Polymers, Vol. 17, Pages 2863: Calibration Framework for Modeling Nonlinear Viscoelastic–Plastic Behavior of Bioresorbable Polymers in Finite Element Analysis for Stent Applications


Polymers, Vol. 17, Pages 2863: Calibration Framework for Modeling Nonlinear Viscoelastic–Plastic Behavior of Bioresorbable Polymers in Finite Element Analysis for Stent Applications

Polymers doi: 10.3390/polym17212863

Authors:
Nicklas Fiedler
Thomas Kleine
Stefan Oschatz
Selina Schultz
Niels Grabow
Kerstin Lebahn

Finite element analysis (FEA) is common in biomedical engineering for combining design and material development, with model validation crucial for accurate prediction of material behavior. Simplified geometries are commonly needed in stent development due to high effort in prototype manufacturing. This study outlines a methodology for FEA validation related to stent development-related FEA validation using injection-molded planar 2D substructures from a stent design with two types of polymers: poly(l-lactide) (PLLA) and poly(glycolide-co-trimethylene carbonate) (PGA-co-TMC). Specimens underwent quasi-static and cyclic testing, including loading, stress relaxation, unloading, and strain recovery. The material model coefficients for FEA were calibrated for three different constitutive models: linear elastic–plastic (LEP), Parallel Rheological Framework (PRF), and Three-Network (TN) model. The validation of planar stent segment expansion (PSSE) showed strong agreement with the experiments in deformation patterns, with varying force–displacement responses. The PRF and TN models provided better fits for behavioral predictions, with the PRF model being especially favorable for PLLA, while all models exhibited limitations for PGA-co-TMC. This study proposes a robust approach for the material modeling in stent development, enabling efficient material screening and stent design optimization through a simplified 2D validation setup. Material model accuracy depends strongly on calibration–load case congruence, while phenomenological approaches (PRF) show enhanced model robustness against load case variations compared to physically coupled models (TN).



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