Predicting localised corrosion and mechanical performance of a PEO surface modified rare earth magnesium alloy for implant use through in-silico modelling.

In this study, the influence of a plasma electrolytic oxidation (PEO) surface treatment on a medical-grade WE43-based magnesium alloy is examined through an experimental and computational framework that considers the effects of localised corrosion features and mechanical properties throughout the corrosion process. First, a comprehensive in-vitro immersion study was performed on WE43-based tensile specimens with and without PEO surface modification, which included fully automated spatial reconstruction of the phenomenological features of corrosion through micro-CT scanning, followed by uniaxial tensile testing. Then the experimental data of both unmodified and PEO-modified groups were used to calibrate parameters of a finite element-based surface corrosion model. In-vitro, it was found that the WE43-PEO modified group had a significantly lower corrosion rate and maintained significantly higher mechanical properties than the unmodified. While corrosion rates were ∼50% lower in the WE43-PEO modified specimens, the local geometric features of corroding surfaces remained similar to the unmodified WE43 group, however evolving after almost the double amount of time. We were also able to quantitatively demonstrate that the PEO surface treatment on magnesium continued to protect samples from corrosion throughout the entire period tested, and not just in the early stages of corrosion. Using the results from the testing framework, the model parameters of the surface-based corrosion model were identified for both groups. This enabled, for the first time, in-silico prediction of the physical features of corrosion and the mechanical performance of both unmodified and PEO modified magnesium specimens. This simulation framework can enable future in-silico design and optimisation of bioabsorbable magnesium devices for load-bearing medical applications.

An enhanced phenomenological model to predict surface-based localised corrosion of magnesium alloys for medical use.

This study developed an enhanced phenomenological model for the predictions of surface-based localised corrosion of magnesium alloys for use in medical applications. The modelling framework extended previous surface-based approaches by considering the role of ß-phase components throughout the material volume to better predict spatial and temporal aspects of surface-based corrosion in magnesium alloys. This enhanced surface-based corrosion model offers many advantages as it (i) captures multi-directional pitting, (ii) captures various pit morphologies, (iii) eliminates mesh sizing effects, (iv) reduces computational cost through custom time controls (v) offers control of pit sizing and (vi) produces corrosion rates that are independent of pitting parameter values. The model was fully implemented in three dimensions within the finite element framework and shows excellent potential to enable robust predictions of the long-term performance of magnesium-based implants undergoing corrosion.

Linking the effect of localised pitting corrosion with mechanical integrity of a rare earth magnesium alloy for implant use.

This study presents a computational framework that investigates the effect of localised surface-based corrosion on the mechanical performance of a magnesium-based alloy. A finite element-based phenomenological corrosion model was used to generate a wide range of corrosion profiles, with subsequent uniaxial tensile test simulations to predict the mechanical response to failure. The python-based detection framework PitScan provides detailed quantification of the spatial phenomenological features of corrosion, including a full geometric tracking of corroding surface. Through this approach, this study is the first to quantitatively demonstrate that a surface-based non-uniform corrosion model can capture both the geometrical and mechanical features of a magnesium alloy undergoing corrosion by comparing to experimental data. Using this verified corrosion modelling approach, a wide range of corrosion scenarios was evaluated and enabled quantitative relationships to be established between the mechanical integrity and key phenomenological corrosion features. In particular, we demonstrated that the minimal cross-sectional area parameter was the strongest predictor of the remaining mechanical strength (R2 = 0.98), with this relationship being independent of the severity or spatial features of localised surface corrosion. Interestingly, our analysis demonstrated that parameters described in ASTM G46-94 showed weaker correlations to the mechanical integrity of corroding specimens, compared to parameters determined by Pitscan. This study establishes new mechanistic insight into the performance of the magnesium-based materials undergoing corrosion.

A coupled computational framework for bone fracture healing and long-term remodelling: Investigating the role of internal fixation on bone fractures.

In this study, a coupled computational modelling framework for bone fracture repair is presented that enables predictions of both healing and remodelling phases of the fracture region and is used to investigate the role of an internal fixation plate on the long-term healing performance of a fracture tibia under a range of different conditions. It was found that introduction of a titanium plate allowed the tibia to undergo successful healing at higher loading conditions and fracture gaps, compared with the non-plated versions. While these plated cases showed faster rates of repair in the healing phase, their performance was substantially different once they entered the remodelling phase, with substantial regions of stress shielding predicted. This framework is one of the few implementations of both fracture healing and remodelling phases of bone repair and includes several innovative approaches to smoothing, time-averaging and time incrementation in its implementation, thereby avoiding any unwanted abrupt changes between tissue phenotypes. This provides a better representation of tissue development in the fracture site when compared with fracture healing models alone and provides a suitable platform to investigate the long-term performance of orthopaedic fixation devices. This would enable the more effective design of permanent fixation devices and optimisation of the spatial and temporal performance of bioabsorbable implants.