An Investigation of a Prediction Method for Failure in the Transition Zones of Locally Reinforced CFRP Materials by Steel Hybridization

Master Thesis (2018)
Author(s)

K. Kořán (TU Delft - Aerospace Engineering)

Contributor(s)

RC Alderliesten – Mentor

Faculty
Aerospace Engineering
Copyright
© 2018 Krystof Kořán
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Krystof Kořán
Graduation Date
13-07-2018
Awarding Institution
Delft University of Technology
Programme
['Aerospace Engineering']
Faculty
Aerospace Engineering
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Abstract

Local metal hybridization is a practice, wherein composite layers are substituted by equally thick metallic foils in a finite region of the laminate. The method can be used to locally increase the bearing strength of the laminate, but also introduces a potential weak-point. The transition zone (TZ) between the fully composite and hybrid laminate must be carefully designed in order to avoid premature failure in this region.

In this research, a methodology is designed to predict failure behavior in the TZ under a tension-bending loading case. A tool is designed for a combined loading test, in which a specimen is eccentrically loaded in tension. Specimens with a tailored geometry and various TZ arrangements were designed and manufactured. With the use of digital image correlation and observations by microscopy, the results of the tests are used to validate a finite element model. Interlaminar failure is modeled using cohesive zone interfaces with parameters validated by experimental correlation. The Cuntze criterion is applied with a damage degradation model for characterizing intralaminar failure. Characteristics of a good TZ design are recognized based on knowledge of the sequence of events leading to failure. The principle of a good design is to increase the moment of inertia by placing steel foils at outer positions, to the extent where the increased tendency for delamination does not become critical. A methodology is set up as a tool for future designers to evaluate and (algorithmically) optimize their local hybridization designs.

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