Evaluation of measurement uncertainty of the high-speed variable-slit system based on the Monte Carlo method

Yin ZHANG, Jianwei WU, Kunpeng XING, Zhongpu WEN, Jiubin TAN

PDF(4973 KB)
PDF(4973 KB)
Front. Mech. Eng. ›› 2020, Vol. 15 ›› Issue (4) : 517-537. DOI: 10.1007/s11465-020-0589-z
RESEARCH ARTICLE
RESEARCH ARTICLE

Evaluation of measurement uncertainty of the high-speed variable-slit system based on the Monte Carlo method

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Abstract

This paper presents a dynamic and static error transfer model and uncertainty evaluation method for a high-speed variable-slit system based on a two- dimensional orthogonal double-layer air-floating guide rail structure. The motion accuracy of the scanning blade is affected by both the moving component it is attached to and the moving component of the following blade during high-speed motion. First, an error transfer model of the high-speed variable-slit system is established, and the influence coefficients are calculated for each source of error associated with the accuracy of the blade motion. Then, the maximum range of each error source is determined by simulation and experiment. Finally, the uncertainty of the blade displacement measurement is evaluated using the Monte Carlo method. The proposed model can evaluate the performance of the complex mechanical system and be used to guide the design.

Keywords

air-floating guide rail / error transfer model / driving and following structure / dynamic error / uncertainty evaluation / Monte Carlo method

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Yin ZHANG, Jianwei WU, Kunpeng XING, Zhongpu WEN, Jiubin TAN. Evaluation of measurement uncertainty of the high-speed variable-slit system based on the Monte Carlo method. Front. Mech. Eng., 2020, 15(4): 517‒537 https://doi.org/10.1007/s11465-020-0589-z

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Acknowledgement

This work was funded by the National Natural Science Foundation of China (Grant No. 51675136), the National Science and Technology Major Project (Grant No. 2017ZX02101006-005), the China Postdoctoral Science Foundation (Grant No. 2018T110291), and the Heilongjiang Natural Science Foundation (Grant No. E2017032).

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