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Statistical point cloud model to investigate measurement uncertainty in coordinate metrology

Senin, N.; Catalucci, S.; Moretti, M.; Leach, R.K.

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Authors

N. Senin

S. Catalucci

M. Moretti



Abstract

In this work an approach to investigate measurement uncertainty in coordinate metrology is presented, based on fitting Gaussian random fields to high-density point clouds produced by measurement repeats. The fitted field delivers a depiction of the spatial distribution of random measurement error over a part geometry, and can incorporate local bias information through further measurement or with the use of an external model to obtain a complete, spatial uncertainty map. The statistical model also allows the application of Monte Carlo simulation to investigate how error propagates through the data processing pipeline ultimately affecting the determination of features of size and the verification of conformance to specifications. The proposed approach is validated through application to simulated test cases involving known measurement error, and then applied to a real part, measured with optical and contact technologies. The results indicate the usefulness of the approach to estimate measurement uncertainty and to investigate performance and behaviour of measurement solutions applied to the inspection and verification of industrial parts. The approach paves the way for the implementation of automated measurement systems capable of self-assessment of measurement performance.

Citation

Senin, N., Catalucci, S., Moretti, M., & Leach, R. (2021). Statistical point cloud model to investigate measurement uncertainty in coordinate metrology. Precision Engineering, 70, 44-62. https://doi.org/10.1016/j.precisioneng.2021.01.008

Journal Article Type Article
Acceptance Date Jan 22, 2021
Online Publication Date Feb 1, 2021
Publication Date 2021-07
Deposit Date Jan 25, 2021
Publicly Available Date Mar 28, 2024
Journal Precision Engineering
Print ISSN 0141-6359
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 70
Pages 44-62
DOI https://doi.org/10.1016/j.precisioneng.2021.01.008
Public URL https://nottingham-repository.worktribe.com/output/5268852
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0141635921000118

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