Saeid Sepahi-Boroujeni, J. R. René Mayer
and Farbod Khameneifar
Article (2022)
 Open Acess document in PolyPublie and at official publisher |
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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.
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| Department: | Department of Mechanical Engineering |
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| Funders: | NSERC / CRSNG - CANRIMT2 Strategic Research Network Grant NETGP, NSERC / CRSNG - Discovery Grant |
| Grant number: | 479639–15 |
| PolyPublie URL: | https://publications.polymtl.ca/50739/ |
| Conference Title: | 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021) |
| Conference Location: | Gulf of Naples, Italy |
| Conference Date(s): | 2021-07-15 - 2021-07-17 |
| Journal Title: | Procedia CIRP (vol. 112) |
| Publisher: | Elsevier |
| 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: | 03 Feb 2026 23:21 |
| Cite in APA 7: | Sepahi-Boroujeni, S., Mayer, J. R. R., & Khameneifar, F. (2022). Prompt uncertainty estimation with GUM framework for on-machine tool coordinate metrology. Procedia CIRP, 112, 117-121. Presented at 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021), Gulf of Naples, Italy. https://doi.org/10.1016/j.procir.2022.09.045 |
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