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An efficient stiffness degradation model for layered composites with arbitrarily oriented tunneling and delamination cracks
A periodic 2D finite element model is proposed to identify the axial and transverse stiffness degradation for arbitrarily oriented parallel tunneling cracks. This is achieved with a recently developed off-axis framework taking the 3D deformation into account via a special kinematic formulation. The...
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Published in: | Composites science and technology 2022-11, Vol.230, p.109729, Article 109729 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A periodic 2D finite element model is proposed to identify the axial and transverse stiffness degradation for arbitrarily oriented parallel tunneling cracks. This is achieved with a recently developed off-axis framework taking the 3D deformation into account via a special kinematic formulation. The proposed model is successfully validated against a variety of cases from the literature. Not only is the model capable of accurately predicting what previously was only possible with expensive 3D models or complex analytical methods, but at the same time, it is achieved with remarkably small finite element models which only take seconds for each simulation. A parametric study, shows that by including frictionless contact between the crack surfaces, a significant effect on the stiffness degradation is present for carbon fiber composite materials for off-axis orientations below 40°. An effect not seen for the analyzed glass fiber composites. In addition, based on the axial and transverse stiffness degradation, a method is proposed from which the amount of simultaneous tunnel cracking and delamination can be predicted. A Fortran-based user subroutine and supplementary Python scripts for the commercial finite element code Abaqus are made available as a co-published data-repository reference.
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ISSN: | 0266-3538 1879-1050 |
DOI: | 10.1016/j.compscitech.2022.109729 |