CMM Influence Factors and Uncertainty Associated with Length Measurement

This paper is concerned with coordinate measuring machine (CMM) uncertainty evaluation, in particular, the uncertainties associated with point clouds and distances derived from the point cloud. The uncertainty evaluation approach is model-based following the principles of the Guide to the Expression...

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Main Author: Alistair Forbes
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/15/1/271
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author Alistair Forbes
author_facet Alistair Forbes
author_sort Alistair Forbes
collection DOAJ
description This paper is concerned with coordinate measuring machine (CMM) uncertainty evaluation, in particular, the uncertainties associated with point clouds and distances derived from the point cloud. The uncertainty evaluation approach is model-based following the principles of the Guide to the Expression of Uncertainty in Measurement and the law of the propagation of uncertainty. The paper considers a range of CMM influence factors and derives an explicit dependence for the point cloud data coordinates on the influence factors, allowing uncertainties associated with the influence factors to be propagated through to point cloud uncertainties. The paper describes the use of Gaussian processes to model kinematic and probing errors using a small number of statistical hyper-parameters. These models permit an explicit statement of the uncertainty associated with point clouds and length measurement, enabling the latter to be compared directly with a statement of the maximum permissible error in length measurement. The uncertainty evaluation methodology is direct in that it requires no optimisation nor Monte Carlo simulations.
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series Applied Sciences
spelling doaj-art-6b663b7ff520423c8e2ac054aedbd21f2025-01-10T13:14:59ZengMDPI AGApplied Sciences2076-34172024-12-0115127110.3390/app15010271CMM Influence Factors and Uncertainty Associated with Length MeasurementAlistair Forbes0National Physical Laboratory, London TW11 0LW, UKThis paper is concerned with coordinate measuring machine (CMM) uncertainty evaluation, in particular, the uncertainties associated with point clouds and distances derived from the point cloud. The uncertainty evaluation approach is model-based following the principles of the Guide to the Expression of Uncertainty in Measurement and the law of the propagation of uncertainty. The paper considers a range of CMM influence factors and derives an explicit dependence for the point cloud data coordinates on the influence factors, allowing uncertainties associated with the influence factors to be propagated through to point cloud uncertainties. The paper describes the use of Gaussian processes to model kinematic and probing errors using a small number of statistical hyper-parameters. These models permit an explicit statement of the uncertainty associated with point clouds and length measurement, enabling the latter to be compared directly with a statement of the maximum permissible error in length measurement. The uncertainty evaluation methodology is direct in that it requires no optimisation nor Monte Carlo simulations.https://www.mdpi.com/2076-3417/15/1/271coordinate metrologyGaussian process modelslength measurement capabilityuncertainty evaluation
spellingShingle Alistair Forbes
CMM Influence Factors and Uncertainty Associated with Length Measurement
Applied Sciences
coordinate metrology
Gaussian process models
length measurement capability
uncertainty evaluation
title CMM Influence Factors and Uncertainty Associated with Length Measurement
title_full CMM Influence Factors and Uncertainty Associated with Length Measurement
title_fullStr CMM Influence Factors and Uncertainty Associated with Length Measurement
title_full_unstemmed CMM Influence Factors and Uncertainty Associated with Length Measurement
title_short CMM Influence Factors and Uncertainty Associated with Length Measurement
title_sort cmm influence factors and uncertainty associated with length measurement
topic coordinate metrology
Gaussian process models
length measurement capability
uncertainty evaluation
url https://www.mdpi.com/2076-3417/15/1/271
work_keys_str_mv AT alistairforbes cmminfluencefactorsanduncertaintyassociatedwithlengthmeasurement