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Efficient Methods for Evaluating Task-Specific Uncertainty in Laser-Tracking Measurement

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Abstract

For task-specific coordinate metrology, it is necessary to associate with task-specific parameters an estimate of their uncertainty at a specified level of confidence. In recent years, various sources of uncertainty for laser trackers themselves have been studied. However, methods for determining the uncertainty in laser-tracker-based task-specific measurement must be further studied. In this paper, the guide to the expression of uncertainty in measurement (GUM) uncertainty framework (GUF), the Monte Carlo method and a hybrid method combining grey evaluation and neural network technologies are used to evaluate the task-specific uncertainty in laser-tracking measurement. First, this article discusses the contributions to measurement uncertainty in specific laser-tracking measurement, including instruments themselves, data fusion, measurement strategies, measurement environment and task-specific data processing. Second, the principles of GUF, Monte Carlo and the aforementioned hybrid method are presented. Finally, a case study involving the uncertainty evaluation of a cylindricity measurement process using the above-mentioned methods is illustrated. The results demonstrate that these methods have different characteristics in task-specific uncertainty evaluation.

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References

  1. Evaluation of measurement data—guide to the expression of uncertainty in measurement, JCGM, BIPM (2008).

  2. J.M. Calkins and R.J. Salerno, A practical method for evaluating measurement system uncertainty, Boeing large scale metrology conference, Long Beach (2000).

  3. D. Huo and P.G. Maropoulos,. The framework of the virtual laser tracker: a systematic approach to the assessment of error sources and uncertainty in laser tracker measurement (2009).

  4. F. Zhang, X. Qu, J. Dai et al., A method of precision evaluation for field large-scale measurement, Acta Opt. Sin., 28 (11) (2008) 2159–2163 (in Chinese).

    Article  Google Scholar 

  5. F. Zhang, X. Qu and S.H. Ye, Uncertainty analysis in large-scale measurement based on Monte Carlo simulation method, Comput. Integr. Manuf. Syst., 15 (1) (2009) 184–187 (in Chinese).

    Google Scholar 

  6. W. Liu and Z. Wang, Coordinate uncertainty analyses of coupled multiple measurement systems, Meas. Sci. Technol., 21 (6) (2010) 1–6.

    Article  Google Scholar 

  7. H. Schwenke, B.R.L. Siebert, F. Waldele et al., Assessment of uncertainties in dimensional metrology by Monte Carlo simulation, Ann. CIRP, 49 (2000) 395–398.

    Article  Google Scholar 

  8. ISO/TS 15530-2: Geometrical product specification (GPS)-coordinate measuring machines (CMM): technique for determining the uncertainty of measurement—Part 2: use of multiple measurement strategies.

  9. ISO/TS 15530-3: Geometrical product specification (GPS)-coordinate measuring machines (CMM): technique for determining the uncertainty of measurement—Part 3: use of calibrated workpieces or standards.

  10. ISO/TS 15530-4: Geometrical product specification (GPS)-coordinate measuring machines (CMM): technique for determining the uncertainty of measurement—Part 4: evaluating task-specific measurement uncertainty using simulation.

  11. ISO/TS 15530-4: Geometrical product specification (GPS)-coordinate measuring machines (CMM): technique for determining the uncertainty of measurement—Part 5: use of expert judgment, sensitivity analysis and error budgeting.

  12. J.-P. Kruth, N. Van Gestel, P. Bleys and F. Welkenhuyzen, Uncertainty determination for CMMs by Monte Carlo simulation integrating feature form deviations, CIRP Ann. Manuf. Technol., 58 (2009) 463–466.

    Article  Google Scholar 

  13. S.V. Kosarevsky and V.N. Latypov, Practical procedure for position tolerance uncertainty determination via Monte Carlo error propagation, Meas. Sci. Rev., 12 (1) (2012) 1–7.

    Article  Google Scholar 

  14. J. Sładek and A.G. Ska, Evaluation of coordinate measurement uncertainty with use of virtual machine model based on Monte Carlo method, Measurement, 45 (2012) 1564–1575.

    Article  Google Scholar 

  15. J. Beaman and E. Morse, Experimental evaluation of software estimates of task specific measurement uncertainty for CMMs, Precis. Eng., 34 (2010) 28–33.

    Article  Google Scholar 

  16. X. Ni, Shape error evaluation and measurement uncertainty estimation, Chemical Industry Press, Beijing (2008) (in Chinese).

    Google Scholar 

  17. X.L. Wen, Y.B. Zhao, D.X. Wang and J. Pan, Adaptive Monte Carlo and GUM methods for the evaluation of measurement uncertainty of cylindricity error, Precis. Eng., 37 (2013) 856–864.

    Article  Google Scholar 

  18. J. Baldwin and K.D. Summerhays, Application of simulation software to coordinate measurement uncertainty evaluation, Measure, 2 (4) (2007) 40–52.

    Google Scholar 

  19. M. Galovska, M. Petz and R. Tutsch, Uncertainty in data fusion of coordinate measurements, GMA/ITG-Fachtagung Sensoren und Messsysteme (2012).

  20. F.A. Arenhart, G.D. Donatelli and M.C. Porath, An experimental method for assessing the contribution of the production process variations to the task-specific uncertainty, Measurement, 45 (2012) 507–516.

    Article  Google Scholar 

  21. Evaluation of measurement data—supplement 1 to the ‘‘Guide to the expression of uncertainty in measurement” – propagation of distributions using a Monte Carlo method, JCGM (2006).

  22. M.J. Calkins, Quantifying coordinate uncertainty fields in coupled spatial measurement systems, Ph.D. dissertation, Virginia Polytechnic Institute and State University, Virginia (2002).

  23. C. Cui, S. Fu and F. Huang, Research on the uncertainties from different form error evaluation methods by CMM sampling, Int. J. Adv. Manuf. Technol., 43 (2009) 136–145.

    Article  Google Scholar 

  24. M. Matsumoto and T. Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Trans. Model. Comput. Simul., 8 (1) (1998) 3–30.

    Article  MATH  Google Scholar 

  25. G.E.P. Box and M.E. Muller, A note on the generation of random normal deviates, Ann. Math. Stat., 29 (1958) 610–611

    Article  MATH  Google Scholar 

  26. P. Qin, Y. Shen and Z. Wang, Grey evaluation of non-statistical uncertainty in multidimensional precision measurement, Int. J. Adv. Manuf. Technol., 31 (2006) 539–545.

    Article  Google Scholar 

  27. Z. Wang, L. Ge and W. Yang, A novel measurement uncertainty evaluation method for small sample virtual instrument, Acta Metrol. Sin., 29 (4) (2006) 387–392.

  28. Z. Wang, Z. Liu and X. Xia, Measurement error and uncertainty evaluation, Science Press, Beijing (2008) (in Chinese).

    Google Scholar 

  29. J. Zhu, Z. Wang and X. Xia, Analytical model of indirect measurement based on neural network, Trans. Chin. Soc. Agric. Mach., 37 (5) 129–131 (in Chinese).

  30. ISO/TS 12180-2: Geometrical product specification (GPS)-Cylindricity—Part 2: specification operators.

  31. ISO/TS 12180-1: Geometrical product specification (GPS)-Cylindricity—Part 1: vocabulary and parameters of cylindrical form.

  32. J. Mao, Y. Cao and J. Yang, Implementation uncertainty evaluation of cylindricity errors based on geometrical product specification (GPS), Measurement, 42 (2009) 742–747.

    Article  Google Scholar 

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Acknowledgments

This work was funded by the Department Pre-Research Program of China (51318010402).

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Authors and Affiliations

  1. College of Mechatronics and Automation, National University of Defense Technology, Changsha, 410073, China

    Jingzhao Yang, Guoxi Li, Baozhong Wu, Jingzhong Gong, Jie Wang & Meng Zhang

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  1. Jingzhao Yang
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  2. Guoxi Li
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  3. Baozhong Wu
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  4. Jingzhong Gong
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  5. Jie Wang
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  6. Meng Zhang
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Correspondence to Guoxi Li.

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Yang, J., Li, G., Wu, B. et al. Efficient Methods for Evaluating Task-Specific Uncertainty in Laser-Tracking Measurement. MAPAN 30, 105–117 (2015). https://doi.org/10.1007/s12647-014-0126-9

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