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Prompt uncertainty estimation with GUM framework for on-machine tool coordinate metrology

Saeid Sepahi-Boroujeni, J. R. René Mayer and Farbod Khameneifar

Paper (2021)

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Abstract

This paper validates the uncertainty evaluated following the Guide to the Expression of Uncertainty in Measurement (GUM) for on-machine probing with a five-axis machine tool. A partly synthetic input covariance matrix is assembled for Monte Carlo and GUM frameworks, which separately estimate the uncertainty of on-machine probed point sets and obtained geometric features. The differences between the GUM and Monte Carlo results lie within the stipulated tolerances with comparable coverage regions and marginal distributions. This validates the GUM framework, which is on average 24 and 249 times faster for on-machine measurement of a gauge block and a precision sphere, respectively.

Uncontrolled Keywords

Uncertainty; On-machine measurement; GUM; Monte Carlo; Five-axis machine tool

Department: Department of Mechanical Engineering
PolyPublie URL: https://publications.polymtl.ca/50739/
Conference Title: 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021)
Conference Location: Gulf on Naples, Italy
Conference Date(s): 2021-07-14 - 2021-07-16
Editors: Roberto Teti and Doriana D'addona
Journal Title: Procedia CIRP (vol. 112)
Publisher: Elsevier B.V.
DOI: 10.1016/j.procir.2022.09.045
Official URL: https://doi.org/10.1016/j.procir.2022.09.045
Date Deposited: 18 Apr 2023 15:00
Last Modified: 05 Apr 2024 11:54
Cite in APA 7: Sepahi-Boroujeni, S., Mayer, J. R. R., & Khameneifar, F. (2021, July). Prompt uncertainty estimation with GUM framework for on-machine tool coordinate metrology [Paper]. 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021), Gulf on Naples, Italy (5 pages). Published in Procedia CIRP, 112. https://doi.org/10.1016/j.procir.2022.09.045

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