Statistical point cloud model to investigate measurement uncertainty in coordinate metrology

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

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: Feb 2, 2022
• 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