FE-DOE: Finite element informed design of experiments to optimise bioinspired melt electrowritten (MEW) polymeric heart valves
Hughes, C.; Johnston, R. D.; McCarthy, D.; Klusak, E.; Growney, E.; Campbell, E.; Lally, C.
Show abstract
Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. The materials used in these devices, however, suffer from premature failure. Polymer heart valves have the potential to improve current commercial devices, offering materials with extended durability and customisation through fibre-reinforcement. Due to the wide range of available materials and structures, there is a need for a methodical approach to the design and optimisation of novel, bioinspired polymeric leaflets. This work presents a framework using FE and DOE tools to enable the creation of optimised bioinspired, 3D-printed, fibre-reinforced polymer leaflets using MEW. Here, FE models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, showing the benefit of using a bioinspired fibre reinforcement structure. Using DOE, the structural combination of MEW fibre-reinforcement and elastomeric matrix was optimised based on valve performance and leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets by utilising an in-silico approach to remove the need for manufacturing and testing of these devices.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An Investigation into Patient-Specific 3D Printed Titanium Stents and the use of Etching to Overcome Selective Laser Melting Design Constraints. 94%
- Air leaks: stapling affects porcine lungs biomechanics 93%
- A Computational Growth Framework for Biological Tissues: Application to Growth of Aortic Root Aneurysm Repaired by the V-shape Surgery 93%
Similar papers in this journal
- Fluid-Structure Interactions of Peripheral Arteries Using a Coupled in silico and in vitro Approach 94%
- Deployment of a digital twin using the coupled momentum method for fluid-structure interaction: a case study for aortic aneurysm 94%
- Biomechanical stress analysis of Type-A aortic dissection at pre-dissection, post-dissection, and post-repair states 94%
Similar papers in this journal
- A coupled atrioventricular-aortic setup for in-vitro hemodynamic study of the systemic circulation: Design, Fabrication, and Physiological relevancy 94%
- Mechanical effects of MitraClip on leaflet stress and myocardial strain in functional mitral regurgitation: A finite element modeling study. 94%
- Variability of Tissue Mechanical Response in Sus Domesticus Porcine Models from in vivo to ex vivo Conditions 92%
Similar papers in this journal
- In Silico Modeling of Transcatheter Heart Valve Oversizing and Ellipticity, Part I: Establishing Credibility of an Advanced Model 95%
- In Silico Modeling of Transcatheter Heart Valve Oversizing and Ellipticity, Part II: Effects on Leaflet Mechanics, Hemodynamics, and Stent Deflection Contributing to Thrombogenic Risk and Structural Degeneration 94%
- Computational hemodynamic indices to identify Transcatheter Aortic Valve Implantation degeneration 92%
Similar papers in this journal
- Collagen fibre-mediated mechanical damage increases calcification of bovine pericardium for use in bioprosthetic heart valves 93%
- Characterization of pediatric porcine pulmonary valves as a model for tissue engineered heart valves 93%
- Identifiability of Tissue Material Parameters from Uniaxial Tests using Multi-start Optimization 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.