Mechanical characterization of hydrolysis effects on the stiffness of bioabsorbable polymeric filaments: An experimental and modeling approach based on a simple constitutive damage model

Abstract

This manuscript presents an experimental and modeling approach in order to characterize the stiffness loss of bioabsorbable polymer filaments due to hydrolysis. In this regard, bioabsorbable suture yarns (poly(lactic-co-glycolic) acid—PLGA) were chosen as a representative material for the present investigation. The observed mechanical response was characterized by means of a thermodynamically consistent constitutive variational framework. Usually, two different damage variables are assumed to take place in this class of materials: a hydrolytic damage (long-term degradation) and a strain-driven damage (short-term degradation). This work concerns the proposition of a constitutive model that only considers the hydrolytic damage, in which a specific strain energy and a proper dissipation damage potential were tailored to model the tested material. A nonlinear curve fitting procedure based on Particle Swarm Optimization was performed to identify the constitutive parameters. A set of numerical simulations demonstrates the effectiveness of the proposed constitutive model to predict damage-induced creep and damage-induced stress relaxation, behaviors that can be used as design criteria in absorbable implants. The main achieved results show that the proposed constitutive approach leads to a simple but effective model capable to drive the first steps in the design of absorbable biomedical devices. The present variational framework can be extended to study the constitutive response of other bioabsorbable polymers, accounting for viscous and/or plastic behaviors.

Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

“…These algorithms have proven to be particularly useful when dealing with noisy target functions, and with multiple local minima. The popular genetic algorithms and particle swarm methods stand out due to their multiple applications, including the solution of inverse problems [117,[126][127][128]. These methods require intensive sampling, so they are contraindicated for the solution of inverse problems when the forward problem is computationally expensive [15].…”