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On the Progressive Failure Simulation and Experimental Validation of Fiber Metal Laminate Bolted Joints
- Publication Year :
- 2019
-
Abstract
- The joint efficiency of mechanical joints in fiber-reinforced composite materials can be increased significantly by embedding metal plies in the composite layup, as in the case of fiber metal laminates. In this work, a novel finite element-based framework is presented for predicting the static progressive failure behavior of fiber metal laminate bolted joints. Motivated from experimental observations, the proposed framework accounts not only for damage in the fiber-reinforced composite plies, but also for different types of damage of the metallic inlays. For this purpose, user-defined continuum-damage constitutive models are formulated and employed in the general-purpose FE software Abaqus/Implicit for the fiber-reinforced polymer plies and the embedded metallic inlays. Accordingly, the interaction between different failure modes and the influence of the bolt’s washer on the damage evolution is considered to increase the predictive quality. To demonstrate the applicability and validity of the developments, predictive simulations are carried out and compared to conducted experimental measurements on different fiber metal laminate grades (GFRP/stainless steel and CFRP/titanium) with a wide range of metal volume content, reaching from 0% (pure composite material) to 50%.
- Subjects :
- Washer
Fiber metal laminate
Experimental validation
Materials science
Composite number
02 engineering and technology
Fibre-reinforced plastic
021001 nanoscience & nanotechnology
Continuum damage modeling
Finite element method
Fiber metal laminates
020303 mechanical engineering & transports
0203 mechanical engineering
Static progressive failure prediction
Bolted joint
Mechanical joint
Ceramics and Composites
Bolted joints
Fiber
Composite material
0210 nano-technology
Civil and Structural Engineering
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7ccbee2dea78032a6a3eecbbfd7b5c16