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Journal of Sensors and Sensor Systems An open-access peer-reviewed journal
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Virtual assembly (VA) is a method for the quality prediction of assemblies considering local form deviations of relevant geometries. Point clouds of measured objects are registered in order to recreate the objects’ hypothetical physical assembly state, which is strongly influenced by the measurement uncertainty of individual points. Thus, we studied the propagation of uncertainties by VA. The results reveal larger propagated uncertainties by VA compared to the unconstrained Gaussian best fit.
JSSS | Articles | Volume 10, issue 1
J. Sens. Sens. Syst., 10, 101–108, 2021
https://doi.org/10.5194/jsss-10-101-2021

Special issue: Sensors and Measurement Science International SMSI 2020

J. Sens. Sens. Syst., 10, 101–108, 2021
https://doi.org/10.5194/jsss-10-101-2021

Regular research article 22 Apr 2021

Regular research article | 22 Apr 2021

Measurement uncertainty assessment for virtual assembly

Manuel Kaufmann et al.

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Cited articles

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Deutsches Institut für Normung e.V.: Geometrische Produktspezifikation (GPS) – Geometrische Tolerierung: Bezüge und Bezugssysteme (ISO 5459:2011); Deutsche Fassung EN ISO 5459:2011, 17.040.30, Beuth, Berlin, 99 pp., 2011. 
Deutsches Institut für Normung e.V.: Geometrische Produktspezifikation (GPS) – Geometrische Tolerierung: Bezüge und Bezugssysteme, 01.100.20; 17.040.10, Beuth, Berlin, 236 pp., 2017. 
Fleßner, M., Müller, A., Götz, D., Helmecke, E., and Hausotte, T.: Assessment of the single point uncertainty of dimensional CT measurements, in: 6th Conference on Industrial Computed Tomography, edited by: Diederichs, R., 6th Conference on Industrial Computed Tomography (iCT) 2016, Wels, Austria, 9–12 February 2016, available at: https://www.ndt.net/article/ctc2016/papers/ICT2016_paper_id48.pdf (last access: 18 April 2021), 2016.  
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Short summary
Virtual assembly (VA) is a method for the quality prediction of assemblies considering local form deviations of relevant geometries. Point clouds of measured objects are registered in order to recreate the objects’ hypothetical physical assembly state, which is strongly influenced by the measurement uncertainty of individual points. Thus, we studied the propagation of uncertainties by VA. The results reveal larger propagated uncertainties by VA compared to the unconstrained Gaussian best fit.
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