| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Cox, Sophie C.
University of Birmingham
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2024A genetic algorithm optimization framework for the characterization of hyper-viscoelastic materials
- 2023Tailoring absorptivity of highly reflective Ag powders by pulsed-direct current magnetron sputtering for additive manufacturing processescitations
- 2023Tailoring absorptivity of highly reflective Ag powders by pulsed-direct current magnetron sputtering for additive manufacturing processescitations
- 2022Surface Free Energy Dominates the Biological Interactions of Postprocessed Additively Manufactured Ti-6Al-4Vcitations
- 2022Controlled Release of Epigenetically-Enhanced Extracellular Vesicles from a GelMA/Nanoclay Composite Hydrogel to Promote Bone Repaircitations
- 2022The influence of thermal oxidation on the microstructure, fatigue properties, tribological and in vitro behaviour of laser powder bed fusion manufactured Ti-34 Nb-13Ta-5Zr-0.2O alloycitations
- 2022Development, characterisation, and modelling of processability of nitinol stents using laser powder bed fusioncitations
- 2022Photocurable antimicrobial silk-based hydrogels for corneal repaircitations
- 2021Surface finish of additively manufactured metalscitations
- 2021Biofilm viability checkercitations
- 2020Optimizing the antimicrobial performance of metallic glass composites through surface texturingcitations
- 2020Selective laser melting of Ti-6Al-4V: the impact of post-processing on the tensile, fatigue and biological properties for medical implant applicationscitations
- 2020Selective laser melting of ti-6al-4vcitations
- 2019Dynamic viscoelastic characterisation of human osteochondral tissuecitations
- 2018Formulation and viscoelasticity of mineralised hydrogels for use in bone-cartilage interfacial reconstructioncitations
- 2018The role of subchondral bone, and its histomorphology, on the dynamic viscoelasticity of cartilage, bone and osteochondral corescitations
- 2018Tailoring selective laser melting process for titanium drug-delivering implants with releasing micro-channelscitations
- 2016Adding functionality with additive manufacturing : fabrication of titanium-based antibiotic eluting implantscitations
Places of action
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article
A genetic algorithm optimization framework for the characterization of hyper-viscoelastic materials
Abstract
This study aims to develop an automated framework for the characterization of materials which are both hyper-elastic and viscoelastic. This has been evaluated using human articular cartilage (AC). AC (26 tissue samples from 5 femoral heads) underwent dynamic mechanical analysis with a frequency sweep from 1 to 90 Hz. The conversion from a frequency- to time-domain hyper-viscoelastic material model was approximated using a modular framework design where finite element analysis was automated, and a genetic algorithm and interior point technique were employed to solve and optimize the material approximations. Three orders of approximation for the Prony series were evaluated at N = 1, 3 and 5 for 20 and 50 iterations of a genetic cycle. This was repeated for 30 simulations of six combinations of the above all with randomly generated initialization points. There was a difference between N = 1 and N = 3/5 of approximately ~5% in terms of the error estimated. During unloading the opposite was seen with a 10% error difference between N = 5 and 1. A reduction of ~1% parameter error was found when the number of generations increased from 20 to 50. In conclusion, the framework has proved effective in characterizing human AC.