Measurement uncertainty assessment for virtual assembly
<p>Virtual assembly (VA) is a method for datum definition and 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. By VA, the geome...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-04-01
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| Series: | Journal of Sensors and Sensor Systems |
| Online Access: | https://jsss.copernicus.org/articles/10/101/2021/jsss-10-101-2021.pdf |
| Summary: | <p>Virtual assembly (VA) is a method for datum definition and 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. By VA, the geometrical verification becomes more accurate and, thus, increasingly function oriented. The VA algorithm is a nonlinear, constrained derivate of the Gaussian best fit algorithm, where outlier points strongly influence the registration result. In order to assess the robustness of the developed algorithm, the propagation of measurement uncertainties through the nonlinear transformation due to VA is studied. The work compares selected propagation methods distinguished from their levels of abstraction. The results reveal larger propagated uncertainties by VA compared to the unconstrained Gaussian best fit.</p> |
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| ISSN: | 2194-8771 2194-878X |