Skip to main content
Log in

Contributions to the adaptive Monte Carlo method

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

The GUM Supplement 1 presented the adaptive Monte Carlo (AMC) method. A basic implementation of an AMC procedure involves carrying out an increasing number of Monte Carlo trials until four parameters have stabilized in a statistical sense. Although the AMC method has been successfully used for uncertainty evaluations, the amount of stored data was seen as significant, and even after achieving stability, it can be lost with the increase in the number of trials. To overcome these problems, two modifications to the AMC method were proposed, implemented and validated. The first is related to data storage, while the second consists of applying an alternative criterion to assess convergence. After modifications, the AMC was named the modified adaptive Monte Carlo (MAMC) method and was applied when estimating the uncertainty of measurements carried out with a micrometer. The MAMC effectiveness was validated through the comparison of the uncertainty values and those from the application of the GUM and AMC methods. Under the evaluated experimental conditions, the MAMC showed greater repeatability when compared to AMC, regarding the number of trials to be carried out. This factor contributes toward the higher reliability of this method. The amount of data to be stored and manipulated through the application of the MAMC method was decreased significantly. This fact may increase the adoption of the AMC method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. BIPM, IEC, IDCC, et al (2008) JCGM 100:2008 Evaluation of measurement data—guide to the expression of uncertainty in measurement

  2. Kruth J-P, Van Gestel N, Bleys P, Welkenhuyzen F (2009) Uncertainty determination for CMMs by Monte Carlo simulation integrating feature form deviations. CIRP Ann 58:463–466. https://doi.org/10.1016/j.cirp.2009.03.028

    Article  Google Scholar 

  3. Désenfant M, Priel M (2017) Reference and additional methods for measurement uncertainty evaluation. Measurement 95:339–344. https://doi.org/10.1016/j.measurement.2016.10.022

    Article  Google Scholar 

  4. Wen X, Zhao Y, Wang D, Pan J (2013) Adaptive Monte Carlo and GUM methods for the evaluation of measurement uncertainty of cylindricity error. Precis Eng 37:856–864. https://doi.org/10.1016/j.precisioneng.2013.05.002

    Article  Google Scholar 

  5. Weckenmann A, Knauer M, Killmaier T (2001) Uncertainty of coordinate measurements on sheet-metal parts in the automotive industry. J Mater Process Technol 115:9–13. https://doi.org/10.1016/S0924-0136(01)00758-0

    Article  Google Scholar 

  6. ISO—International Organization for Standardization (2017) ISO/IEC 17025:2017 General requirements for the competence of testing and calibration laboratories

  7. Schwenke H, Siebert BRL, Wäldele F, Kunzmann H (2000) Assessment of uncertainties in dimensional metrology by Monte Carlo simulation: proposal of a modular and visual software. CIRP Ann 49:395–398. https://doi.org/10.1016/S0007-8506(07)62973-4

    Article  Google Scholar 

  8. BIPM, IEC, IFCC, et al (2008) JCGM 101:2008 Evaluation of measurement data—supplement 1 to the “guide to the expression of uncertainty in measurement”—propagation of distributions using a Monte Carlo method 90

  9. Yang C, Kumar M (2019) An adaptive Monte Carlo method for uncertainty forecasting in perturbed two-body dynamics. Acta Astronaut 155:369–378. https://doi.org/10.1016/j.actaastro.2018.05.053

    Article  Google Scholar 

  10. Piratelli-Filho A, DI Giacomo B (2003) Uncertainty evaluation in small angle calibration using ISO GUM approach and Monte Carlo Method. In: XVII IMEKO world congress metrology in the 3rd MILLenium. Dubrovnik, Croatia

  11. Andolfatto L, Mayer JRR, Lavernhe S (2011) Adaptive Monte Carlo applied to uncertainty estimation in five axis machine tool link errors identification with thermal disturbance. Int J Mach Tools Manuf 51:618–627. https://doi.org/10.1016/j.ijmachtools.2011.03.006

    Article  Google Scholar 

  12. Matus M (2012) Uncertainty of the variation in length of gauge blocks by mechanical comparison: a worked example. Meas Sci Technol 23:1–6. https://doi.org/10.1088/0957-0233/23/9/094003

    Article  Google Scholar 

  13. Arantes LJ, Fernandes KA, Schramm CR et al (2017) The roughness characterization in cylinders obtained by conventional and flexible honing processes. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-017-0544-2

    Article  Google Scholar 

  14. Mahmoud GM, Hegazy RS (2017) Comparison of GUM and Monte Carlo methods for the uncertainty estimation in hardness measurements. Int J Metrol Qual Eng 8:14. https://doi.org/10.1051/ijmqe/2017014

    Article  Google Scholar 

  15. Widmaier T, Hemming B, Juhanko J et al (2017) Application of Monte Carlo simulation for estimation of uncertainty of four-point roundness measurements of rolls. Precis Eng 48:181–190. https://doi.org/10.1016/j.precisioneng.2016.12.001

    Article  Google Scholar 

  16. Rezende Júnior MV, Leal JES, Pires RR et al (2019) Traceability for measurements carried out on incremental step loading equipment. J Braz Soc Mech Sci Eng 41:316. https://doi.org/10.1007/s40430-019-1817-5

    Article  Google Scholar 

  17. Balsamo A, Di Ciommo M, Mugno R et al (1999) Evaluation of CMM uncertainty through Monte Carlo simulations. Ann CIPR 48:425–428

    Google Scholar 

  18. Ramu P, Yagüe JA, Hocken RJ, Miller J (2011) Development of a parametric model and virtual machine to estimate task specific measurement uncertainty for a five-axis multi-sensor coordinate measuring machine. Precis Eng 35:431–439. https://doi.org/10.1016/j.precisioneng.2011.01.003

    Article  Google Scholar 

  19. Acero R, Santolaria J, Pueo M, Abad J (2016) Uncertainty estimation of an indexed metrology platform for the verification of portable coordinate measuring instruments. Measurement 82:202–220. https://doi.org/10.1016/j.measurement.2015.12.024

    Article  Google Scholar 

  20. Bali NP (2005) Golden real analysis. Firewall Media, New Delhi, Boston

    Google Scholar 

  21. ASTM (2019) ASTM E29-13: standard practice for using significant digits in test data to determine conformance with specifications. ASTM Interational i, pp 1–5. https://doi.org/10.1520/E0029-13R19.2

  22. ISO—International Organization for Standardization (2013) ISO 7870-2: control charts—part 2: Shewhart control charts

  23. Leal J (2016) Uma abordagem alternativa para avaliação da convergência do método de Monte Carlo adaptativo do GUM S1. Universidade Federal de Uberlândia, Uberlândia

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José Eduardo Silveira Leal.

Additional information

Technical Editor: José Roberto de França Arruda.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leal, J.E.S., da Silva, J.A. & Arencibia, R.V. Contributions to the adaptive Monte Carlo method. J Braz. Soc. Mech. Sci. Eng. 42, 462 (2020). https://doi.org/10.1007/s40430-020-02548-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-020-02548-3

Keywords

Navigation