6.4.1 Separation of locally determined work piece deviations and measurement uncertainties for structured-light scanning of customized polymer gear wheels
- Event
- 20. GMA/ITG-Fachtagung Sensoren und Messsysteme 2019
2019-06-25 - 2019-06-26
Nürnberg, Germany - Chapter
- 6.4 Messunsicherheit und Modellbildung
- Author(s)
- A. Müller, S. Metzner, T. Hausotte - Institute of Manufacturing Metrology (FMT), Erlangen (Deutschland), D. Schubert, D. Drummer - Institute of Polymer Technology (LKT), Erlangen-Tennenlohe (Deutschland)
- Pages
- 527 - 534
- DOI
- 10.5162/sensoren2019/6.4.1
- ISBN
- 978-3-9819376-0-2
- Price
- free
Abstract
During the manufacturing of work pieces, geometrical deviations from the intended nominal geometry of the designer are inevitable. The procedure of conformance testing defined in ISO 14253-1:2018-07 is used to ensure the function of a work piece by verifying the geometrical compliance with pre-defined tolerance specifications. Depending on the measurement setup used for the validation step and the accuracy of the manufacturing process, it is possible that the measurement uncertainty is large enough to have a significant influence on the conformance evaluation. The measurement uncertainty for optical measurement systems is influenced by the surface properties of the test specimen. This contribution aims to demonstrate the complete workflow for the determination of the single point uncertainties for a given measurement task in order to separate the local work piece deviations from the systematic and random components of the measurement uncertainty. It could be shown for a demonstration scenario that different necessary coloring methods of polymer gear wheels, which are required to enable measurements using structured-light scanning, have a measureable influence on the local distribution of the measurement uncertainty. This information could then be used for downstream processes in various use cases, e.g. for the improvement of holistic tolerance simulation models or the improvement of geometrical measurements using weighted regression analysis.