Fatigue Analysis of Continuously Carbon Fiber Reinforced Laminates

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Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Lightweight constructions and the reduction of production time and costs is of increasingly importance. Therefore, engineers make a lot of effort to replace metallic parts by other materials. Carbon fiber reinforced laminates are suitable in many cases because of their high specific strength and the low specific weight. The available material-data of this material group from datasheets are mostly static values like tensile strength and fracture elongation. For the fatigue assessment of parts regarding geometry, loading conditions and material behavior, static material data are not sufficient, but also the knowledge of the local S-N curve is necessary. Component specific effects, such as fiber orientation, type of loading, mean stress, temperature, production process and many more, essentially influence these local S-N curves, determined by the material. For fatigue life prediction an assessment method was established, which takes into account the fiber orientation and considers different types of failure mechanisms like fiber fracture, inter fiber fracture and delamination. As input data, structural stresses are needed analyzed by the Finite Element Method, where the local orthotropic material behavior for each ply has been considered. A hypothesis for fatigue life prediction of orthotropic carbon fiber reinforced materials has been derived based on the well-known static failure criterion of Puck, implemented into a standard fatigue software tool and verified so far with component tests. The hypothesis is applicable even for general random-like and multi-axial loads.
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DOI
https://doi.org/10.4271/2017-01-0327
Pages
11
Citation
Gaier, C., Fischmeister, S., Maier, J., and Pinter, G., "Fatigue Analysis of Continuously Carbon Fiber Reinforced Laminates," SAE Int. J. Engines 10(2):305-315, 2017, https://doi.org/10.4271/2017-01-0327.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0327
Content Type
Journal Article
Language
English