Yield surface derivation for a structural adhesive based on multiaxial experiments

Wentingmann, Michael; Manousides, Nikolas; Antoniou, Alexandros; Balzani, Claudio

doi:10.1016/j.polymertesting.2022.107648

Cited by 12 publications

(55 citation statements)

References 19 publications

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“…Qualitative porosity scans are shown in Figure 1 for all specimens in this work. Similar to previous studies 4,22,23 , the porosity level is negligible in the test section. Only a small amount of pores was found at the end and outer perimeter of the tapered section, which is a result of race tracking effects in the injection process.…”

Section: Specimen Design and Manufacturingsupporting

confidence: 86%

“…In this work, an epoxy-based and short glass fiber-reinforced adhesive is analyzed. As in previous studies 4,22,23…”

Section: Materials Systemsupporting

confidence: 55%

“…Therefore, the fiber orientation is linked to the injection speed, which is lower in close proximity to the mold due to friction. Due to the excellent manufacturing quality of the specimens and the low standard deviation of the results observed in previous studies 4,22,23 , it is expected that the fiber orientation is approximately the same in all specimens.…”

Section: Specimen Design and Manufacturingmentioning

confidence: 91%

“…The adoption of the yield strength ratio as a metric for ductility is supported by a discussion on the yield locus of the adhesive presented in a previous publication 22 . Therein, the Drucker-Prager 32 criterion was identified as a significantly better fit for rather ductile (machinemixed) specimens in comparison to the von Mises-Schleicher 33 criterion, which was proposed for rather brittle (hand-mixed) specimens by other authors 34,35 .…”

Section: Influence Of Materials Ductilitymentioning

confidence: 92%

“…Two load levels were chosen in such a way that the specimens fail in between 10 4 and 10 5 cycles. This way, the specimens fail close to the yield point of the material 22 in terms of an equivalent stress amplitude.…”

Section: Biaxial Fatigue Testing Campaignmentioning

confidence: 99%

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Effects of non-proportionality and tension-compression asymmetry on the fatigue life prediction of equivalent stress criteria

Wentingmann

Manousides

Antoniou

et al. 2023

Preprint

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Biaxial tension/compression-torsion fatigue tests with varying levels of non-proportionality were performed employing a structural adhesive designed for wind turbine rotor blades. The cycles to failure were found to be independent of the level of non-proportionality. It is demonstrated that numerical fatigue life predictions via rainflow-counted equivalent stress histories are not able to replicate these experimental observations and overestimate the fatigue life up to a hundredfold. The tension-compression asymmetry of the adhesive resulted in significant damage prediction differences depending on the stress space representation of the Haigh diagram. If not properly taken care of, the asymmetry will also lead to non-conservative results. While demonstrated with a short fiber reinforced adhesive, the results can be transferred to other materials.

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Section: Specimen Design and Manufacturingsupporting

confidence: 86%

“…In this work, an epoxy-based and short glass fiber-reinforced adhesive is analyzed. As in previous studies 4,22,23…”

Section: Materials Systemsupporting

confidence: 55%

Section: Specimen Design and Manufacturingmentioning

confidence: 91%

Section: Influence Of Materials Ductilitymentioning

confidence: 92%

Section: Biaxial Fatigue Testing Campaignmentioning

confidence: 99%

See 3 more Smart Citations

Effects of non-proportionality and tension-compression asymmetry on the fatigue life prediction of equivalent stress criteria

Wentingmann

Manousides

Antoniou

et al. 2023

Preprint

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Using conditional generative adversarial networks for the prediction of stresses in an adhesive composite

Khan,

Balzani

2023

Proc Appl Math and Mech

Self Cite

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Fiber reinforced adhesives are an integral part of wind energy turbine blades and play an important role in evaluation of structural integrity of the blades. Digitization of manufacturing processes and demand for fast and efficient stress analysis argues for the application of deep learning. As a step forward in using deep learning to predict structural properties in composite, the work would explore the applicability of deep learning methods to 2D fiber reinforced composite. In this work, a composite adhesive used in wind energy turbine blades is used as an illustrative example. The composite adhesive when viewed using a CT scan machine shows a heterogeneous structure with two phases, a softer matrix phase and a much stiff fiber phase. For this purpose, several CT scan images of the fiber adhesive composite are used. The images on subsequent FEA analysis serves as the training data for the deep learning framework. Once trained, the neural network would be able to predict the stress distributions in the structure. We apply conditional adversarial networks as general purpose solutions to problems focused on stress prediction, which could be thought of as an image‐to‐image translation from the microstructure domain to the stress domain. These networks in addition to learning the mapping from input image to output image also learn a loss function to train for this mapping. This makes the method more generic because otherwise different loss formulations for different problems have to be formulated.

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Manufacturing and toughening effects on the material properties of wind turbine blade adhesives

Srinivasan

Vassilopoulos

2022

Polymer Testing

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Yield surface derivation for a structural adhesive based on multiaxial experiments

Cited by 12 publications

References 19 publications

Effects of non-proportionality and tension-compression asymmetry on the fatigue life prediction of equivalent stress criteria

Effects of non-proportionality and tension-compression asymmetry on the fatigue life prediction of equivalent stress criteria

Using conditional generative adversarial networks for the prediction of stresses in an adhesive composite

Manufacturing and toughening effects on the material properties of wind turbine blade adhesives

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