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Proceedings 2018, 2(8), 391; https://doi.org/10.3390/ICEM18-05216

Steps towards Industrial Validation Experiments

1
Empa, Materials Science and Technology, CH-8600 Dübendorf, Switzerland
2
National Physical Laboratory, Teddington TW11 0LW, UK
3
School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
4
Industrial Systems Institute, Athena Research and Innovation Center, 265 04 Patras, Greece
5
Dantec Dynamics GmbH, D-89077 Ulm, Germany
6
Airbus Operations Ltd., Filton, Bristol BS99 7AR, UK
Presented at the 18th International Conference on Experimental Mechanics, Brussels, Belgium, 1–5 July 2018.
*
Author to whom correspondence should be addressed.
Published: 9 May 2018
PDF [878 KB, uploaded 5 June 2018]

Abstract

Imaging systems for measuring surface displacement and strain fields such as stereoscopic Digital Image Correlation (DIC) are increasingly used in industry to validate model simulations. Recently, CEN has published a guideline for validation that is based on image decomposition to compare predicted and measured data fields. The CEN guideline was evaluated in an inter-laboratory study that demonstrated its usefulness in laboratory environments. This paper addresses the incorporation of the CEN methodology into an industrial environment and reports progress of the H2020 Clean Sky 2 project MOTIVATE. First, while DIC is a well-established technique, the estimation of its measurement uncertainty in an industrial environment is still being discussed, as the current approach to rely on the calibration uncertainty is insufficient. Second, in view of the push towards virtual testing it is important to harvest existing data in the course of the V&V activities before requesting a dedicated validation experiment, specifically at higher levels of the test pyramid. Finally, it is of uttermost importance to ensure compatibility and comparability of the simulation and measurement data so as to optimize the test matrix for maximum reliability and credibility of the simulations and a quantification of the model quality.
Keywords: validation; validation metric; Digital Image Correlation; DIC; image decomposition validation; validation metric; Digital Image Correlation; DIC; image decomposition
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hack, E.; Burguete, R.; Dvurecenska, K.; Lampeas, G.; Patterson, E.; Siebert, T.; Szigeti, E. Steps towards Industrial Validation Experiments. Proceedings 2018, 2, 391.

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