Predictive quality control of hybrid metal-CFRP components using information fusion View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04

AUTHORS

Dietrich Berger, Marielouise Zaiß, Gisela Lanza, Jannik Summa, Michael Schwarz, Hans-Georg Herrmann, Markus Pohl, Fabian Günther, Markus Stommel

ABSTRACT

The paper presents an approach to determine the durability of hybrid metal-CFRP components combining the results of non-destructive testing (ndt) and finite element simulation The advantage of hybrid metal-CFRP components lies in the use of the properties of the materials used. CFRP parts with higher specific stiffness and strength are combined with metallic joining points, so that established joining processes for metal components can be applied to these lightweight components. In order to further promote the use of these hybrids in industry, it is necessary to guarantee a high level of component reliability through 100% quality control in order to avoid production-related defects. These defects such as delamination or fibre disorientation however vary in shape, size and position and lead to different effects on the part performance and reliability. Therefore the presented approach includes the application of non-destructive testing methods that are applied as in-line quality control measures in order to determine defect characteristics of the inspected parts. Due to the novelty of the component under test it is necessary to evaluate the individual criticality of detected defects and how they affect part performance during the testing procedure. Therefore the acquired ndt-data is used in finite element simulations where defect characteristics are added to the component model and whose effects on part reliability are evaluated. The generation of additional information combining non-destructive testing and simulation is referred to as data fusion. In order to evaluate the validity of the presented approach the determined part performances are compared to experimental mechanic tests. More... »

PAGES

161-172

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11740-018-0816-1

DOI

http://dx.doi.org/10.1007/s11740-018-0816-1

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https://app.dimensions.ai/details/publication/pub.1101897208


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