0）	（-67.2，345.2）	9	（-80.0，1.0）	（-69.3，355.3）	17	（-73.0，333.0）	（-69.5，342.6）
2	（-71.0，345.0）	（-69.8，353.2）	10	（-77.0，348.0）	（-69.0，1.6）	18	（-78.0，325.0）	（-73.0，352.8）
3	（-80.0，335.0）	（-71.6，356.2）	11	（-82.0，1.0）	（-71.4，5.5）	19	（-81.0，342.0）	（-77.3，357.4）
4	（-74.0，344）	（-72.4，0.2）	12	（-76.0，0.0）	（-73.0，14.0）	20	（-68.0，6.0）	（-74.7，353.8）
5	（-74.0，345.0）	（-71.5，359.0）	13	（-77.0，341.0）	（-75.3，12.7）	21	（-72.0，341.0）	（-57.0，357.4）
6	（-70.0，340.0）	（-75.4，3.2）	14	（-55.0，357.0）	（-73.6，12.7）	22	（-78.0，340.0）	（-56.6，358.0）
7	（-73.0，6.0）	（-53.7，356.3）	15	（-63.0，329.0）	（-58.9，10.6）	23	（-58.0，336.0）	（-59.3，1.2）
8	（-58.0，323.0）	（-52.6，353.4）	16	（-54，0，326.0）	（-59.7，13.5）	24	（-59.0，322.0）	（-62.5，357.5）

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Optimization method for five⁃axis on⁃machine measurement path based on error distribution graph

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