Data fusion of local and global sensors within a Global Reference System in aeroplane assembly

Meike Huber; Christoph Nicksch; Robert H. Schmitt

doi:10.1515/teme-2022-0008

Article

Data fusion of local and global sensors within a Global Reference System in aeroplane assembly

Meike Huber, born in 1994, completed her studies of Mathematics with the specialization on Statistics and Data Science at the RWTH Aachen University in 2019. She is now working as a research associate in the Department Model-based Systems at the Chair for Metrology and Quality Management of Prof. Dr.-Ing. Robert H. Schmitt at WZL of RWTH Aachen University.
,

Christoph Nicksch, born in 1990, completed his studies of Mechanical Engineering with the specialization on Automation Engineering at the RWTH Aachen University in 2017. He is now working as a research associate in the Department Model-based Systems at the Chair for Metrology and Quality Management of Prof. Dr.-Ing. Robert H. Schmitt at WZL of RWTH Aachen University.
and

Prof. Dr.-Ing. Robert H. Schmitt, born in 1961, completed his studies of Electrical Engineering with the specialisation on Communications Engineering at the Technical University of Aachen and became research associate at the Chair for Metrology and Quality Management. His work there focused on production-related Metrology and Communications Engineering in an automated environment. In 1997 Professor Schmitt moved on to MAN Nutzfahrzeuge AG (commercial vehicles) in Munich where he took on leading positions in the fields of Quality and Production. In 2002 he assumed responsibility for the commercial vehicle production in Steyr, Austria. On July 1st, 2004 he was appointed as professor at the Technical University of Aachen. As head of the Chair for Metrology and Quality Management at the Laboratory for Machine he serves as a member of the Board of Directors at the Laboratory for Machine Tools and Production Engineering (WZL) and the Fraunhofer Institute for Production Technology IPT.

Published/Copyright: April 21, 2022

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal tm - Technisches Messen Volume 89 Issue 10

Abstract

Currently, aeroplane assembly is carried out in rigid, aeroplane-specific assembly lines which causes high costs for reconfiguration. One approach to reduce the costs is to use automated lineless assembly systems. To enable automated reconfiguration of assembly processes, continuous availability of factory-wide position data is required. When providing these data, a challenge is the combination of large-volume, “global” measurement sensors and high-precision, “local” measurement sensors to a single easily interpretable indication. The aim of this research work is the fusion of local and global measurement data within a so-called Global Reference System (GRS). For data fusion, sensors are linked to a reference measurement system covering the entire assembly area. The relationships between the coordinate systems of the individual measurement systems are described using homogeneous transformations. Using Kalman filters, the transformed measurement data are fused into a vector containing information about the pose of aeroplane components. Furthermore, a GUM-compliant statement about the measurement uncertainty of the fused sensor data is made by specifying the covariance matrix. The method is validated using a demonstrator covering the essential aspects of the assembly process. The result is a validated procedure for data fusion and for the determination of the combined measurement uncertainty in a GRS.

Zusammenfassung

Derzeit erfolgt die Flugzeugmontage in starren, flugzeugspezifischen Montagelinien, was hohe Kosten bei der Neukonfiguration verursacht. Ein Ansatz zur Kostensenkung ist der Einsatz automatisierter, linienloser Montagesysteme. Um eine automatisierte Rekonfiguration von Montageprozessen zu ermöglichen, ist die kontinuierliche Verfügbarkeit von fabrikweiten Positionsdaten erforderlich. Eine Herausforderung bei der Bereitstellung dieser Daten ist die Kombination von großvolumigen, „globalen“ Messsensoren und hochpräzisen, „lokalen“ Messsensoren zu einer einzigen, leicht interpretierbaren Größe. Ziel der hier vorgestellten Arbeit ist die Fusion von lokalen und globalen Messdaten in einem so genannten Globalen Referenzsystem (GRS). Für die Datenfusion werden die Sensoren mit einem Referenzmesssystem verbunden, das den gesamten Montagebereich abdeckt. Die Beziehungen zwischen den Koordinatensystemen der einzelnen Messsysteme werden durch homogene Transformationen beschrieben. Die transformierten Messdaten werden mit Hilfe von Kalman-Filtern zu einem Vektor fusioniert, der Informationen über die Lage der Flugzeugkomponenten enthält. Weiterhin wird eine GUM-konforme Aussage über die Messunsicherheit der fusionierten Sensordaten durch Angabe der Kovarianzmatrix getroffen. Das Verfahren wird anhand eines Demonstrators validiert, der die wesentlichen Aspekte der Flugzeugmontage abdeckt. Das Ergebnis ist ein validiertes Verfahren zur Datenfusion und zur Bestimmung der kombinierten Messunsicherheit innerhalb eines GRS.

Keywords: Measurement uncertainty determination; sensor data fusion; global reference system

Schlagwörter: Messunsicherheitsbestimmung; Sensordatenfusion; Globales Referenzsystem

Funding source: Bundesministerium für Wirtschaft und Energie

Award Identifier / Grant number: LUFO V3-2018-2022/FKZ: 20x1708G

Funding statement: The research was conducted as part of the BMWi research project “iVeSPA – Integrierte Verifikation, Sensoren und Positionierung in der Flugzeugfertigung“ (LUFO V3-2018-2022/FKZ: 20x1708G).

About the authors

Meike Huber

Meike Huber, born in 1994, completed her studies of Mathematics with the specialization on Statistics and Data Science at the RWTH Aachen University in 2019. She is now working as a research associate in the Department Model-based Systems at the Chair for Metrology and Quality Management of Prof. Dr.-Ing. Robert H. Schmitt at WZL of RWTH Aachen University.

Christoph Nicksch

Christoph Nicksch, born in 1990, completed his studies of Mechanical Engineering with the specialization on Automation Engineering at the RWTH Aachen University in 2017. He is now working as a research associate in the Department Model-based Systems at the Chair for Metrology and Quality Management of Prof. Dr.-Ing. Robert H. Schmitt at WZL of RWTH Aachen University.

Robert H. Schmitt

Prof. Dr.-Ing. Robert H. Schmitt, born in 1961, completed his studies of Electrical Engineering with the specialisation on Communications Engineering at the Technical University of Aachen and became research associate at the Chair for Metrology and Quality Management. His work there focused on production-related Metrology and Communications Engineering in an automated environment. In 1997 Professor Schmitt moved on to MAN Nutzfahrzeuge AG (commercial vehicles) in Munich where he took on leading positions in the fields of Quality and Production. In 2002 he assumed responsibility for the commercial vehicle production in Steyr, Austria. On July 1st, 2004 he was appointed as professor at the Technical University of Aachen. As head of the Chair for Metrology and Quality Management at the Laboratory for Machine he serves as a member of the Board of Directors at the Laboratory for Machine Tools and Production Engineering (WZL) and the Fraunhofer Institute for Production Technology IPT.

Acknowledgment

Special thanks go to the “Fraunhofer-Institut für Fabrikbetrieb und –automatisierung” IFF in Magdeburg, which was responsible for developing and providing the demonstrator to validate the method presented here.

References

1. Andrei, O. (2017): 3D affine coordinate transformations. Available online at https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1065595&dswid=3702.Search in Google Scholar

2. Bloomenthal, J.; Rokne, J. (1994): Homogeneous coordinates. In The Visual Computer 11 (1), pp. 15–26. DOI: 10.1007/BF01900696.Search in Google Scholar

3. Cuypers, W.; van Gestel, N.; Voet, A.; Kruth, J.-P.; Mingneau, J.; Bleys, P. (2009): Optical measurement techniques for mobile and large-scale dimensional metrology. In Optics and Lasers in Engineering 47 (3-4), pp. 292–300. DOI: 10.1016/j.optlaseng.2008.03.013.Search in Google Scholar

4. Demeester, F.; Dresselhaus, M.: Referenzsysteme für wandlungsfähige Produktion. In AWK Aachener Wekzeugmaschinen-Kolloquium 2011, pp. 449–477.Search in Google Scholar

5. Durrant-Whyte, H.; Henderson, T. C. (2016): Multisensor Data Fusion. In B. Siciliano, O. Khatib (Eds.): Springer Handbook of Robotics. Cham: Springer International Publishing, pp. 867–896.10.1007/978-3-319-32552-1_35Search in Google Scholar

6. Estler, W. T.; Edmundson, K. L.; Peggs, G. N.; Parker, D. H. (2002): Large-Scale Metrology – An Update. In CIRP Annals 51 (2), pp. 587–609. DOI: 10.1016/S0007-8506(07)61702-8.Search in Google Scholar

7. Franceschini, F. (2011): Distributed large-scale dimensional metrology. New insights. London: Springer (SpringerLink: Bücher). Available online at https://books.google.de/books?id=bIwbFtXyMcMC.10.1007/978-0-85729-543-9Search in Google Scholar

8. Gan, Q.; Harris, C. J. (2001): Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. In IEEE Trans. Aerosp. Electron. Syst. 37 (1), pp. 273–279. DOI: 10.1109/7.913685.Search in Google Scholar

9. Khaleghi, B.; Khamis, A.; Karray, F. O.; Razavi, S. N. (2013): Multisensor data fusion: A review of the state-of-the-art. In Information Fusion 14 (1), pp. 28–44. DOI: 10.1016/j.inffus.2011.08.001.Search in Google Scholar

10. Liu, H.; Fang, Y. (2021): Direct 3D coordinate transformation based on the affine invariance of barycentric coordinates. In Journal of Spatial Science 66 (3), pp. 425–444. DOI: 10.1080/14498596.2019.1658653.Search in Google Scholar

11. Muelaner, J. E.; Maropoulos, P. G. (2014): Large Volume Metrology Technologies for the Light Controlled Factory. In Procedia CIRP 25, pp. 169–176. DOI: 10.1016/j.procir.2014.10.026.Search in Google Scholar

12. Müller, R.; Esser, M.; Mbarek, T.; Biyiklioglu, N.; Quinders, S. (2011): Development of Modular Low Cost Automation Systems for Aircraft Assembly. In: Aerospace Technology Conference and Exposition: SAE International, checked on 2011.10.4271/2011-01-2638Search in Google Scholar

13. Mutilba, U.; Gomez-Acedo, E.; Kortaberria, G.; Olarra, A.; Yagüe-Fabra, J. A. (2017): Traceability of On-Machine Tool Measurement: A Review. In Sensors (Basel, Switzerland) 17 (7), p. 1605. DOI: 10.3390/s17071605.Search in Google Scholar PubMed PubMed Central

14. Nicksch, C.; Kluge-Wilkes, A.; Huber, M.; Schmitt, R. H. (2020): Global Reference System for factory-wide integration of metrology enabling flexible automation in aeroplane assembly – requirements, concept and suitable technologies. In Procedia Manufacturing 52, pp. 89–94. DOI: 10.1016/j.promfg.2020.11.017.Search in Google Scholar

15. Nicksch, C.; Sabzehi, M.; Schmitt, R. H. (2022): Virtual indoor-GPS for measurement uncertainty determination in reconfigurable environments. In Production Engineering. DOI: 10.1007/s11740-022-01111-4.Search in Google Scholar

16. Peggs, G. N.; Maropoulos, P. G.; Hughes, E. B.; Forbes, A. B.; Robson, S.; Ziebart, M.; Muralikrishnan, B. (2009): Recent developments in large-scale dimensional metrology. In Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223 (6), pp. 571–595. DOI: 10.1243/09544054JEM1284.Search in Google Scholar

17. Schmitt, R. H.; Corves, B.; Loosen, P.; Brecher, C.; Jeschke, S.; Kimmelmann, W. et al.(2017): Cognition-Enhanced, Self-optimizing Assembly Systems. In C. Brecher, D. Özdemir (Eds.): Integrative production technology. Theory and applications. Cham, Switzerland: Springer, pp. 877–990.10.1007/978-3-319-47452-6_10Search in Google Scholar

18. Schmitt, R. H.; Peterek, M.; Morse, E.; Knapp, W.; Galetto, M.; Härtig, F. et al.(2016): Advances in Large-Scale Metrology – Review and future trends. In CIRP Annals 65 (2), pp. 643–665. DOI: 10.1016/j.cirp.2016.05.002.Search in Google Scholar

19. Schmitt, R. H.; Voigtmann, C. (2017): Sensorinformationen als Dienst – Baustein der vernetzten Produktion. In tm – Technisches Messen 84 (5), pp. 296–309. DOI: 10.1515/teme-2017-0006.Search in Google Scholar

Received: 2022-01-14

Accepted: 2022-03-24

Published Online: 2022-04-21

Published in Print: 2022-10-31

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/teme-2022-0008

Keywords for this article

Measurement uncertainty determination; sensor data fusion; global reference system