Abstract
In the new digital environment of Industry 4.0 for Germany, Innovation 25 program for Japan, Advanced Manufacturing for USA, Intelligent Manufacturing or Made in China 2025 for China, Factories of the Future for France, etc., the measurement uncertainty needs a specific management in order to control the quality of the manufactured part. In this future digital world, where the software will have a central position in the verification of a specification, it is necessary to provide to the metrologist data with uncertainty in real time. The aim of this chapter is to present the uncertainty calculation methodologies. The common analytical and numerical methods to estimate uncertainty will be presented.
This is a preview of subscription content, log in via an institution to check access.
References
Aranda S, Linares JM, Sprauel JM (2010) Best-fit criterion within the context of likelihood maximization estimation. Measurement 43(4):538–548
Bachmann J, Linares JM, Sprauel JM, Bourdet P (2004) Aide in decision-making: contribution to uncertainties in three-dimensional measurement. Precision Engineering 28(1):78–88
Farooqui SA, Doiron T, Sahay C (2009) Uncertainty analysis of cylindricity measurements using bootstrap method. Measurement 42(4):524–531
Linares JM, Mailhé J, Sprauel JM (2006) Uncertainties of multi sensors CMM measurements applied to high quality surfaces. In: XVIII IMEKO world congress
Linares JM, Sprauel JM, Bourdet P (2009) Uncertainty of reference frames characterized by real time optical measurements: Application to Computer Assisted Orthopaedic Surgery. CIRP Annals 58(1):447–450
Mailhe J, Linares JM, Sprauel JM, Bourdet P (2008) Geometrical checking by virtual gauge, including measurement uncertainties. CIRP Annals 57(1):513–516
Maihle J, Linares JM, Sprauel JM (2009a) The statistical gauge in geometrical verification. Precision Engineering 33(4):333–341
Maihle J, Linares JM, Sprauel JM (2009b) The statistical gauge in geometrical verification. Part II. The virtual gauge and verification process. Precision Engineering 33(4):342–352
Saltelli A (2002a) Sensitivity Analysis for Importance Assessment. Risk Analysis 22(3):579–590
Saltelli A (2002b) Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications 145(2):280–297
Sobol′ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55(1–3):271–280
Weckenmann A, Eitzert H, Garmer H, Weber H (1995) Functionality-oriented evaluation and sampling strategy in coordinate metrology. Precision Engineering 17(4):244–252
Wen XL, Zhao YB, Wang DX, Pan J (2013) Adaptive Monte Carlo and GUM methods for the evaluation of measurement uncertainty of cylindricity error. Precision Engineering 37(4):856–864
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this entry
Cite this entry
Linares, JM. (2019). Uncertainty Estimation in Computational Tools in Metrology. In: Gao, W. (eds) Metrology. Precision Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-10-4912-5_20-1
Download citation
DOI: https://doi.org/10.1007/978-981-10-4912-5_20-1
Received:
Accepted:
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4912-5
Online ISBN: 978-981-10-4912-5
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering